Abstract
In this paper we evaluate the sentiment of messages by Russian Internet Research Agency (IRA) on Twitter discourse during US Presidential Elections of 2016 using VADER-a rule-based model for sentiment analysis of social media text. We use two datasets for analysis. The first consists of 51.3 million tweets collected during the US Elections of 2016 (October 30th, 2016-November 18th, 2016) and the second was shared by Twitter in October 2018, consisting of 8.77 million tweets generated by IRA accounts over a decade. We look for overlap of IRA tweets in the two datasets, evaluate their sentiment, and compare it with sentiment of other messages during that time period discussing the two Presidential candidates. Our findings show: (1) IRA tweets and retweets had a significantly positive sentiment towards Donald Trump and negative sentiment towards Hillary Clinton; (2) IRA messages mentioning Hillary Clinton had a more negative sentiment than non-IRA messages in our dataset.
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