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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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.
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.
Barberá, P. (2015) ‘Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data’, Political Analysis, 23(1), 76–91.
Bartels, L.M. (2005) ‘Homer gets a tax cut: Inequality and public policy in the American mind’, Perspectives on Politics, 3(1), 5–31.
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.
Beauchamp, N. (2013) ‘Predicting and interpolating state-level polling using Twitter textual data’, Meeting on Automated Text Analysis. London: London School of Economics.
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.
Dalton, R.J. (2000) The Decline of Party Identification. Oxford: Oxford University Press.
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.
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.
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.
Iyengar, S., Sood, G. and Lelkes, Y. (2012) ‘Affect, not ideology a social identity perspective on polarization’, Public Opinion Quarterly, 76(3), 405–31.
Jurka, T.P. (2012) Sentiment: Tools for Sentiment Analysis. R package version 0.2, http://CRAN.R-project.org/package=sentiment.
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.
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.
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.
Lowe, W (2008) ‘Understanding wordscores’, Political Analysis, 16(4), 356–371.
Lowe, W. (2013) ‘There’s (basically) only one way to do it’, Social Science Research Network, http://ssrn.com/abstract=2318543.
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.
Pang, B. and Lee, L. (2008) ‘Opinion mining and sentiment analysis’, Foundations and Trends in Information Retrieval, 2(2), 1–135.
Sajuria, J. (2014) Sentimiento: Package for Sentiment Analysis in Spanish [beta]. R package version 0.1, available from https://github.com/jsajuria/sentimiento.
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.
Zaller, J. (1992) The Nature and Origins of Mass Opinion. Cambridge: Cambridge University Press.
Editor information
Editors and Affiliations
Copyright information
© 2016 Javier Sajuria and Jorge Fábrega
About this chapter
Cite this chapter
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
Download citation
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)