Predicting elections from social media: a three-country, three-method comparative study

Abstract

This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries-Malaysia, India, and Pakistan from Twitter posts. We find that predictive power of social media performs well for India and Pakistan but is not effective for Malaysia. Overall, we find that it is useful to consider the recency of Twitter posts while using it to predict a real outcome, such as an election result. Sentiment information mined using machine learning models was the most accurate predictor of election outcomes. Social network information is stable despite sudden surges in political discussions, for e.g. around electionsrelated news events. Methods combining sentiment and volume information, or sentiment and social network information, are effective at predicting smaller vote shares, for e.g. vote shares in the case of independent candidates and regional parties. We conclude with a detailed discussion on the caveats of social media analysis for predicting real-world outcomes and recommendations for future work.

Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

“…On the other hand, a supervised learning approach, which trains sentiment models on a small set of hand-annotated political social media messages, yields much better predictions by inferring sentiments from otherwise neutral words used in context. Furthermore, studies have suggested that discarding negative posts and instead focusing on the positive tweets can help to filter out a large part of the noise from election-related content on social media [24].…”

“…Structural features can also capture the density of online discussions. More decentralized networks have more active users and thus wider outreach to a larger potential voter base [24]. Structural features have been found to be useful to dampen the estimation effects associated with national parties that are over-represented on social media, or regional parties which may be popular online.…”

“…Second, social media data can be used to profile not only the active users but also "lurkers" who may not openly volunteer an opinion, by examining the relational connections and network attributes of their accounts. Third, by looking at social media posts over time, we can examine opinion dynamics, public sentiment and information diffusion within a population [24].…”

Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

“…Presidential campaign prediction using Twitter data is a much-discussed research topic and the number of studies are still growing (Awais et al, 2019;Bansal & Srivastava, 2019;Heredia et al, 2018;Jaidka et al, 2019;McGregor et al, 2017;Verma et al, 2019). There is, however, no real benchmarking on the performance (Gayo-Avello, 2013) to identify the best method to determine the winner of the elections or the voting share with the minimum error.…”

“…to predict consumer behaviour in the real world (Rathor et al, 2018;Volkova et al, 2015). Recently this type of analysis has jumped into the eld of politics in an attempt to predict the results of election campaigns by monitoring the interaction between candidates and voters (Awais et al, 2019;Jaidka et al, 2019;Shmargad & Sanchez, 2020). The fact that more and more people are posting on the OSN had led to researchers and journalists to believe that a collective feeling is present in the OSN, which can be listened to, captured and analysed (Budiharto & Meiliana, 2018;Cury, 2019).…”