2 - Political Opinion
Published online by Cambridge University Press: 05 May 2015
Summary
Despite being a fairly recent phenomenon, microblogging has attracted a large number of researchers and practitioners who consider microposts a suitable source of data to ascertain public opinion. Among the reasons for that interest, we may find the fact that one single platform (i.e., Twitter) is the default choice for users; the ease with which one can collect data using public application programming interfaces (APIs); and the brevity of microposts, which forces users to get to the point when discussing any given topic.
This chapter is focused on efforts to exploit Twitter data to scrutinize public opinion in general, and political discussion in particular. It covers representative case studies conducted during the late 2000s and early 2010s and discusses their respective limitations. Finally, we analyze the implications of such approaches to political opinion in Twitter and depict important lines of research to further advance the field.
Introduction
This chapter is devoted to the problem of exploiting Twitter data to take the pulse of public opinion, particularly with regard to electoral forecasting. However, most of the arguments exposed here are not limited to Twitter, but apply broadly to any social networking site.
Twitter is (at the moment of this writing) the most convenient way to obtain user-generated content of opinionated nature about current events. That is the main reason why so much research has been performed on that platform and, in turn, such high expectations have been put on mining Twitter data.
For an in-depth overview of Twitter, in particular its historical evolution, consult the work by Van Dijck (2013, pp. 68–88). Rogers (2013) also analyzes Twitter's evolution, but mainly from a research perspective. His work shows the way in which research has evolved together with the service. In this regard, he provides compelling arguments to drop, once and for all, the caricatured image of Twitter as “pointless babble” and to acknowledge finally that it “serves as a mean to study cultural conditions.” Indeed, that simple idea pervades this chapter and the rest of the book.
- Type
- Chapter
- Information
- Twitter: A Digital Socioscope , pp. 52 - 74Publisher: Cambridge University PressPrint publication year: 2015
References
, , & (2012). Homophily and latent attribute inference: inferring latent attributes of Twitter users from neighbors. In ICWSM ’12 (p. 1). AAAI.
(1937). Toward a science of public opinion. Public Opinion Quarterly, 1(1), 7–23.CrossRefGoogle Scholar
, & (2010). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010IEEE/WIC/ACM International Conference, ed. Xiangji Jimmy Huang, Irwin King, Vijay Rahhavan, and Stefan Ruger (vol. 1, pp. 492–9). IEEE.Google Scholar
(2013). Gay Politics, Urban Politics: Identity and Economics in the Urban Setting. Columbia University Press.Google Scholar
, , , , , & (2013). Sentiment analysis of political tweets: towards an accurate classifier. NAACL, 49, 49–58.Google Scholar
(2012). A new measure of party identification in Twitter: evidence from Spain. In Proceedings of the 2nd Annual General Conference of EPSA. European Political Science Association.
, & (2009). Democrat, Republican, or none of the above? The role of religiosity in Muslim American party identification. Politics and Religion, 2(02), 200–29.CrossRefGoogle Scholar
(2014). Predicting and interpolating state-level polling using Twitter textual data. Working paper.
, & (2011). On using Twitter to monitor political sentiment and predict election results. In Proceedings of the Workshop on Sentiment Analysis Where AI Meets Psychology (SAAIP), IJCNLP 2011 (pp. 2–10). Asian Federation of Natural Language Processing.
, , & (2010). Determining the public mood state by analysis of microblogging posts. In Proceedings of the ALIFE XII Conference (pp. 667–8). ALIFE.
, , & (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. ICWSM.
, ., , & (2012). Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish presidential election as a case study. Chaos, 22(2), 023138.CrossRef
, , & (2012). What's in your tweets? I know who you supported in the UK 2010 general election. In Proceedings of the Sixth International AAAI. Conference on Weblogs and Social Media (ICWSM).
, & (1999). Affective norms for English words (ANEW): instruction manual and affective ratings (pp. 1–45). Technical Report C-1, Center for Research in Psychophysiology, University of Florida.
, , , , , , , & (forthcoming). Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change.
, , , , , , & (2014). A multi-level geographical study of Italian political elections from Twitter data. PLOS ONE, 9(5), e95809.CrossRefGoogle ScholarPubMed
, & (2008). The religion card: gay marriage and the 2004 presidential election. Public Opinion Quarterly, 72(3), 399–419.CrossRefGoogle Scholar
, , , & (2009). Clues for detecting irony in user-generated contents: oh...!! it's so easy;-). In Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion (pp. 53–6). ACM.CrossRef
, , & (2011). Information credibility on Twitter. In Proceedings of the 20th International Conference on the World Wide Web (pp. 675–84). ACM.CrossRef
, , & . (2013). Predicting information credibility in time-sensitive social media. Internet Research, 23(5), 560–88.CrossRefGoogle Scholar
, , , & (2013). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 1461444813480466.Google Scholar
, & (2012). Predicting the present with Google trends. Economic Record, 88(s1), 2–9.CrossRefGoogle Scholar
, , , & (2010). Who is tweeting on Twitter: human, bot, or cyborg? In Proceedings of the 26th Annual Computer Security Applications Conference (pp. 21–30). ACM.CrossRef
, & (2013). Classifying political orientation on Twitter: It's not easy! In Proceedings of the 7th International Conference on Weblogs and Social Media. AAAI.
, , , , & (2011). Predicting the political alignment of twitter users. In Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Conference on Social Computing (SocialCom) (pp. 192–9). IEEE.CrossRef
(1998). Counting on the Latino Vote: Latinos as a New Electorate. University of Virginia Press.Google Scholar
(2013). Blog Theory: Feedback and Capture in the Circuits of Drive. John Wiley & Sons.Google Scholar
, , , & (2013). More tweets, more votes: social media as a quantitative indicator of political behavior. PLOS ONE, 8(11), e79449.CrossRefGoogle ScholarPubMed
, & (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1195–8). ACM.CrossRef
, & . (2006). Sentiwordnet: a publicly available lexical resource for opinion mining. In Proceedings of LREC (vol. 6, pp. 417–22).
(2013). Discourse architecture, ideology, and democratic norms in online political discussion. New Media & Society, 1461444813513259.Google Scholar
(2010). Analyzing online political discussion using three models of democratic communication. New Media & Society, 12(7), 1172–90.CrossRefGoogle Scholar
(2001). Fenced Off: The Suburbanization of American Politics. Georgetown University Press.Google Scholar
(2011a). All liaisons are dangerous when all your friends are known to us. In Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia (pp. 171–80). ACM.
(2011b). Don't turn social media into another “Literary Digest” poll. Communications of the ACM, 54(10), 121–8.CrossRefGoogle Scholar
(2012). No, you cannot predict elections with Twitter. Internet Computing, IEEE, 16(6), 91–4.CrossRefGoogle Scholar
(2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review, 31(6), 649–79.CrossRefGoogle Scholar
, , , , , & (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–14.CrossRefGoogle ScholarPubMed
, & (1977). Rural-urban differences in attitudes and behavior in the United States. Annals of the American Academy of Political and Social Science, 429(1), 36–50.CrossRefGoogle Scholar
, & (2011). Computing political preference among Twitter followers. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1105–8). ACM.CrossRef
, , , & (2014). From sentiment analysis to preference aggregation. In Proceedings of the 2014 International Symposium on Artificial Intelligence and Mathematics (ISAIM-2014). AAAI Press.
, , & (2004). Partisan Hearts and Minds: Political Parties and the Social Identities of Voters. Yale University Press.Google Scholar
, , , & (2005). The American religious landscape and the 2004 presidential vote: increased polarization. Paper circulated by Bliss Institute, University of Akron.
(1991). The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society. MIT Press.Google Scholar
(1996). The Lavender Vote: Lesbians, Gay Men, and Bisexuals in American Electoral Politics. NYU Press.Google Scholar
(2013). Multi-cycle forecasting of congressional elections with social media. In Proceedings of the 2nd workshop on Politics, Elections and Data (pp. 23–30). ACM.CrossRef
. (2015). Awaiting the second big data revolution: From digital noise to value creation. Journal of Industry, Competition and Trade. Online first article.CrossRef
(2009). The politics of Muslims in America. Politics and Religion, 2(02), 163–99.CrossRefGoogle Scholar
, , , & . (2009). Twitter power: tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–88.CrossRefGoogle Scholar
, & (2013). Psephological investigations: tweets, votes, and unknown unknowns in the republican nomination process. Policy & Internet, 5(2), 161–82.CrossRefGoogle Scholar
, , & (2012). Why the Pirate Party won the German election of 2009 or the trouble with predictions: a response to Tumasjan, A., Sprenger, T.O., Sander, P.G., & Welpe, I.M. “Predicting elections with Twitter: what 140 characters reveal about political sentiment.” Social Science Computer Review, 30(2), 229–34.CrossRefGoogle Scholar
, , & (2013). Understanding the predictive power of social media. Internet Research, 23(5), 544–59.CrossRefGoogle Scholar
, , , & (2012). Statistical detection of systematic election irregularities. Proceedings of the National Academy of Sciences, 109(41), 16469–73.CrossRefGoogle ScholarPubMed
(2012). On voting intentions inference from Twitter content: a case study on UK 2010 general election. arXiv preprint arXiv:1204.0423.
, , & (2010). Flu detector-tracking epidemics on Twitter. In Machine Learning and Knowledge Discovery in Databases (pp. 599–602). SpringerBerlin Heidelberg.Google Scholar
, , & (2013). A user-centric model of voting intention from social media. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (pp. 993–1003). Association for Computational Linguistics.
, , & (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(02), 311–31.CrossRefGoogle Scholar
, & (1992). Individual and systemic influences on turnout: who votes? 1984. Journal of Politics, 54(03), 718–40.CrossRefGoogle Scholar
, , , & (2011). The party is over here: structure and content in the 2010 election. ICWSM (July).
, & (2012). Learning for microblogs with distant supervision: political forecasting with Twitter. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 603–12). Association for Computational Linguistics.
(2012). Sentiment analysis within and across social media streams. Ph.D. (doctor of philosophy) thesis, available at http://ir.uiowa.edu/etd/2943.
, , & (2010). Twitter under crisis: can we trust what we RT? In Proceedings of the First Workshop on Social Media Analytics (pp. 71–79). ACM.CrossRef
, & (2010). From obscurity to prominence in minutes: political speech and real-time search. In Proceedings of Web Science Conference 2010. ACM.
, , & (2011). How (not) to predict elections. In Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Conference on Social Computing (SocialCom) (pp. 165–71). IEEE.CrossRef
(1995). The industry of public opinion. In Public Opinion and the Communication of Consent, ed. Theodore L. Glasser and Charles T. Salmon (pp. 105–31). Guilford Press.
, , , , & (2011). Understanding the demographics of Twitter users. ICWSM, 11 (5).
, & (2013). Twitter reaction to events often at odds with overall public opinion. Technical Report. Pew Research Center.
, , , & (2013). Is the sample good enough? Comparing data from Twitter's streaming API with Twitter's firehose. In Proceedings of ICWSM. AAAI Press.
, , , & (2011). Vocal minority versus silent majority: discovering the opinions of the long tail. In Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Conference on Social Computing (SocialCom) (pp. 103–10). IEEE.CrossRef
, , , & (2010). From tweets to polls: linking text sentiment to public opinion time series. ICWSM’10, 122–9.Google Scholar
, & (2011a). A machine learning approach to Twitter user classification. In Proceedings of the Fifth International AAAI Conferences on Weblogs and Social Media (pp. 281–8). AAAI Press.
, & (2011b). Democrats, Republicans and Starbucks afficionados: user classification in Twitter. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 430–8). ACM.
, , , & (2010). Classifying latent user attributes in Twitter. In Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents (pp. 37–44). ACM.CrossRef
, , , , , & (2011a). Detecting and tracking political abuse in social media. In Proceedings of the Fifth International AAAI Conferences on Weblogs and Social Media (pp. 297–304). AAAI.
, , , , , , & (2011b). Truthy: mapping the spread of astroturf in microblog streams. In Proceedings of the 20th International Conference Companion on the World Wide Web (pp. 249–52). ACM.
, , and . From humor recognition to irony detection: the figurative language of social media. Data & Knowledge Engineering 74 (2012), 1–12.CrossRefGoogle Scholar
, , & (2011). A short introduction to preferences: between artificial intelligence and social choice. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(4), 1–102.CrossRefGoogle Scholar
, & (2012). Predicting the 2011 Dutch senate election results with Twitter. In Proceedings of the Workshop on Semantic Analysis in Social Media (pp. 53–60). Association for Computational Linguistics.
, & (1999). The urban electorate in presidential elections, 1920–1996. Urban Affairs Review, 35(1), 72–91.CrossRefGoogle Scholar
, , , , , & (2013). The power of prediction with social media. Internet Research, 23(5), 528–43.CrossRefGoogle Scholar
, , & (1997). Sex as a Political Variable: Women as Candidates and Voters in US Elections. Lynne Rienner.Google Scholar
, & (1986). Gender differences in policy preferences: a summary of trends from the 1960s to the 1980s. Public Opinion Quarterly, 50(1), 42–61.
(1996). The political power of lesbians, gays, and bisexuals. PS: Political Science & Politics, 29(03), 469–73.Google Scholar
, , , ., & (2012). Predicting US primary elections with Twitter. In Proceedings of Social Network and Social Media Analysis: Methods, Models and Applications (NIPS Workshop), Lake Tahoe, NV, December, vol. 7.
, , & (2011). The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PLOS ONE, 6(5), e19467.CrossRefGoogle Scholar
, , , , & (2012). Tweets and votes: a study of the 2011 Singapore general election. In 2012 45th Hawaii International Conference on System Science (HICSS) (pp. 2583–91). IEEE.CrossRef
(ed.). (1994). From Protest to Politics: The New Black Voters in American Elections.Harvard University Press.Google Scholar
, & (2013). Scaling-laws of human broadcast communication enable distinction between human, corporate and robot twitter users. PLOS ONE, 8(7), e65774.CrossRefGoogle ScholarPubMed
(2014). Big questions for social media big data: representativeness, validity and other methodological pitfalls. In Proceedings of the Seventh International Conference on Weblogs and Social Media. AAAI.
, , , & (2010). Predicting elections with Twitter: what 140 characters reveal about political sentiment. ICWSM, 10, 178–85.Google Scholar
(2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (pp. 417–424). Association for Computational Linguistics.
(2013). The Culture of Connectivity: A Critical History of Social Media. Oxford University Press.CrossRefGoogle Scholar
, , & (2012, May). Measuring and quantifying the silent majority on the Internet. In Sarnoff Symposium (SARNOFF), 2012 35th IEEE (pp. 1–5). IEEE.
, , & (2014). Inferring user political preferences from streaming communications. In Proceedings of the Association for Computational Linguistics (ACL). Association for Computational Linguistics.CrossRef
(2006). The causes of city-suburban political polarization? A Canadian case study. Annals of the Association of American Geographers, 96(2), 390–414.CrossRefGoogle Scholar
, , & (2013). Secular vs. Islamist polarization in Egypt on Twitter. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 290–7). ACM.
, , & (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 347–54). Association for Computational Linguistics.CrossRef
, , & (2012). Why watching movie tweets won't tell the whole story? In Proceedings of the 2012 ACM Workshop on Workshop on Online Social Networks (pp. 61–6). ACM.
, , & (2008). Exploring the characteristics of opinion expressions for political opinion classification. In Proceedings of the 2008 International Conference on Digital Government Research (pp. 82–91). Digital Government Society of North America.
, & (2011, January). Detecting and analyzing automated activity on Twitter. In Passive and Active Measurement, ed. Neil Spring and George F. Riley (pp. 102–11). Springer Berlin Heidelberg.
- 2
- Cited by