  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.
