A Bi-level assessment of twitter data for election prediction: Delhi assembly elections 2020

Abstract

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of the Delhi Assembly elections held in 2020, using a bilevel approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party over time. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to candidates’ tweets are good indicators for predicting actual election outcomes. However, we observe that the number of tweets mentioning a party and the temporal analysis of voters’ sentiment towards the party shown in tweets are not aligned with the election result. Moreover, the variations in the activeness of candidates and political parties on Twitter with time also not very helpful in identifying the winner. Thus, merely using temporal data from Twitter is not sufficient to make accurate predictions, especially for countries like India.