"I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" - A Balanced Survey on Election Prediction using Twitter Data

By Daniel Gayo-Avello. 2012

244 citations recorded by the source corpus.

Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such kind of studies, electoral prediction is maybe the most attractive, and at this moment there is a growing body of literature on such a topic. This is not only an interesting research problem but, above all, it is extremely difficult. However, most of the authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. 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. After reading many of such papers I have decided to write such a survey myself. Hence, in this paper, every study relevant to the matter of electoral prediction using social media is commented. From this review it can be concluded that the predictive power of Twitter regarding elections has been greatly exaggerated, and that hard research problems still lie ahead.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “, 2015b; Pavalanathan and Eisenstein, 2015), or exit pools or election results (Gayo-Avello et al., 2011; Gayo-Avello, 2012; Gayo-Avello, 2013).”
The Twitter of Babel: Mapping World Languages through Microblogging Platforms (2012, result): “This is not surprising, and corroborates previous analysis pointing out the possible distortions induced by observing the World through Twitter [13,32].”
Can Online Emotions Predict the Stock Market in China? (2016, background): “In addition, there have long been controversies on predictive power of social media aiming at different fields [7,14].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “However, other research expresses skepticism at the extent to which forecasting is possible (Avello, 2012).”
On Unravelling Opinions of Issue Specific-Silent Users in Social Media (2016, background): “As a result, one may obtain a biased opinion profile of the entire user community (Lin et al. 2013; Gayo-Avello 2012).”
Characterizing Silent Users in Social Media Communities (2015, unknown): “The problem of silent users has been pointed out in a few previous works (Mustafaraj et al. 2011; Gayo-Avello 2012; Lin et al. 2013).”