Human Nature, The Good Life, Its Importance Of Rhetoric

Name: Professor: Course: Date: Human Nature, the Good Life, Its Importance to Rhetoric in Aristotle’s Rhetoric Introduction Rhetoric is an art of communication that aims at enhancing the capability of writers or speakers who endeavor to persuade, inform or inspire distinct audiences in exceptional scenarios. As a discipline of recognized teaching and a prolific civic application, rhetoric has played a fundamental role in the Western convention. Rhetoric is acknowledged best from the description of Aristotle who regards it as a compliment of both politics and logic, and terms it as the ability to make an observation in any given instance from the accessible means of influence. Unlike other Aristotle works that have been around for ages,†¦show more content†¦Therefore, in political rhetoric, the discussion is whether the suggestion is outstanding or detrimental. The trial lawyers have arguments over whatever is fair or unfair and the display rhetoric is concerned with the shame or honor. In deliberating for whatever is convenient, the political presenters may perhaps disregard whether it is unjust or not. Complainants may not refute that something has occurred or that it has a basis for damage; nevertheless they will not confess that their client is culpable of prejudice (James 211). Rhetorical suggestions may be complete evidences, indications, or possibilities. Political rhetoric incorporates the ethical and logic branch of politics. Aristotle defined the five main disciplines of political rhetoric as techniques and means, peace and war, national security, legislation, and trade. Consequently, the presenter must be conscious of the revenue source of the state and the expenditures, the strength of the armed forces of the nation and its foes, the installations and means of security, the food supply sources and requirements, as well as, the exports and imports, ascertaining the country does not upset the superior states and partners of trade, laws and constitution of the state, the developments that are internal, and in acknowledging the conventions of additional states’ history are valuable. Rhetoric can provoke emotions that may perhaps not be connected to the fundamental facts.Show MoreRelatedPlato and Aristotles Impact on Rhetoric1503 Words   |  7 Pagesrhetoricians than had a great impact on the history of rhetoric. Although they were similar in many ways, their use and definition of rhetoric were different. Plato had the more classical approach where he used rhetoric as a means of education to pass down his beliefs and practice of rhetoric to his students. He believed that it should be used to educate the masses, provoking thought, and thereby preserving that knowledge. Plato thought that rhetoric should be used to convey truth, truths already knownRead MoreAccording To Aristotle : The Three Modes Of Persuasion1483 Words   |  6 Pagesskills required to be successful in life is the ability to persuade others. The art of persua sion is a talent that is often overlooked. However, if one is unable to persuade others effectively, they will never be taken seriously in a professional environment. In his book, Rhetoric, Aristotle spends quite a bit of time on the subject of persuasion. In fact, he defines rhetoric as, â€Å" the faculty of observing in any given case the available means of persuasion (Rhetoric). According to Aristotle, persuasionRead MoreWealth of Nations1626 Words   |  7 PagesMichelle Trejo Dr. King Human Nature and the Social Order II June 6, 2008 â€Å"The Wealth of Nations† Adam Smith, the author of â€Å"The Wealth of Nations†, was a Scottish moral philosopher during the Industrial Revolution who was inspired by his surroundings to write about the field of economics. Being a man of intellect on various types of philosophical views, Smith was able to portray his passionate feelings about political thought through his well-written works. While publishing his book, Smith becameRead MoreAnalysis Of Gorgias Encomium Of Helen, Isocrates, And Plato s Gorgias1316 Words   |  6 PagesOne of the main differences between humans and animals is our stream of conscience. Our stream of conscience contributes to our ability to speak and form language in a powerful way, which overall contributes to the ability to function successfully within a society. Many philosophers built on the philosophies of the political atmosphere, language, and the shift from literacy (recited knowledge) to oratory (agency, ability to formulate personal thoughts and opinions). Through the analysis of variousRead MoreAnalysis Of Encomium Of Helen, Dissoi Logoi, And Plato s Gorgias1541 Words   |  7 PagesIntroduction One of the main differences between humans and animals is our stream of conscience. Our stream of conscience contributes to our ability to speak and form language in a powerful way, which overall contributes to the ability to function successfully within a society. Many philosophers built on the philosophies of the political atmosphere, language, and the shift from literacy (recited knowledge) to oratory (agency, ability to formulate personal thoughts and opinions). Through the analysisRead MoreGod Is A Problem Of Failure1367 Words   |  6 PagesAs humans, we are so focused on sin. It is innate in us, and it overpowers us. We are so caught in our failure and wrong doings that we forget what we are doing right, often avoiding a relationship with the one who created us and made us who we are today. Everyone wonders why we die, but the simple answer can be found in the bible-sin. You may think sin leads to failure, and failure leads to death, but that is not the case. It may seem like there is no escape to avoid death, but there is. God hasRead MoreRhetoric In Boy I n The Striped Pajamas1246 Words   |  5 Pagesespecially when it comes to the aspect of race. In The Boy in the Striped Pajamas, the power of rhetoric is shown in the culture between the German’s and the Jew’s lives and the importance in the little boy’s love toward the other side of the fence. The message in The Boy in the Striped Pajamas indicates the significance of life. The German and Jewish cultures shouldn’t define the importance of an individual’s life. However, during the German War it was exactly what was happening. The German’s viewed theRead MoreHow Does Rhetoric Affect Our Life?1400 Words   |  6 PagesI have learned that rhetoric is something I use regularly in my daily life. Unknowingly, I have been using this art of persuasion for even the most everyday things. Now that I can identify rhetoric, I see it everywhere in the form of politics, media, advertising, parental rearing, public speaking, personal, and even at our work place. I use rhetoric every day in my work life, convincing my residents to take physical rehab, because by them taking the service that is how the facility makes most ofRead MoreSocial Media s Influence On Our Lives1747 Words   |  7 PagesSocial media has had a tremendous impact on our lives, influencing the way we communicate, interact, and even think. In the 21st century, social media has emerged as a tool utilized in all aspects of life, ranging from entertainment to politics. In the context of politics, the lack of gatekeepers in social media has provided an even playing field for candidates to communicate with the public, and due to the effects this medium had on communication, public discourse has been influenced to fit theRead MoreMod B: Critical Study Essay- speeches (Lessing + Atwood)1035 Words   |  5 Pagesdraws attention to gender inequality by examining the unfair representation of women in literature. The worth of Lessing’s speech lies in her ability to evoke a response to world poverty, from her audience, through her emotionally gripping use of rhetoric. The euphemistic allusion to the Nobel prizes in â€Å"I don’t think many of the pupils of this school will get prizes† is especially confronting for her immediate audience, the Nobel Prize Committee, as it brings immediacy to the fact that, it is near

Academic Background Essay Samples - Is it a Scam?

Academic Background Essay Samples - Is it a Scam? Both law and company schools also often need a number of essays of their applicants, with questions that range from details about your private background to questions asking you to compose an essay exploring a controversial matter. To acquire accepted into one of the best schools is an important matter. Most colleges embrace diverseness and try to accept individuals of all races. Students have to bear in mind 3 important differences. Academic Background Essay Samples Explained An academic letter isn't only a document that can showcase your mastery when it regards a distinct academic subject. To put it simply, an academic essay may be an evidence of the depth of your research procedures and the rest of the activities which you've executed so you can support the content of your written output. Writing an introduction to an essay can therefore appear an intimidating task, although it need not be quite as difficult, so long as yo u comprehend the purpose and the structure of the introduction. Education is among the nearly all of import activities that we need to travel through in our life. When you're writing an essay, providing background information is quite important for several reasons. Whether this information appears insufficient to conduct an ideal study, don't hesitate to contact online paper writers and receive a ready solution! Here are a few suggestions for techniques to use this resource effectively. Therefore, you shouldn't use any example that you run into on the world wide web. It could refer to any sort of paper. Looking at IELTS essay topics with answers is a superb means that will help you to get ready for the test. The thesis states the particular subject, and frequently lists the main (controlling) ideas which are discussed in the home body. The function of the introduction is to present your reader a very clear idea about what your essay will cover. The motive of your essay is extremely important to be deemed as it can identify whether you are able to be of help to the men and women who want a specific educational reference. Always bear in mind your academic essay needs to be playful it must not bore your audience. A self-introduction essay is, in most instances, written utilizing the first-person viewpoint. Using Academic Background Essay Samples This paragraph ought to supply the crucial contextual or background information regarding the topic when presenting a thesis statement. Writing such a paragraph can appear intimidating but when you get a fantastic instance, the procedure gets easy. Standard introduction paragraphs have a unique function. Be precise with the aim of your writing. Along with showing that you're ready for the program, explain what you expect to become out of it. Bear in mind that all scholarship applications are different, and that means you may need to design your essay to fulfill those particular requirements. This essay examines the explanations for why assignment essays are beneficial for student learning and considers a number of the troubles with this technique of assessment. Since academic essays are popular in the discipline of education and research, you must make sure your writing is both logical, interesting and informative. Citations and extracts from several sources have to be formatted properly. Reading example essays works precisely the same way! Doing this will enable you to be more familiarized with the typical content and basic formats which are usually seen in an acad emic essay. Utilizing different examples of introductory paragraph allows you to understand how introductions of distinct essays are written. It is possible to only understand how to compose excellent introductory paragraphs for essays if you apply the greatest introductory paragraph samples. A letter of consent will likewise be sent to them together with a sample copy of the questionnaire which will be used, in addition to the protocol of the researcher. The works addressed in this essay share obvious similarities. Following are a few frequent scholarship essay questions. What You Need to Do About Academic Background Essay Samples You may use the samples as a foundation for working out how to write in the suitable style. For instance, you might begin with a chronological story of wherever your interests began, or maybe you open with your targets and after that opt for a succession of examples that show your ability to achieve them. The very best strategy is to earn a list of the points you desire to include as part of your background info. There are difference contexts that could be used within the very same subjec t so that you must make sure you will be clear in regards to identifying the section of the topic that you're going to speak about.

Open Domain Event Extraction from Twitter Free Essays

string(212) " approaches to event categorization would require \? st designing annotation guidelines \(including selecting an appropriate set of types to annotate\), then annotating a large corpus of events found in Twitter\." Open Domain Event Extraction from Twitter Alan Ritter University of Washington Computer Sci. Eng. Seattle, WA aritter@cs. We will write a custom essay sample on Open Domain Event Extraction from Twitter or any similar topic only for you Order Now washington. edu Mausam University of Washington Computer Sci. Eng. Seattle, WA mausam@cs. washington. edu Oren Etzioni University of Washington Computer Sci. Eng. Seattle, WA etzioni@cs. washington. edu Sam Clark? Decide, Inc. Seattle, WA sclark. uw@gmail. com ABSTRACT Tweets are the most up-to-date and inclusive stream of information and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitter’s unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal— the ? rst open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of signi? cant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar. com; Our NLP tools are available at http://github. com/aritter/ twitter_nlp. Entity Steve Jobs iPhone GOP Amanda Knox Event Phrase died announcement debate verdict Date 10/6/11 10/4/11 9/7/11 10/3/11 Type Death ProductLaunch PoliticalEvent Trial Table 1: Examples of events extracted by TwiCal. vents. Yet the number of tweets posted daily has recently exceeded two-hundred million, many of which are either redundant [57], or of limited interest, leading to information overload. 1 Clearly, we can bene? t from more structured representations of events that are synthesized from individual tweets. Previous work in event extraction [21, 1, 54, 18, 43, 11, 7] has focused largely on news articles, as historically this genre of text has been the best source of information on curr ent events. Read also Twitter Case Study In the meantime, social networking sites such as Facebook and Twitter have become an important complementary source of such information. While status messages contain a wealth of useful information, they are very disorganized motivating the need for automatic extraction, aggregation and categorization. Although there has been much interest in tracking trends or memes in social media [26, 29], little work has addressed the challenges arising from extracting structured representations of events from short or informal texts. Extracting useful structured representations of events from this disorganized corpus of noisy text is a challenging problem. On the other hand, individual tweets are short and self-contained and are therefore not composed of complex discourse structure as is the case for texts containing narratives. In this paper we demonstrate that open-domain event extraction from Twitter is indeed feasible, for example our highest-con? dence extracted future events are 90% accurate as demonstrated in  §8. Twitter has several characteristics which present unique challenges and opportunities for the task of open-domain event extraction. Challenges: Twitter users frequently mention mundane events in their daily lives (such as what they ate for lunch) which are only of interest to their immediate social network. In contrast, if an event is mentioned in newswire text, it 1 http://blog. twitter. com/2011/06/ 200-million-tweets-per-day. html Categories and Subject Descriptors I. 2. 7 [Natural Language Processing]: Language parsing and understanding; H. 2. [Database Management]: Database applications—data mining General Terms Algorithms, Experimentation 1. INTRODUCTION Social networking sites such as Facebook and Twitter present the most up-to-date information and buzz about current ? This work was conducted at the University of Washington Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro? t or commercial advantage and that copies bear this notice and the full citation on the ? rst page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci? c permission and/or a fee. KDD’12, August 12–16, 2012, Beijing, China. Copyright 2012 ACM 978-1-4503-1462-6 /12/08 †¦ $10. 00. is safe to assume it is of general importance. Individual tweets are also very terse, often lacking su? cient context to categorize them into topics of interest (e. g. Sports, Politics, ProductRelease etc†¦ ). Further because Twitter users can talk about whatever they choose, it is unclear in advance which set of event types are appropriate. Finally, tweets are written in an informal style causing NLP tools designed for edited texts to perform extremely poorly. Opportunities: The short and self-contained nature of tweets means they have very simple discourse and pragmatic structure, issues which still challenge state-of-the-art NLP systems. For example in newswire, complex reasoning about relations between events (e. g. before and after ) is often required to accurately relate events to temporal expressions [32, 8]. The volume of Tweets is also much larger than the volume of news articles, so redundancy of information can be exploited more easily. To address Twitter’s noisy style, we follow recent work on NLP in noisy text [46, 31, 19], annotating a corpus of Tweets with events, which is then used as training data for sequence-labeling models to identify event mentions in millions of messages. Because of the terse, sometimes mundane, but highly redundant nature of tweets, we were motivated to focus on extracting an aggregate representation of events which provides additional context for tasks such as event categorization, and also ? lters out mundane events by exploiting redundancy of information. We propose identifying important events as those whose mentions are strongly associated with references to a unique date as opposed to dates which are evenly distributed across the calendar. Twitter users discuss a wide variety of topics, making it unclear in advance what set of event types are appropriate for categorization. To address the diversity of events discussed on Twitter, we introduce a novel approach to discovering important event types and categorizing aggregate events within a new domain. Supervised or semi-supervised approaches to event categorization would require ? st designing annotation guidelines (including selecting an appropriate set of types to annotate), then annotating a large corpus of events found in Twitter. You read "Open Domain Event Extraction from Twitter" in category "Papers" This approach has several drawbacks, as it is apriori unclear what set of types should be annotated; a large amount of e? ort would be required to manually annotate a corpus of ev ents while simultaneously re? ning annotation standards. We propose an approach to open-domain event categorization based on latent variable models that uncovers an appropriate set of types which match the data. The automatically discovered types are subsequently inspected to ? lter out any which are incoherent and the rest are annotated with informative labels;2 examples of types discovered using our approach are listed in ? gure 3. The resulting set of types are then applied to categorize hundreds of millions of extracted events without the use of any manually annotated examples. By leveraging large quantities of unlabeled data, our approach results in a 14% improvement in F1 score over a supervised baseline which uses the same set of types. Stanford NER T-seg P 0. 62 0. 73 R 0. 5 0. 61 F1 0. 44 0. 67 F1 inc. 52% Table 2: By training on in-domain data, we obtain a 52% improvement in F1 score over the Stanford Named Entity Recognizer at segmenting entities in Tweets [46]. 2. SYSTEM OVERVIEW TwiCal extracts a 4-tuple representation of events which includes a named entity, event phrase, calendar date, and event type (see Table 1). This representation was chosen to closely match the way import ant events are typically mentioned in Twitter. An overview of the various components of our system for extracting events from Twitter is presented in Figure 1. Given a raw stream of tweets, our system extracts named entities in association with event phrases and unambiguous dates which are involved in signi? cant events. First the tweets are POS tagged, then named entities and event phrases are extracted, temporal expressions resolved, and the extracted events are categorized into types. Finally we measure the strength of association between each named entity and date based on the number of tweets they co-occur in, in order to determine whether an event is signi? cant. NLP tools, such as named entity segmenters and part of speech taggers which were designed to process edited texts (e. g. news articles) perform very poorly when applied to Twitter text due to its noisy and unique style. To address these issues, we utilize a named entity tagger and part of speech tagger trained on in-domain Twitter data presented in previous work [46]. We also develop an event tagger trained on in-domain annotated data as described in  §4. 3. NAMED ENTITY SEGMENTATION NLP tools, such as named entity segmenters and part of speech taggers which were designed to process edited texts (e. g. ews articles) perform very poorly when applied to Twitter text due to its noisy and unique style. For instance, capitalization is a key feature for named entity extraction within news, but this feature is highly unreliable in tweets; words are often capitalized simply for emphasis, and named entities are often left all lowercase. In addition, tweets contain a higher proportion of out -ofvocabulary words, due to Twitter’s 140 character limit and the creative spelling of its users. To address these issues, we utilize a named entity tagger trained on in-domain Twitter data presented in previous work [46]. Training on tweets vastly improves performance at segmenting Named Entities. For example, performance compared against the state-of-the-art news-trained Stanford Named Entity Recognizer [17] is presented in Table 2. Our system obtains a 52% increase in F1 score over the Stanford Tagger at segmenting named entities. 4. EXTRACTING EVENT MENTIONS This annotation and ? ltering takes minimal e? ort. One of the authors spent roughly 30 minutes inspecting and annotating the automatically discovered event types. 2 In order to extract event mentions from Twitter’s noisy text, we ? st annotate a corpus of tweets, which is then 3 Available at http://github. com/aritter/twitter_nlp. Temporal Resolution S M T W T F S Tweets POS Tag NER Signi? cance Ranking Calend ar Entries Event Tagger Event Classi? cation Figure 1: Processing pipeline for extracting events from Twitter. New components developed as part of this work are shaded in grey. used to train sequence models to extract events. While we apply an established approach to sequence-labeling tasks in noisy text [46, 31, 19], this is the ? rst work to extract eventreferring phrases in Twitter. Event phrases can consist of many di? erent parts of speech as illustrated in the following examples: †¢ Verbs: Apple to Announce iPhone 5 on October 4th?! YES! †¢ Nouns: iPhone 5 announcement coming Oct 4th †¢ Adjectives: WOOOHOO NEW IPHONE TODAY! CAN’T WAIT! These phrases provide important context, for example extracting the entity, Steve Jobs and the event phrase died in connection with October 5th, is much more informative than simply extracting Steve Jobs. In addition, event mentions are helpful in upstream tasks such as categorizing events into types, as described in  §6. In order to build a tagger for recognizing events, we annotated 1,000 tweets (19,484 tokens) with event phrases, following annotation guidelines similar to those developed for the Event tags in Timebank [43]. We treat the problem of recognizing event triggers as a sequence labeling task, using Conditional Random Fields for learning and inference [24]. Linear Chain CRFs model dependencies between the predicted labels of adjacent words, which is bene? cial for extracting multi-word event phrases. We use contextual, dictionary, and orthographic features, and also include features based on our Twitter-tuned POS tagger [46], and dictionaries of event terms gathered from WordNet by Sauri et al. [50]. The precision and recall at segmenting event phrases are reported in Table 3. Our classi? er, TwiCal-Event, obtains an F-score of 0. 64. To demonstrate the need for in-domain training data, we compare against a baseline of training our system on the Timebank corpus. precision 0. 56 0. 48 0. 24 recall 0. 74 0. 70 0. 11 F1 0. 64 0. 57 0. 15 TwiCal-Event No POS Timebank Table 3: Precision and recall at event phrase extraction. All results are reported using 4-fold cross validation over the 1,000 manually annotated tweets (about 19K tokens). We compare against a system which doesn’t make use of features generated based on our Twitter trained POS Tagger, in addition to a system trained on the Timebank corpus which uses the same set of features. as input a reference date, some text, and parts of speech (from our Twitter-trained POS tagger) and marks temporal expressions with unambiguous calendar references. Although this mostly rule-based system was designed for use on newswire text, we ? d its precision on Tweets (94% estimated over as sample of 268 extractions) is su? ciently high to be useful for our purposes. TempEx’s high precision on Tweets can be explained by the fact that some temporal expressions are relatively unambiguous. Although there appears to be room for improving the recall of temporal extraction on Twitter by handling no isy temporal expressions (for example see Ritter et. al. [46] for a list of over 50 spelling variations on the word â€Å"tomorrow†), we leave adapting temporal extraction to Twitter as potential future work. . CLASSIFICATION OF EVENT TYPES To categorize the extracted events into types we propose an approach based on latent variable models which infers an appropriate set of event types to match our data, and also classi? es events into types by leveraging large amounts of unlabeled data. Supervised or semi-supervised classi? cation of event categories is problematic for a number of reasons. First, it is a priori unclear which categories are appropriate for Twitter. Secondly, a large amount of manual e? ort is required to annotate tweets with event types. Third, the set of important categories (and entities) is likely to shift over time, or within a focused user demographic. Finally many important categories are relatively infrequent, so even a large annotated dataset may contain just a few examples of these categories, making classi? cation di? cult. For these reasons we were motivated to investigate un- 5. EXTRACTING AND RESOLVING TEMPORAL EXPRESSIONS In addition to extracting events and related named entities, we also need to extract when they occur. In general there are many di? rent ways users can refer to the same calendar date, for example â€Å"next Friday†, â€Å"August 12th†, â€Å"tomorrow† or â€Å"yesterday† could all refer to the same day, depending on when the tweet was written. To resolve temporal expressions we make use of TempEx [33], which takes Sports Party TV Politics Celebrity Music Movie Food Concert Performance Fitness Interview ProductRelease Meeting Fashion Finance School AlbumRele ase Religion 7. 45% 3. 66% 3. 04% 2. 92% 2. 38% 1. 96% 1. 92% 1. 87% 1. 53% 1. 42% 1. 11% 1. 01% 0. 95% 0. 88% 0. 87% 0. 85% 0. 85% 0. 78% 0. 71% Con? ct Prize Legal Death Sale VideoGameRelease Graduation Racing Fundraiser/Drive Exhibit Celebration Books Film Opening/Closing Wedding Holiday Medical Wrestling OTHER 0. 69% 0. 68% 0. 67% 0. 66% 0. 66% 0. 65% 0. 63% 0. 61% 0. 60% 0. 60% 0. 60% 0. 58% 0. 50% 0. 49% 0. 46% 0. 45% 0. 42% 0. 41% 53. 45% Label Sports Concert Perform TV Movie Sports Politics Figure 2: Complete list of automatically discovered event types with percentage of data covered. Interpretable types representing signi? cant events cover roughly half of the data. supervised approaches that will automatically induce event types which match the data. We adopt an approach based on latent variable models inspired by recent work on modeling selectional preferences [47, 39, 22, 52, 48], and unsupervised information extraction [4, 55, 7]. Each event indicator phrase in our data, e, is modeled as a mixture of types. For example the event phrase â€Å"cheered† might appear as part of either a PoliticalEvent, or a SportsEvent. Each type corresponds to a distribution over named entities n involved in speci? c instances of the type, in addition to a distribution over dates d on which events of the type occur. Including calendar dates in our model has the e? ct of encouraging (though not requiring) events which occur on the same date to be assigned the same type. This is helpful in guiding inference, because distinct references to the same event should also have the same type. The generative story for our data is based on LinkLDA [15], and is presented as Algorithm 1. This approach has the advantage that information about an event ph rase’s type distribution is shared across it’s mentions, while ambiguity is also naturally preserved. In addition, because the approach is based on generative a probabilistic model, it is straightforward to perform many di? rent probabilistic queries about the data. This is useful for example when categorizing aggregate events. For inference we use collapsed Gibbs Sampling [20] where each hidden variable, zi , is sampled in turn, and parameters are integrated out. Example types are displayed in Figure 3. To estimate the distribution over types for a given event, a sample of the corresponding hidden variables is taken from the Gibbs markov chain after su? cient burn in. Prediction for new data is performed using a streaming approach to inference [56]. TV Product Meeting Top 5 Event Phrases tailgate – scrimmage tailgating – homecoming – regular season concert – presale – performs – concerts – tickets matinee – musical priscilla – seeing wicked new season – season ? nale – ? nished season episodes – new episode watch love – dialogue theme – inception – hall pass – movie inning – innings pitched – homered homer presidential debate osama – presidential candidate – republican debate – debate performance network news broadcast – airing – primetime drama – channel stream unveils – unveiled – announces – launches wraps o? shows trading – hall mtg – zoning – brie? g stocks – tumbled – trading report – opened higher – tumbles maths – english test exam – revise – physics in stores – album out debut album – drops on – hits stores voted o? – idol – scotty – idol season – dividendpaying sermon – preaching preached – worship preach declared war – war shelling – opened ? re wounded senate – legislation – repeal – budget – election winners – lotto results enter – winner – contest bail plea – murder trial – sentenced – plea – convicted ? lm festival – screening starring – ? lm – gosling live forever – passed away – sad news – condolences – burried add into – 50% o? up shipping – save up donate – tornado relief disaster relief – donated – raise money Top 5 Entities espn – ncaa – tigers – eagles – varsity taylor swift – toronto britney spears – rihanna – rock shrek – les mis – lee evans – w icked – broadway jersey shore – true blood – glee – dvr – hbo net? ix – black swan – insidious – tron – scott pilgrim mlb – red sox – yankees – twins – dl obama president obama – gop – cnn america nbc – espn – abc – fox mtv apple – google – microsoft – uk – sony town hall – city hall club – commerce – white house reuters – new york – u. . – china – euro english – maths – german – bio – twitter itunes – ep – uk – amazon – cd lady gaga – american idol – america – beyonce – glee church – jesus – pastor faith – god libya – afghanistan #syria – syria – nato senate – house – congress – obama – gop ipad – award – facebook â⠂¬â€œ good luck – winners casey anthony – court – india – new delhi supreme court hollywood – nyc – la – los angeles – new york michael jackson afghanistan john lennon – young – peace groupon – early bird facebook – @etsy – etsy japan – red cross – joplin – june – africa Finance School Album TV Religion Con? ict Politics Prize Legal Movie Death Sale Drive 6. 1 Evaluation To evaluate the ability of our model to classify signi? cant events, we gathered 65 million extracted events of the form Figure 3: Example event types discovered by our model. For each type t, we list the top 5 entities which have highest probability given t, and the 5 event phrases which assign highest probability to t. Algorithm 1 Generative story for our data involving event types as hidden variables. Bayesian Inference techniques are applied to invert the generative process and infer an appropriate set of types to describe the observed events. for each event type t = 1 . . . T do n Generate ? t according to symmetric Dirichlet distribution Dir(? n ). d Generate ? t according to symmetric Dirichlet distribution Dir(? d ). end for for each unique event phrase e = 1 . . . |E| do Generate ? e according to Dirichlet distribution Dir(? ). for each entity which co-occurs with e, i = 1 . . . Ne do n Generate ze,i from Multinomial(? e ). Generate the entity ne,i from Multinomial(? n ). e,i TwiCal-Classify Supervised Baseline Precision 0. 85 0. 61 Recall 0. 55 0. 57 F1 0. 67 0. 59 Table 4: Precision and recall of event type categorization at the point of maximum F1 score. d,i end for end for 0. 6 end for for each date which co-occurs with e, i = 1 . . . Nd do d Generate ze,i from Multinomial(? e ). Generate the date de,i from Multinomial(? zn ). Precision 0. 8 1. 0 listed in Figure 1 (not including the type). We then ran Gibbs Sampling with 100 types for 1,000 iterations of burnin, keeping the hidden variable assignments found in the last sample. One of the authors manually inspected the resulting types and assigned them labels such as Sports, Politics, MusicRelease and so on, based on their distribution over entities, and the event words which assign highest probability to that type. Out of the 100 types, we found 52 to correspond to coherent event types which referred to signi? cant events;5 the other types were either incoherent, or covered types of events which are not of general interest, for example there was a cluster of phrases such as applied, call, contact, job interview, etc†¦ hich correspond to users discussing events related to searching for a job. Such event types which do not correspond to signi? cant events of general interest were simply marked as OTHER. A complete list of labels used to annotate the automatically discovered event types along wi th the coverage of each type is listed in ? gure 2. Note that this assignment of labels to types only needs to be done once and produces a labeling for an arbitrarily large number of event instances. Additionally the same set of types can easily be used to lassify new event instances using streaming inference techniques [56]. One interesting direction for future work is automatic labeling and coherence evaluation of automatically discovered event types analogous to recent work on topic models [38, 25]. In order to evaluate the ability of our model to classify aggregate events, we grouped together all (entity,date) pairs which occur 20 or more times the data, then annotated the 500 with highest association (see  §7) using the event types discovered by our model. To help demonstrate the bene? s of leveraging large quantities of unlabeled data for event classi? cation, we compare against a supervised Maximum Entropy baseline which makes use of the 500 annotated events using 10-fold c ross validation. For features, we treat the set of event phrases To scale up to larger datasets, we performed inference in parallel on 40 cores using an approximation to the Gibbs Sampling procedure analogous to that presented by Newmann et. al. [37]. 5 After labeling some types were combined resulting in 37 distinct labels. 4 0. 4 Supervised Baseline TwiCal? Classify 0. 0 0. 2 0. 4 Recall 0. 0. 8 Figure 4: types. Precision and recall predicting event that co-occur with each (entity, date) pair as a bag-of-words, and also include the associated entity. Because many event categories are infrequent, there are often few or no training examples for a category, leading to low performance. Figure 4 compares the performance of our unsupervised approach to the supervised baseline, via a precision-recall curve obtained by varying the threshold on the probability of the most likely type. In addition table 4 compares precision and recall at the point of maximum F-score. Our unsupervised approach to event categorization achieves a 14% increase in maximum F1 score over the supervised baseline. Figure 5 plots the maximum F1 score as the amount of training data used by the baseline is varied. It seems likely that with more data, performance will reach that of our approach which does not make use of any annotated events, however our approach both automatically discovers an appropriate set of event types and provides an initial classi? er with minimal e? ort, making it useful as a ? rst step in situations where annotated data is not immediately available. . RANKING EVENTS Simply using frequency to determine which events are signi? cant is insu? cient, because many tweets refer to common events in user’s daily lives. As an example, users often mention what they are eating for lunch, therefore entities such as McDonalds occur relatively frequently in association with references to most calendar days. Important events can be distinguished as those whi ch have strong association with a unique date as opposed to being spread evenly across days on the calendar. To extract signi? ant events of general interest from Twitter, we thus need some way to measure the strength of association between an entity and a date. In order to measure the association strength between an 0. 8 0. 2 Supervised Baseline TwiCal? Classify 100 200 300 400 tweets. We then added the extracted triples to the dataset used for inferring event types described in  §6, and performed 50 iterations of Gibbs sampling for predicting event types on the new data, holding the hidden variables in the original data constant. This streaming approach to inference is similar to that presented by Yao et al. 56]. We then ranked the extracted events as described in  §7, and randomly sampled 50 events from the top ranked 100, 500, and 1,000. We annotated the events with 4 separate criteria: 1. Is there a signi? cant event involving the extracted entity which will take place on t he extracted date? 2. Is the most frequently extracted event phrase informative? 3. Is the event’s type correctly classi? ed? 4. Are each of (1-3) correct? That is, does the event contain a correct entity, date, event phrase, and type? Note that if (1) is marked as incorrect for a speci? event, subsequent criteria are always marked incorrect. Max F1 0. 4 0. 6 # Training Examples Figure 5: Maximum F1 score of the supervised baseline as the amount of training data is varied. entity and a speci? c date, we utilize the G log likelihood ratio statistic. G2 has been argued to be more appropriate for text analysis tasks than ? 2 [12]. Although Fisher’s Exact test would produce more accurate p-values [34], given the amount of data with which we are working (sample size greater than 1011 ), it proves di? cult to compute Fisher’s Exact Test Statistic, which results in ? ating point over? ow even when using 64-bit operations. The G2 test works su? ciently well in our setti ng, however, as computing association between entities and dates produces less sparse contingency tables than when working with pairs of entities (or words). The G2 test is based on the likelihood ratio between a model in which the entity is conditioned on the date, and a model of independence between entities and date references. For a given entity e and date d this statistic can be computed as follows: G2 = x? {e, ¬e},y? {d, ¬d} 2 8. 2 Baseline To demonstrate the importance of natural language processing and information extraction techniques in extracting informative events, we compare against a simple baseline which does not make use of the Ritter et. al. named entity recognizer or our event recognizer; instead, it considers all 1-4 grams in each tweet as candidate calendar entries, relying on the G2 test to ? lter out phrases which have low association with each date. 8. 3 Results The results of the evaluation are displayed in table 5. The table shows the precision of the systems at di? rent yield levels (number of aggregate events). These are obtained by varying the thresholds in the G2 statistic. Note that the baseline is only comparable to the third column, i. e. , the precision of (entity, date) pairs, since the baseline is not performing event identi? cation and classi? cation. Although in some cases ngrams do correspond to informative calendar entries, the precision of the ngram baseline is extremely low compared wi th our system. In many cases the ngrams don’t correspond to salient entities related to events; they often consist of single words which are di? ult to interpret, for example â€Å"Breaking† which is part of the movie â€Å"Twilight: Breaking Dawn† released on November 18. Although the word â€Å"Breaking† has a strong association with November 18, by itself it is not very informative to present to a user. 7 Our high-con? dence calendar entries are surprisingly high quality. If we limit the data to the 100 highest ranked calendar entries over a two-week date range in the future, the precision of extracted (entity, date) pairs is quite good (90%) – an 80% increase over the ngram baseline. As expected precision drops as more calendar entries are displayed, but 7 In addition, we notice that the ngram baseline tends to produce many near-duplicate calendar entries, for example: â€Å"Twilight Breaking†, â€Å"Breaking Dawn†, and â€Å"Twilight Breaking Dawn†. While each of these entries was annotated as correct, it would be problematic to show this many entries describing the same event to a user. Ox,y ? ln Ox,y Ex,y Where Oe,d is the observed fraction of tweets containing both e and d, Oe, ¬d is the observed fraction of tweets containing e, but not d, and so on. Similarly Ee,d is the expected fraction of tweets containing both e and d assuming a model of independence. 8. EXPERIMENTS To estimate the quality of the calendar entries generated using our approach we manually evaluated a sample of the top 100, 500 and 1,000 calendar entries occurring within a 2-week future window of November 3rd. 8. 1 Data For evaluation purposes, we gathered roughly the 100 million most recent tweets on November 3rd 2011 (collected using the Twitter Streaming API6 , and tracking a broad set of temporal keywords, including â€Å"today†, â€Å"tomorrow†, names of weekdays, months, etc. ). We extracted named entities in addition to event phrases, and temporal expressions from the text of each of the 100M 6 https://dev. twitter. com/docs/streaming-api Mon Nov 7 Justin meet Other Motorola Pro+ kick Product Release Nook Color 2 launch Product Release Eid-ul-Azha celebrated Performance MW3 midnight release Other Tue Nov 8 Paris love Other iPhone holding Product Release Election Day vote Political Event Blue Slide Park listening Music Release Hedley album Music Release Wed Nov 9 EAS test Other The Feds cut o? Other Toca Rivera promoted Performance Alert System test Other Max Day give Other November 2011 Thu Nov 10 Fri Nov 11 Robert Pattinson iPhone show debut Performance Product Release James Murdoch Remembrance Day give evidence open Other Performance RTL-TVI France post play TV Event Other Gotti Live Veterans Day work closed Other Other Bambi Awards Skyrim perform arrives Performance Product Release Sat Nov 12 Sydney perform Other Pullman Ballroom promoted Other Fox ? ght Other Plaza party Party Red Carpet invited Party Sun Nov 13 Playstation answers Product Release Samsung Galaxy Tab launch Product Release Sony answers Product Release Chibi Chibi Burger other Jiexpo Kemayoran promoted TV Event Figure 6: Example future calendar entries extracted by our system for the week of November 7th. Data was collected up to November 5th. For each day, we list the top 5 events including the entity, event phrase, and event type. While there are several errors, the majority of calendar entries are informative, for example: the Muslim holiday eid-ul-azha, the release of several videogames: Modern Warfare 3 (MW3) and Skyrim, in addition to the release of the new playstation 3D display on Nov 13th, and the new iPhone 4S in Hong Kong on Nov 11th. # calendar entries 100 500 1,000 ngram baseline 0. 50 0. 6 0. 44 entity + date 0. 90 0. 66 0. 52 precision event phrase event 0. 86 0. 56 0. 42 type 0. 72 0. 54 0. 40 entity + date + event + type 0. 70 0. 42 0. 32 Table 5: Evaluation of precision at di? erent recall levels (generated by varying the threshold of the G2 statistic). We evaluate the top 100, 500 and 1,000 (entity, date) pairs. In addition we evaluate the precision of the most frequently extracted event phrase, and the predicted event type in association with these calendar entries. Also listed is the fraction of cases where all predictions (â€Å"entity + date + event + type†) are correct. We also compare against the precision of a simple ngram baseline which does not make use of our NLP tools. Note that the ngram baseline is only comparable to the entity+date precision (column 3) since it does not include event phrases or types. remains high enough to display to users (in a ranked list). In addition to being less likely to come from extraction errors, highly ranked entity/date pairs are more likely to relate to popular or important events, and are therefore of greater interest to users. In addition we present a sample of extracted future events on a calendar in ? ure 6 in order to give an example of how they might be presented to a user. We present the top 5 entities associated with each date, in addition to the most frequently extracted event phrase, and highest probability event type. 9. RELATED WORK While we are the ? rst to study open domain event extraction within Twitter, there are two key related strands of research: extracting speci? c types of events from Twi tter, and extracting open-domain events from news [43]. Recently there has been much interest in information extraction and event identi? cation within Twitter. Benson et al. 5] use distant supervision to train a relation extractor which identi? es artists and venues mentioned within tweets of users who list their location as New York City. Sakaki et al. [49] train a classi? er to recognize tweets reporting earthquakes in Japan; they demonstrate their system is capable of recognizing almost all earthquakes reported by the Japan Meteorological Agency. Additionally there is recent work on detecting events or tracking topics [29] in Twitter which does not extract structured representations, but has the advantage that it is not limited to a narrow domain. Petrovi? t al. investigate a streaming approach to identic fying Tweets which are the ? rst to report a breaking news story using Locally Sensitive Hash Functions [40]. Becker et al. [3], Popescu et al. [42, 41] and Lin et al. [28] inv estigate discovering clusters of related words or tweets which correspond to events in progress. In contrast to previous work on Twitter event identi? cation, our approach is independent of event type or domain and is thus more widely applicable. Additionally, our work focuses on extracting a calendar of events (including those occurring in the future), extract- . 4 Error Analysis We found 2 main causes for why entity/date pairs were uninformative for display on a calendar, which occur in roughly equal proportion: Segmentation Errors Some extracted â€Å"entities† or ngrams don’t correspond to named entities or are generally uninformative because they are mis-segmented. Examples include â€Å"RSVP†, â€Å"Breaking† and â€Å"Yikes†. Weak Association between Entity and Date In some cases, entities are properly segmented, but are uninformative because they are not strongly associated with a speci? c event on the associated date, or are involved in ma ny di? rent events which happen to occur on that day. Examples include locations such as â€Å"New York†, and frequently mentioned entities, such as â€Å"Twitter†. ing event-referring expressions and categorizing events into types. Also relevant is work on identifying events [23, 10, 6], and extracting timelines [30] from news articles. 8 Twitter status messages present both unique challenges and opportunities when compared with news articles. Twitter’s noisy text presents serious challenges for NLP tools. On the other hand, it contains a higher proportion of references to present and future dates. Tweets do not require complex reasoning about relations between events in order to place them on a timeline as is typically necessary in long texts containing narratives [51]. Additionally, unlike News, Tweets often discus mundane events which are not of general interest, so it is crucial to exploit redundancy of information to assess whether an event is signi? cant. Previous work on open-domain information extraction [2, 53, 16] has mostly focused on extracting relations (as opposed to events) from web corpora and has also extracted relations based on verbs. In contrast, this work extracts events, using tools adapted to Twitter’s noisy text, and extracts event phrases which are often adjectives or nouns, for example: Super Bowl Party on Feb 5th. Finally we note that there has recently been increasing interest in applying NLP techniques to short informal messages such as those found on Twitter. For example, recent work has explored Part of Speech tagging [19], geographical variation in language found on Twitter [13, 14], modeling informal conversations [44, 45, 9], and also applying NLP techniques to help crisis workers with the ? ood of information following natural disasters [35, 27, 36]. 1. ACKNOWLEDGEMENTS The authors would like to thank Luke Zettlemoyer and the anonymous reviewers for helpful feedback on a previous draft. This research was supported in part by NSF grant IIS-0803481 and ONR grant N00014-08-1-0431 and carried out at the University of Washington’s Turing Center. 12. REFERENCES [1] J. Allan, R. Papka, and V . Lavrenko. On-line new event detection and tracking. In SIGIR, 1998. [2] M. Banko, M. J. Cafarella, S. Soderl, M. Broadhead, and O. Etzioni. Open information extraction from the web. In In IJCAI, 2007. [3] H. Becker, M. Naaman, and L. Gravano. Beyond trending topics: Real-world event identi? ation on twitter. In ICWSM, 2011. [4] C. Bejan, M. Titsworth, A. Hickl, and S. Harabagiu. Nonparametric bayesian models for unsupervised event coreference resolution. In NIPS. 2009. [5] E. Benson, A. Haghighi, and R. Barzilay. Event discovery in social media feeds. In ACL, 2011. [6] S. Bethard and J. H. Martin. Identi? cation of event mentions and their semantic class. In EMNLP, 2006. [7] N. Chambers and D. Jurafsky. Template-based information extraction without the templates. In Proceedings of ACL, 2011. [8] N. Chambers, S. Wang, and D. Jurafsky. Classifying temporal relations between events. In ACL, 2007. 9] C. Danescu-Niculescu-Mizil, M. Gamon, and S. Dumais. Mark my words! Linguistic style accommodation in social media. In Proceedings of WWW, pages 745–754, 2011. [10] A. Das Sarma, A. Jain, and C. Yu. Dynamic relationship and event discovery. In WSDM, 2011. [11] G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel. The Automatic Content Extraction (ACE) Program–Tasks, Data, and Evaluation. LREC, 2004. [12] T. Dunning. Accurate methods for the statistics of surprise and coincidence. Comput. Linguist. , 1993. [13] J. Eisenstein, B. O’Connor, N. A. Smith, and E. P. Xing. A latent variable model for geographic lexical variation. In EMNLP, 2010. [14] J. Eisenstein, N. A. Smith, and E. P. Xing. Discovering sociolinguistic associations with structured sparsity. In ACL-HLT, 2011. [15] E. Erosheva, S. Fienberg, and J. La? erty. Mixed-membership models of scienti? c publications. PNAS, 2004. [16] A. Fader, S. Soderland, and O. Etzioni. Identifying relations for open information extraction. In EMNLP, 2011. [17] J. R. Finkel, T. Grenager, and C. Manning. Incorporating non-local information into information extraction systems by gibbs sampling. In ACL, 2005. [18] E. Gabrilovich, S. Dumais, and E. Horvitz. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In WWW, 2004. [19] K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith. Part-of-speech tagging 10. CONCLUSIONS We have presented a scalable and open-domain approach to extracting and categorizing events from status messages. We evaluated the quality of these events in a manual evaluation showing a clear improvement in performance over an ngram baseline We proposed a novel approach to categorizing events in an open-domain text genre with unknown types. Our approach based on latent variable models ? rst discovers event types which match the data, which are then used to classify aggregate events without any annotated examples. Because this approach is able to leverage large quantities of unlabeled data, it outperforms a supervised baseline by 14%. A possible avenue for future work is extraction of even richer event representations, while maintaining domain independence. For example: grouping together related entities, classifying entities in relation to their roles in the event, thereby, extracting a frame-based representation of events. A continuously updating demonstration of our system can be viewed at http://statuscalendar. com; Our NLP tools are available at http://github. com/aritter/twitter_nlp. 8 http://newstimeline. googlelabs. com/ [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] for twitter: Annotation, features, and experiments. In ACL, 2011. T. L. Gri? ths and M. Steyvers. Finding scienti? c topics. Proc Natl Acad Sci U S A, 101 Suppl 1, 2004. R. Grishman and B. Sundheim. Message understanding conference – 6: A brief history. In Proceedings of the International Conference on Computational Linguistics, 1996. Z. Kozareva and E. Hovy. Learning arguments and supertypes of semantic relations using recursive patterns. In ACL, 2010. G. Kumaran and J. Allan. Text classi? cation and named entities for new event detection. In SIGIR, 2004. J. D. La? erty, A. McCallum, and F. C. N. Pereira. Conditional random ? elds: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001. J. H. Lau, K. Grieser, D. Newman, and T. Baldwin. Automatic labelling of topic models. In ACL, 2011. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD, 2009. W. Lewis, R. Munro, and S. Vogel. Crisis mt: Developing a cookbook for mt in crisis situations. In Proceedings of the Sixth Workshop on Statistical Machine Translation, 2011. C. X. Lin, B. Zhao, Q. Mei, and J. Han. PET: a statistical model for popular events tracking in social communities. In KDD, 2010. J. Lin, R. Snow, and W. Morgan. Smoothing techniques for adaptive online language models: Topic tracking in tweet streams. In KDD, 2011. X. Ling and D. S. Weld. Temporal information extraction. In AAAI, 2010. X. Liu, S. Zhang, F. Wei, and M. Zhou. Recognizing named entities in tweets. In ACL, 2011. I. Mani, M. Verhagen, B. Wellner, C. M. Lee, and J. Pustejovsky. Machine learning of temporal relations. In ACL, 2006. I. Mani and G. Wilson. Robust temporal processing of news. In ACL, 2000. R. C. Moore. On log-likelihood-ratios and the signi? cance of rare events. In EMNLP, 2004. R. Munro. Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol. In CoNLL, 2011. G. Neubig, Y. Matsubayashi, M. Hagiwara, and K. Murakami. Safety information mining – what can NLP do in a disaster -. In IJCNLP, 2011. D. Newman, A. U. Asuncion, P. Smyth, and M. Welling. Distributed inference for latent dirichlet allocation. In NIPS, 2007. D. Newman, J. H. Lau, K. Grieser, and T. Baldwin. Automatic evaluation of topic coherence. In HLT-NAACL, 2010. ? e D. O S? aghdha. Latent variable models of selectional preference. In ACL, ACL ’10, 2010. S. Petrovi? , M. Osborne, and V. Lavrenko. Streaming c ? rst story detection with application to twitter. In HLT-NAACL, 2010. [41] A. -M. Popescu and M. Pennacchiotti. Dancing with the stars, nba games, politics: An exploration of twitter users’ response to events. In ICWSM, 2011. [42] A. -M. Popescu, M. Pennacchiotti, and D. A. Paranjpe. Extracting events and event descriptions from twitter. In WWW, 2011. [43] J. Pustejovsky, P. Hanks, R. Sauri, A. See, R. Gaizauskas, A. Setzer, D. Radev, B. Sundheim, D. Day, L. Ferro, and M. Lazo. The TIMEBANK corpus. In Proceedings of Corpus Linguistics 2003, 2003. [44] A. Ritter, C. Cherry, and B. Dolan. Unsupervised modeling of twitter conversations. In HLT-NAACL, 2010. [45] A. Ritter, C. Cherry, and W. B. Dolan. Data-driven response generation in social media. In EMNLP, 2011. [46] A. Ritter, S. Clark, Mausam, and O. Etzioni. Named entity recognition in tweets: An experimental study. EMNLP, 2011. [47] A. Ritter, Mausam, and O. Etzioni. A latent dirichlet allocation method for selectional preferences. In ACL, 2010. [48] K. Roberts and S. M. Harabagiu. Unsupervised learning of selectional restrictions and detection of argument coercions. In EMNLP, 2011. [49] T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In WWW, 2010. [50] R. Saur? R. Knippen, M. Verhagen, and ? , J. Pustejovsky. Evita: a robust event recognizer for qa systems. In HLT-EMNLP, 2005. [51] F. Song and R. Cohen. Tense interpretation in the context of narrative. In Proceedings of the ninth National conference on Arti? cial intelligence – Volume 1, AAAI’91, 1991. [52] B. Van Durme and D. Gildea. Topic models for corpus-centric knowledge generalization. In Technical Report TR-946, Department of Computer Science, University of Rochester, Rochester, 2009. [53] D. S. Weld, R. Ho? mann, and F. Wu. Using wikipedia to bootstrap open information extraction. SIGMOD Rec. , 2009. 54] Y. Yang, T. Pierce, and J. Carbonell. A study of retrospective and on-line event detection. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’98, 1998. [55] L. Yao, A. Haghighi, S. Riedel, and A. McCallum. Structured relation discovery using generative models. In EMNLP, 2011. [56] L. Ya o, D. Mimno, and A. McCallum. E? cient methods for topic model inference on streaming document collections. In KDD, 2009. [57] F. M. Zanzotto, M. Pennaccchiotti, and K. Tsioutsiouliklis. Linguistic redundancy in twitter. In EMNLP, 2011. How to cite Open Domain Event Extraction from Twitter, Papers

The improvement in technology

Introduction The past decade has seen business trends receive a great upgrade due to the influx of technology. The improvement in technology has greatly affected the methods means and manner in which businesses choose to conduct their activities. Technology has been the greatest determinant of business growth for a variety of reasons.Advertising We will write a custom essay sample on The improvement in technology specifically for you for only $16.05 $11/page Learn More The better technology the industry has, the greater the computing power and in effect the faster, persuasive and competitive the product becomes. Technology facilitates an effective harness of talent, resources and ideas from the organisations structure (Boorsma Wolfgang 2007, p9). Today the influx of virtual business enabled by various technological business modules and vehicles has created a new approach to decision-making as well as business-to-business marketing. It would be difficult to ignore the prevalence of business modules such as Facebook which have flourished through social networking to secure a subscriber network of over 400 million people across the globe. Driven by a cost cutting objective technology continues to present new deployment methods which are aimed at reducing the cost of acquisition, maintenance as well upgrade of the technology adopted. This has made them a more attractive option as compared to alternative models and approaches to doing business. Cloud computing for example has opened a window of opportunities for majority if not all business players by offering new approaches to the concept of demand and supply. The consumer on his part is provided a variety of ways to derive utility from products, the entrepreneur also get an opportunity to expand their expansion ambitions to new markets breaching the geographical and structural limitations (Boorsma Wolfgang 2007, p9). Companies and businesses therefore have to make important decision s on how much investment to make in technology and in the transformation of the business models to harness new opportunities presented by new technology Markets operating a business-to-business model are characterised by a rather long and complicated buying process that is complicated further by the high costs of operation involved. It therefore follows that the model demands a fare share of objective and purposeful personalised communication. Traditionally the communication models used have been the face-to-face approach due to their convenience speed and immediate response ability.Advertising Looking for essay on other technology? Let's see if we can help you! Get your first paper with 15% OFF Learn More They have also been commonly used due to their flexibility in delivery of the message. The parties can customize the message to accommodate a change in circumstance as well as new circumstances. This is greatly attributed to the oligopolistic nature of these markets presenting a highly imbalanced seller to buyer ration. These aspects have been responsible for the general trend and direction of the business-to-business models. As De Pelsmacker et al (2004, p59) puts it the general trend has been to focus on personal selling alongside trade shows as the central marketing elements. Others suggest alternatives methods such as advertisements in business journals as the most appropriate methods of informing the consumer of the availability of a specific brand for their choosing and purchase. Despite the effectiveness of these methods however the emergence of new business marketing methods and options such as direct mail, online market strategies as well as database management have created a great indifference for managers and executives. They have to make investment decisions between expensive technologies that have a promise of high results and run the risk of obsolesce of the technology as opposed to sticking to the traditional methods of marketing and consumer outreach approaches. I will examine how the emerging technology and communication has affected the business-to-business buying process and decision-making. Argument A long-standing measure of a successful entrepreneur is their ability to organise groups of market participants to create a market. The influx of technology has created a reliable and viable method of doing just that through the internet in the context of internet marketing. The digital revolution has infected the marketing process with a wave of transformation that has progressively increased over the past few years. The digital influence on the various market and market players has fuelled an enthusiasm that is directed at the various digital options and choices in business models. This revolution has also been of great help to entrepreneurs and sellers who get an opportunity to centralise their efforts to embrace the consumer based business models. This therefore increases their level of consumer service by blending various digital options and elements. Digital marketing is however very different from internet marketing and is often but erroneously mistaken to mean the latter. Generally, internet marketing is a typical example of digital marketing since the tools of digital marketing include the internet alongside other related tools such as television channels, cell phones as well as wireless networks and connections.Advertising We will write a custom essay sample on The improvement in technology specifically for you for only $16.05 $11/page Learn More These tools if well employed have a great capacity to influence the buyer’s decision on whether to buy or not to buy or not. The buyer will be more convinced by a good presentation format that is technologically compliant with the recent trends that relate with them and express a futuristic impression. This however requires great monetary investment as well as strict and proper management to be a ble to achieve results. The technology for example must be compatible with the organisations goals objectives and strategy to avoid conflict and retrogressive or irrelevant technologies. As such, innovation in technology keeps presenting new opportunities and methods of engagement in the buying process and decision-making. This however comes at its own cost. From open source software developers to social network streamers such as Facebook and Twitter the market has switched digital. The open source websites boast of over 68 million bloggers who participate in product evaluation and in the distribution of product information. Customer relations have therefore been greatly enhanced through digital innovation. The players in the business-to-business market have an opportunity to instantly respond to each other’s questions fears and suggestions. A company therefore, lowers the cost of serving its customers by investing in an operational and suitable Web based customer service sol ution. This option allows the company to monitor its performance through the number of complains or complementary comments it receives through the customer service tool. Innovation in technology has gone an extra mile by offering a word of mouth Web based marketing option that allows buyers to share their experience with the specific product for others to see and hear. Sellers on the other hand get to explain verbatim the additional facilities offered by their product over and above their competitors (Immelt, Govindarajan Trimble 2009, p57). In the near future therefore the buy or sell decision will greatly rely on how good the technology conveys the information between the participants of such a market. Technology therefore has created an opportunity to tap into communities and create value from the formed groupings. This therefore implies that companies must comprehensively research before engaging with a potential service provider in relation to technology.Advertising Looking for essay on other technology? Let's see if we can help you! Get your first paper with 15% OFF Learn More Successful communication is a two way process with feed forward and feedback. Technology has also facilitated communication between the buyers and sellers by allowing the parties to supply feedback on the various concerns raised by the participants. This maintains a continuing participation and stimulates the level of commitment by the parties to the buying and selling decisions. As previously discussed, technology has allowed organisations to breach their limits in manpower, resource and geography through technological implements. Research suggests that this attribute of technological influence taps into a world of talent allowing companies to sustain flexibility and create volatility in business relations. Technology has rendered the market more porous allowing companies to work above the constraints of corporate infrastructure. In the past, this has been seen to work very well especially during the economic recession that left most companies with few workable option thereby causi ng companies to push for sustainable networks (Gawer 2010). Typically, the quality of talent an organisation can access in resolving technical client problems would be constrained by the company’s resources being structural and economic. An engineering company for instance,is only as good as its best engineer and therefore it can only be as good as the best salary it can offer to its engineers as the best ones come at a price. Technology has however made it easier for a manager to map knowledge sources with information hubs and a worldwide staff. This facilitates better utilisation of talent and increases the quality of unit innovation among its operation units. The various projects a manager undertakes are therefore authorised and assessed by the best of the best among experts in the specific specialty through the global network. This approach draws input from all calibres of employees ranging from fresh graduates to retirees. A good example of these labour markets is the Me chanical Turk courtesy of Amazon.com which specializes in selling expertise and consultancy as well as problem solving (Prahalad 2009). Despite this advantage and growth potential, management conservatism and bureaucracy still confines most companies to the talent and quality of its full time employees whose limits go only as far as the organisations’ structure. Technological advancement and innovation continue to offer new options every other day. In the near future, these options will be too many and the big question will be one of collaboration. It is important to ensure that any such engaged resource is exploited to its full potential. Essentially different innovations have different potential and capacities. The efficiency however depends on the collaboration of resources in the organisation. The collaboration leads to economies of scale and capacity. Teleconferencing and video conferencing for example has worked as a cost effective tool that saves on time and travel cos ts for the selling managers and business consultants. It also allows for more flexibility in the organisations capacity. The buyer’s decision to buy is therefore greatly influenced by convincing the sales executive in the video conference session. The buying process therefore still maintains an aspect of the interpersonal contact and dimension. In any buying process, the participants will always be concerned about history, authenticity and a promise of future consistency in service delivery. The transactions need to be authenticated to create assurance and confidence. The traditional approach would be for the participants to test, see or try the commodity before buying. Technology has facilitated automation of this process through the adoption of the radiofrequency identification and similar technologies. These create an information system that has assets in the form of elements of the system. One good such example is in the insurance industry where a company can keep account of the driver’s behaviour for the purpose of evaluation of their risk profile and for the purpose of payment of compensation should the risk materialize (Barabasi 2009). Technology has increased the accessories of the buying decision by allowing parties to offer guarantees of safety and an assurance of quality. More advanced innovation has enabled proactive action in luxury automobiles to engage intelligent action just before an accident occurs. In the medical industry the innovation has created an opportunity for cheaper more effective medical surveillance and protective mechanism against diseases and preventable illnesses. The process involves body implants that keep a record of body changes for the purpose of medical adjustments and medical prescription observation and supervision. The information collected allows for a more proper diagnosis of body problems. This not only guarantees the authenticity of products in the buying process but also guarantees safety. A good buy decision relies on the level of information relied on by the decision maker. This information would ordinarily be available only if gathered manually from the field or through trial and experimentation. These however are timely and expensive engagements that need not be undertaken thanks to technological innovation. Commonly referred to as the â€Å"big data,† the information system alternative offers access to smart assets for the buyer to choose from coupled with product information to facilitate their evaluation and information to ensure that the buyer’s expectations are adequately met. This allows the buyer to evaluate different product combinations at a lower cost as opposed to physical examination and testing or sampling. Technology has also allowed specialists, analysts and marketers to conduct purpose based trials and experiments on product combinations depending on customer expectations. The customers’ expectations are gathered from the social media we bsites and product review search engines. The experiment involves putting product combination for the discussion review and evaluation by the consumers (Thomke 2001, p66). Their responses through blogs and comments on these websites create a rating mechanism for these product combinations. These have also been used to adjust prices on a periodic basis to conform to the prevailing circumstances and the data provided by real-time data monitors on social media. From a corporate responsibility perspective, the buying process in certain circumstances caused environmental stress. This is partially due to the depletion of the existing resources and partially due to the waste generated by the process. Technology has facilitated a change in the level of responsibility of the participants of the business market by offering environmental friendly alternatives that go towards conservation and preservation of resources. The green data movement for instance, creates an opportunity to conserve ene rgy by developing environmental friendly implements that have automated energy saving mechanisms. Undeniably, the responsibility to preserve the environment falls on all and every stakeholder. Technology has therefore facilitated the principles of sustainability in the buying process by facilitating cost sharing and harmonised action (McAfee 2009). The mitigation mechanisms offered by technology also provide a quantity analysis. This information can be used in the monitoring supervision and reporting of the benefits as weighed against the damage contributed by information technology. Every company looks to reduce its fixed costs which account for the least possible price they can quote for the consumer. Business to business customers specifically invest in cost cutting alternatives and are more willing to maintain a cost as variable and terminable as opposed to a determinate fixed cost. Transport for instance, can be fixed or variable depending on the approach adopted. If a consumer acquires a bus they write it off as a fixed cost distributed evenly over the useful life of the product. In the alternative, technology has allowed for a re- evaluation of this product into a service where the consumer can acquire the purpose of the product as opposed to the physical product its self. The input of technology has allowed companies such as City Carshare to create a value added market for transportation services as an alternative to the purchase of transport equipment. The transportation service is easier to a count for and is more reliable and takes a corporate value approach. The cost then changes to a variable cost, which is adjusted on a periodic basis. It is also a more economical approach since the service is only paid for when it is rendered and it is paid for in the same measure of utility. This has changed the business-to-business concept through outsourcing which draws from the indefinite global resource. Transactions and business decisions gain value throug h interaction and exchange of information and communication. The traditional business model relies on the face-to-face interaction communication and information exchange. Technology has however transformed the business-to-business model to a multisided business model from a two-side model by allowing a three-way transaction. The advertising aspect in a newspaper allows newspapers to generate their revenue while still offering the users content. This creates a reliable market of defined sellers and many consumers in which case the consumers are segmented based on the side of the transaction and the benefit they expect to derive (Carr 2009). Relevance and suitability of a product are serious considerations in the buying decision. Therefore, the appropriateness of a service or product to a specific consumer environment and circumstance goes a long way in persuading the consumer to acquire or purchase the product. Technology has allowed the business-to-business communication process to adjust to the specific situations and circumstances through different user interfaces that adjust in language circumstance and conditions. The financial sector business to business model has greatly advanced in rural Africa through retail banking under the M-Pesa module that offers a connection between bank accounts and cell phones allowing up to 8 million to access banking services. The use of virtual cash services allows the users to access funds even in remote areas by visiting licensed shops. It is also a multisided method that allows companies to transfer funds to each other and to their employees and from employees to the companies and institutions such as banks (Bryan Joyce 2007). Conclusion The future of technology in business is bright as new methods of operation and interaction continue to emerge. The impact of technology on business transactions and decisions will also continue to gradually increase creating a dependent relationship in regard to decision making choice an d preference (Brynjolfsson Saunders 2009). Technology creates a capacity and opportunity for competitive advantage. The message is clear, organisations should acknowledge the role if innovation and technology in the business process as a strategy towards growth and competitive advantage (Malone 2004). References Barabasi A 2009, How Everything is Connected to Everything Else and What It Means for Business, Science, and Everyday Life, Plume, New York. Boorsma, B Wolfgang W 2007, ‘Connected urban development, Innovation for sustainability’, NATOA Journal, Volume 15, Number 4, pp.5–9. Bryan, L, Joyce C, 2007, Mobilizing Minds, Creating Wealth from Talent in the 21st-Century Organization, McGraw-Hill, New York. Brynjolfsson, E., Saunders, A 2009, Wired for Innovation, How Information Technology is Reshaping the Economy, The MIT Press, Cambridge. Carr, N 2009, The Big Switch, Rewiring the World, from Edison to Google, Norton Company, New York. De Pelsmacker, P., Geuens, M. Van den Bergh, J 2004, Marketing communications: a European perspective. Pearson Education. Essex. Gawer A 2010, Platforms, Markets and Innovation, Edward Elgar Publishing, Cheltenham. Immelt, R., Govindarajan, V Trimble, C 2009, ‘How GE is disrupting itself’, Harvard Business Review, Volume 87, Number 10, pp. 56–65. Malone, T 2004, The Future of Work, How the New Order of Business Will Shape Your Organization, Your Management Style, and Your Life, MA, Harvard Business Press, Cambridge. McAfee, A 2009, Enterprise 2.0, New Collaborative Tools for Your Organization’s Toughest Challenges, Harvard Business School Press, Cambridge. Prahalad, C 2009, The Fortune at the Bottom of the Pyramid, Eradicating Poverty Through Profits, Wharton School Publishing, Philadelphia. Thomke, S 2001, ‘Enlightened experimentation, The new imperative for innovation’, Harvard Business Review, Volume 79, Number 2, pp. 66–75. This essay on The improvement in technology was written and submitted by user Abb1ga1l to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.

Student Role in Politics Essays

Student Role in Politics Essays Student Role in Politics Essay Student Role in Politics Essay Subject: Research By: Making a Difference, Not a Statement: College Students and Politics, Volunteering, and an Agenda for America Peter D. Hart Research Associates 1724 Connecticut Avenue, NW Washington, DC 20009 April 2001 Date: From February 24 to March 2, Hart Research surveyed a national representative sample of 809 students in four-year colleges and universities; this research, conducted on behalf of the Panetta Institute, gauges students’ views of and involvement in civics and politics. This report summarizes our key findings. The margin of error is  ± 3. 5% for the overall sample and higher for specific subgroups. Forty years ago, something began to stir on the nation’s campuses. In March 1961, President John F. Kennedy, sensing the potential idealism of the nation’s youth, signed an executive order creating the Peace Corps, and a few months later, the first cohort of Peace Corps volunteers embarked for Africa. That same year, college students traveled south to join the Freedom Rides, risking life and limb for the civil rights cause. It was the beginning of a youth movement that ultimately changed the face of America, as it touched everything from race relations to women’s rights to war and peace. Four decades later, could students once again provide the energy and idealism that drive social and political change? The results of our national survey among college students suggest that the potential is indeed there. Indeed, the civil rights and women’s movements are now a source of inspiration. And if this potential is realized, this generation is clearly poised to move the country in a progressive direction. In their issue preferences and political leanings, the youth of Generation Y embrace a progressive agenda while rejecting the anti-government cynicism of their Generation X forerunners. Yet, only a fraction of this great potential has been realized so far. Unlike their predecessors four decades ago, today’s college students enjoy the legal right to vote, but only a small minority of Americans age 18 to 21 exercised that right in 2000. These young people care about the issues of the day, yet few believe that working on a political campaign or contacting their congressional representative, for example, can help make society better. They say they want to contribute to their society and make a difference, but most spurn government service as a career option. Their values and priorities seem disconnected from their level of political engagement. Certainly, neither presidential candidate managed to connect with this generation. Today’s students are simultaneously progressive and apolitical; they embrace many government solutions, but evince little interest in government itself. Nevertheless, the survey results indicate that it is possible to get college students involved in the nation’s political life. Indeed, today’s generation of students is like tinder awaiting a spark. New political leadership, making the right kind of appeal and challenging young people to get involved as President Kennedy did in 1961, could once again awaken a powerful response on the nation’s campuses. I. College Students’ Current Outlook 1. Today’s college students are progressive in their views. College students’ agenda for the nation is strongly progressive. Among all the policy priorities tested in the survey, the top three are improving schools by hiring teachers and reducing class size (85% very top or high priority), strengthening and preserving Social Security (76%), and providing assistance to low-income families (73%). The three lowest priorities are strengthening the military (34%), reducing the size of government (23%), and allowing oil exploration in the Alaskan Arctic Wildlife Refuge (21%). Looking back at our history, today’s college students identify with progressive social movements. Overwhelming majorities feel that the civil rights (89%) and women’s rights (78%) movements did a great deal or quite a bit to make American society better. Smaller majorities say the same about the environmental movement (58%), human rights organizations (56%), and the Democratic Party (57%). In contrast, far fewer believe that the Republican Party (45%), the war on drugs (40%), the campaign for teen abstinence (29%), or the anti-tax movement (23%) has changed things for the better. In anticipating the future, many college students look far afield and point to progressive solutions to both international and domestic problems. A majority (59%) believe that most problems facing their generation will be domestic in nature (e. g. , Social Security), but a significant proportion, 37%, think that most will be international in scope. This outlook is reflected in the majorities of students who believe that the following global issues should be either the top priority or a high priority for Congress and President Bush to address: dealing with the worldwide AIDS epidemic (70%), promoting human rights abroad (64%), and cracking down on imported goods made in sweatshops or with child labor (59%). College students’ political affiliations provide further evidence of the progressive environment on most campuses; by a considerable 48%-to-33% margin, students identify more with the Democratic Party than with the GOP. To a lesser extent, this progressive viewpoint is evident in their vote- Gore edged out Bush by 46% to 42% among those who reported voting (another 9% supported Nader). And had they voted, non-voters with a preference among the candidates would have supported Gore by an even larger margin: Half (52%) would have voted for him over Bush (38%); 7% would have voted for Nader. Nevertheless, their votes make clear that neither of the major party candidates managed to connect with these younger voters. Gore, in particular, had the most to gain from the political disposition of the majority students and the issues they believe are important. Yet, while Democrats enjoy a 15-percentage-point advantage over Republicans on campuses nationwide, Gore held only a 4-point lead over Bush in the college vote. 2. Students do not see politics as a primary means of bringing about positive change. Young people are political voyeurs- they watch, but they don’t participate. Students clearly question the efficacy of getting more deeply involved in the political process. Only 12% believe that volunteering on a campaign is a way to bring about a lot of change (40% say some change). Only half that proportion, 6%, actually participated in a federal, state, or local political campaign during the 2000 election cycle. Students also question the effectiveness of other forms of political action. Only 17% say that attending a demonstration can bring about a lot of change (46% say some change). And as far as contacting an elected official about an issue, only 13% to 17% (depending on whether they are e-mailing or writing) say that this is a way to bring about a lot of change. Because students are not sure that their individual involvement will make much of a difference, most students choose not to get involved other than in the easiest, most convenient ways. Although 56% tell us that they have signed a petition, only 19% have participated in a demonstration, and only 18% have written to a member of Congress. 3. The 2000 presidential election may have sparked an interest in politics and an appreciation of the importance of voting. If the election had a single legacy, it would be arousing this generation’s interest in the political process. Students took an active interest in last year’s presidential election: The vast majority (88%) reports checking the latest news at least once a week during the election. Two in five (42%) say they kept up on the news every day, whereas only 6% say they checked the news no more than once a month. One of the most compelling findings from this research is the respect that students say they have for the vote, which perhaps is a result of the historically close election and the equally historic controversy surrounding the Florida recount. A strong majority (84%) believe that voting in a presidential election is a way to make a difference: 47% say that it can bring about a lot of change, and 37% say that it can effect some change. For most students, voting is far more effective in bringing about change than is volunteering on an election campaign, as only 12% say that the latter can bring about a lot of change. The power of one’s vote is recognized particularly among freshmen (88% a lot/some change), women (87%), and students affiliated with one of the major parties (84% Democrats, 85% Republicans). 4. Students believe in and prefer the direct benefits of volunteering. What’s the alternative to politics? An overwhelming majority (86%) of students tell us that doing volunteer work for groups that help the needy is a way to bring about needed change (50% a lot of change, 36% some change). Most students believe that volunteer programs- more so than the two political parties- have made society better. Solid majorities say that youth-mentoring programs, such as Big Brother/Big Sister (68%); private charities (66%); and groups like AmeriCorps (59%) make a great deal or fair amount of difference toward the betterment of society. Because they believe that getting involved in volunteer programs is a way to help their local communities, most students volunteer during their time in college. A large majority (68%) say that they have been involved in volunteer or other types of charitable activities. Sixty-three percent have volunteered at a local school, hospital, or neighborhood center; 38% have been tutors or mentors; and 27% have helped raise funds for a local cause. Volunteering has become part of the college experience. Among students who have gotten involved in their communities, two in five (39%) have volunteered through a program offered by their college or university. Alternatively, they have worked with an organization (13%) or a religious group (12%) with which they are affiliated. The value that students find in volunteerism is evident in their willingness to consider a longer-term commitment to an organization. Nearly three in four (73%) students would consider volunteering for either Habitat for Humanity (42%), the Peace Corps (21%), AmeriCorps (7%), or VISTA (3%) after they finish college or during a break. The vast majority (83%) of students also says that working for an issue organization is an effective way to make a difference. Most students admire progressive issue organizations for their contributions to society: 59% believe that Mothers Against Drunk Driving (MADD) and other groups promoting alcohol awareness have made a great deal or fair amount of difference; 56% say the same of international human rights’ groups such as Amnesty International. 5. Young people are committed to making a difference, but not through government service. Half (49%) of all college students say that in choosing a career, it is very important (a 9 or 10 on a 10-point scale) that it contribute to society. Yet, only about half as many (26%) tell us that they are very or fairly interested in government service; two in five (42%) say that they have no interest whatsoever in working for the government. In part, students question the government’s ability to make changes for the better. While 50% believe that doing volunteer work to assist the needy can bring about a lot of change, only 20% say the same about choosing a career in government. As a result, even those who put a premium on a career’s potential for making a difference are unwilling to consider government service; among this group, only 26% say that they would be interested in working for the government. Sixty-four percent of students say that providing financial aid or forgiving student loans might be an incentive to participate in public service. Three in ten say that it might help to have a parent or professor encourage them to participate. 6. Today’s students don’t hate the government, but they feel disconnected from it. Generation Y is not hostile toward government. Only 23% say that smaller government should be a top or high priority for the nation; 68% say that they are satisfied with the country’s political leadership; and only 38% feel that a candidate’s working to change the way things are done in Washington is a very important quality. If anything, Generation Y would like to see more from government: 85% say that hiring more teachers and reducing class sizes should be a top or high priority for Congress and President Bush; 76% believe that government should be strengthening and preserving Social Security; 73% want government to assist low-income families with children; and 69% say that the President and Congress should put prescription-drug coverage for seniors on the national agenda. Students don’t know what to make of government. When asked whether they think about government as the government or our government, 60% say the government and 39% say our government. Of course, the degree to which students (and presumably most Americans) feel any ownership of government may depend on who is in power- 47% of Bush voters and 46% of Republicans say our government, compared with only 36% of Gore voters and 35% of Democrats. II. How Can We Engage Young Adults in Politics? tudents’ concern about and willingness to help the vulnerable and disadvantaged demonstrate that they can be engaged. But this is not the 1960s- they are concerned more with the impact they will have and less with the ideal they will serve. If candidates, political parties, and social-change organizations hope to involve young people, they should understand the following: 1. Young people want to make a difference, not just a statement. While today’s generation is drawn to ideals, whether in voting or volunteering, it also wants tangible results. Asked what they would most like to see in a political candidate, half (50%) of college students say that it is very important that they be idealistic and stand up for principles, but more (63%) say that it is very important that candidates be practical and realistic. This pragmatic orientation is reflected in the 50% who believe that volunteering in one’s community can bring about a lot of change; only 17% say the same about participating in demonstrations. Three in four students say that people can make a difference just by living their lives in a way that is consistent with their social and political values. Thus, to inspire college students, political leaders must offer a vision and back it up with concrete action. 2. Students are looking for honest leaders who understand young people’s concerns. This generation has tremendous respect for the gains made by the civil rights and women’s movements. Hence, today’s students want candidates who can address similar challenges as well as be forthright in face of great adversity. They want candidates who are honest (65% say this quality is very important) and who say what they think, even if their positions are unpopular (51%). Nearly half (46%) of students feel that a candidate’s understanding of their values is very important. 3. Provide avenues for individual empowerment and celebrate the power of voting. Today’s students are empowered by volunteering because they believe their individual efforts contribute to a larger cause that makes a difference. Students do not feel the same way about volunteering for a political campaign, however. But the 2000 election left a legacy, a legacy whose effects may be felt for quite some time to come: Students now believe strongly that their individual votes truly count. We must build on this belief by giving young people ways to participate in the political process (e. . , aggressive voter registration and GOTV). 4. Build a bridge between direct service, and politics and public service. Many have already volunteered in their communities, and even more are open to working with Habitat for Humanity or the Peace Corps after graduation. Cultivate their willingness to act on their beliefs, not as an alternative to political engagement, but as an additional reason to either participate in the political process or make a career of public service. The challenge for political leaders and parties is to show young people who are willing to help Jimmy Carter build affordable housing, for example, that public policy and government can accomplish even more, or show Peace Corps volunteers that only the world’s governments have the resources to tackle global problems such as AIDS or exploitative child labor. The potential is already there- students want a government that does more, whether it is hiring more teachers and reducing class sizes or providing assistance to low-income families. 5. Support and encourage young women. Female students (59%) are much more likely than male students (39%) to say that making a difference is a key consideration in choosing a career, and they are more likely than male students to believe that volunteering for a campaign can make a big difference (59% vs. 43%). Moreover, female students already are more involved than their male counterparts- by 35% to 23%, women are more likely to have boycotted a product because of a manufacturer’s wrongdoing, and by 72% to 64%, women are more likely to have volunteered in their community. Yet, women are much less likely than men to say that they would seek elected office: Only 24% of women, compared with 39% of men, report any interest in running for a federal office, and just 24%, versus 43% of men, are interested in running on the state or local level. And while 31% of men say that they are very or fairly interested in a government career, the same can be said of only 21% of women. Young women’s commitment and idealism is there, but it has not yet been linked to politics or public service. They need role models and support. . To reach this generation, go on-line. The attention that many students paid to the election may have been facilitated by the Internet. While television was almost certainly their chief news source about the election (51% say they get most of their political information from TV) a significant proportion also looked to the Internet. Three in ten (29%) say they follow the latest news about politics and civic affairs on-line. Of those studen ts who followed the presidential election each day, 35% say they rely on the Internet for their news. Of those who checked the news once a week or less during the election, only 23% say that most of their news comes from an on-line source. In fact, the Internet has surpassed the newspaper as a chief news source on most college campuses- just one in five (21%) students say that they get most of their news from a city or national newspaper. 7. Parents must lay the groundwork. Ideally, college is a place where young people are on their own for the first time; it’s a place where they can begin to express themselves politically or choose whether to volunteer in their community. On many if not most campuses, students will have the opportunity to see speakers or authors address political issues; they will be asked to sign petitions for a range of causes; they will witness or even participate in demonstrations; or they may become a volunteer through a program offered by their school or an on-campus organization. Whether students take advantage of these new freedoms depends in large part on their parents. When children grow up discussing politics with their parents, they grow up to be far more interested and involved, both in the political process and in their community. Half of all students say that while they were growing up, they discussed politics with their parents very or fairly often; 39% indicate that they rarely did so; and 11% say that they never discussed politics at home. Of those college students who regularly discussed politics with their parents, 50% report checking the latest political news every day during the 2000 election, compared with only 34% of those who grew up in households where politics was not a topic of conversation; 10% of those reared on politics have volunteered on a political campaign, compared with only 2% of students from apolitical households. In addition, young people raised on politics are more likely to believe that a career in government or public service leads to change (24% major change, 47% some change); among students in non-political households, only 16% believe it would make a major change, and 50% say it would bring about some change.