A Bi-level assessment of Twitter in predicting the results of an election: Delhi Assembly Elections 2020

TL;DR

This study investigates how Twitter activity and voter sentiment relate to the 2020 Delhi Assembly election outcomes, finding follower count and reply activity are good predictors, while mention volume and sentiment are less aligned.

Contribution

It introduces a bi-level approach analyzing candidate and party Twitter data, and applies machine learning models to predict election results based on social media features.

Findings

Follower count and replies predict election outcomes

Mention volume and sentiment are less correlated with results

Random forest models effectively predict winners based on tweet features

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 Delhi Assembly elections held in 2020, using a bi-level 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. 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 the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate.