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Do We Need Polls? Why Twitter Will Not Replace Opinion Surveys, but Can Complement Them

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Digital Methods for Social Science

Abstract

Monitoring and using social media to understand — or influence — public opinion is not a new thing. Companies, political parties and organizations alike are keen to observe what their followers say, what people are commenting on their Facebook Pages and what is said in the comments sections of YouTube and Instagram. Moreover, a great deal of work has been done in building social media teams in charge of both engaging and analysing what people exchange through these platforms. To some extent, these phenomena have questioned whether traditional, more expensive, ways to observe public opinion are still required. The regular route for understanding public opinion, both at the consumer and the political levels, relies heavily on surveys. These instruments present their own advantages depending on the scope of the research. Moreover, they enjoy a fair amount of validity among the scientific community as proper instruments to analyse public attitudes.

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Notes

  • Ansolabehere, S. and Hersh, E. (2012) ‘Validation: What big data reveal about survey misreporting and the real electorate’, Political Analysis, 20(4), 437–59.

    Article  Google Scholar 

  • Ansolabehere, S., Rodden, J. and Snyder, J.M. (2008) ‘The strength of issues: Using multiple measures to gauge preference stability, ideological constraint, and issue voting’, American Political Science Review, 102(2), 215–32.

    Article  Google Scholar 

  • Barberá, P. (2015) ‘Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data’, Political Analysis, 23(1), 76–91.

    Article  Google Scholar 

  • Bartels, L.M. (2005) ‘Homer gets a tax cut: Inequality and public policy in the American mind’, Perspectives on Politics, 3(1), 5–31.

    Article  Google Scholar 

  • Bartels, L.M. (2010) ‘The study of electoral behavior’, in J.E. Leighley (ed.) The Oxford Handbook of American Elections and Political Behavior. Oxford: Oxford University Press, pp.239–61.

    Google Scholar 

  • Beauchamp, N. (2013) ‘Predicting and interpolating state-level polling using Twitter textual data’, Meeting on Automated Text Analysis. London: London School of Economics.

    Google Scholar 

  • Bunker, K. (n.d.) Tresquintos: Análisis Políticos y Predicciones Electorales, http://www.tresquintos.com, date accessed 1 August 2014. Converse, P.E. (1975) ‘Public opinion and voting behavior’, in F. Greenstein and N. Polsby (eds.) Handbook of Political Science (Vol. 4). Reading, MA: Addison-Wesley, pp. 75–169.

    Google Scholar 

  • Dalton, R.J. (2000) The Decline of Party Identification. Oxford: Oxford University Press.

    Google Scholar 

  • DiGrazia, J., McKelvey, K., Bollen, J. and Rojas, F. (2013) ‘More tweets, more votes: Social media as a quantitative indicator of political behavior’, PLoS One, 8(11), e79449.

    Google Scholar 

  • Gayo-Avello, D. (2012) ‘I wanted to predict elections with Twitter and all I got was this Lousy paper: A balanced survey on election prediction using Twitter data’, arXiv preprint arXiv, 1204, 6441.

    Google Scholar 

  • Godbole, N., Srinivasaiah, M. and Skiena, S. (2007) ‘Large-scale sentiment analysis for news and blogs’, International Conference on Weblogs and Social Media, Boulder, CO, 26–28 March 2007.

    Google Scholar 

  • Iyengar, S., Sood, G. and Lelkes, Y. (2012) ‘Affect, not ideology a social identity perspective on polarization’, Public Opinion Quarterly, 76(3), 405–31.

    Article  Google Scholar 

  • Jurka, T.P. (2012) Sentiment: Tools for Sentiment Analysis. R package version 0.2, http://CRAN.R-project.org/package=sentiment.

    Google Scholar 

  • Laver, M., Benoit, K. and Garry, J. (2003) ‘Extracting policy positions from political texts using words as data’, American Political Science Review, 97(2), 311–31.

    Article  Google Scholar 

  • Lietz, H., Wagner, C., Bleier, A. and Strohmaier, M. (2014). ‘When politicians talk: Assessing online conversational practices of political parties on Twitter’, Computing Research Repository, (CoRR), abs/1405, 6824.

    Google Scholar 

  • López-Sáez, M. and Martínez-Rubio, J. (2005) ‘¿Influyeron los procesos de comu-nicaci’on sobre los sucesos del 11-M en las votaciones del 14-M? La percepción de los jóvenes en función de su ideología política’, Revista de Psicología Social, 20(3), 351–67.

    Article  Google Scholar 

  • Lowe, W (2008) ‘Understanding wordscores’, Political Analysis, 16(4), 356–371.

    Article  Google Scholar 

  • Lowe, W. (2013) ‘There’s (basically) only one way to do it’, Social Science Research Network, http://ssrn.com/abstract=2318543.

    Google Scholar 

  • Morstatter, F., Pfeffer, J., Liu, H. and Carley, K.M. (2013) ‘Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose’, International Conference on Weblogs and Social Media, Cambridge, MA, 8–11 July 2013.

    Google Scholar 

  • Pang, B. and Lee, L. (2008) ‘Opinion mining and sentiment analysis’, Foundations and Trends in Information Retrieval, 2(2), 1–135.

    Article  Google Scholar 

  • Sajuria, J. (2014) Sentimiento: Package for Sentiment Analysis in Spanish [beta]. R package version 0.1, available from https://github.com/jsajuria/sentimiento.

    Google Scholar 

  • Wilson, T., Wiebe, J., and Hoffmann, P. (2005) ‘Recognizing contextual polarity in phrase-level sentiment analysis’, Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, pp.347–54.

    Chapter  Google Scholar 

  • Zaller, J. (1992) The Nature and Origins of Mass Opinion. Cambridge: Cambridge University Press.

    Book  Google Scholar 

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© 2016 Javier Sajuria and Jorge Fábrega

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Sajuria, J., Fábrega, J. (2016). Do We Need Polls? Why Twitter Will Not Replace Opinion Surveys, but Can Complement Them. In: Snee, H., Hine, C., Morey, Y., Roberts, S., Watson, H. (eds) Digital Methods for Social Science. Palgrave Macmillan, London. https://doi.org/10.1057/9781137453662_6

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  • DOI: https://doi.org/10.1057/9781137453662_6

  • Publisher Name: Palgrave Macmillan, London

  • Print ISBN: 978-1-349-55862-9

  • Online ISBN: 978-1-137-45366-2

  • eBook Packages: Social SciencesSocial Sciences (R0)

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