  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.
