How (Not) to Predict Elections

By Panagiotis Takis Metaxas, Eni Mustafaraj, Daniel Gayo-Avello. 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011

302 citations recorded by the source corpus.

Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of ''pulsing'' the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4b923335acff7329d6d3865c6271652c4db9b366

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Citations in the corpus

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View (2016, background): “It is not rare to see potentially spurious conclusions drawn from methodologically inadequate studies [7-11], which in turn compromises the credibility of other valid studies and discourages many researchers who could benefit from adopting machine learning techniques.”
Predicting political preference of Twitter users (2013, methodology): “Finally, as a sanity check, we compare our method to chance; it has been argued that when reporting predictions on Twitter, chance is a viable baseline (Metaxas et al. 2011).”
Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum (2016, background): “But it is still worth noting the contrast, and computational social scientists do not yet understand the sampling parameters sufficiently to make many inferences about how opinion on social media translates to voter intent.[13] Table 1: Hashtag Use on Twitter, by Perspective on the UK Referendum”
"I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" - A Balanced Survey on Election Prediction using Twitter Data (2012, background): “It is also especially worrisome that many recent papers seem to only acknowledge those studies supporting the idea of Twitter predicting elections, instead of conducting a balanced literature review showing both sides of the matter.”