Hashtags are (not) judgemental: The untold story of Lok Sabha elections 2019

TL;DR

This paper conducts a large-scale empirical analysis of Twitter hashtags during the 2019 Indian Lok Sabha elections, uncovering trends, topics, and semantic relationships to predict election outcomes.

Contribution

It introduces a comprehensive analysis of election-related hashtags, employing novel metrics like influence to predict election results solely from hashtag data.

Findings

Influence metric effectively predicts election outcomes.

Trending hashtags correlate with key political events.

Latent topics reveal underlying themes in election discourse.

Abstract

Hashtags in online social media have become a way for users to build communities around topics, promote opinions, and categorize messages. In the political context, hashtags on Twitter are used by users to campaign for their parties, spread news, or to get followers and get a general idea by following a discussion built around a hashtag. In the past, researchers have studied certain types and specific properties of hashtags by utilizing a lot of data collected around hashtags. In this paper, we perform a large-scale empirical analysis of elections using only the hashtags shared on Twitter during the 2019 Lok Sabha elections in India. We study the trends and events unfolded on the ground, the latent topics to uncover representative hashtags and semantic similarity to relate hashtags with the election outcomes. We collect over 24 million hashtags to perform extensive experiments. First, we find the trending hashtags to cross-reference them with the tweets in our dataset to list down notable events. Second, we use Latent Dirichlet Allocation to find topic patterns in the dataset. In the end, we use skip-gram word embedding model to find semantically similar hashtags. We propose popularity and an influence metric to predict election outcomes using just the hashtags. Empirical results show that influence is a good measure to predict the election outcome.