Analysing Russian Trolls via NLP tools
This addresses the issue of foreign interference in elections for policymakers and researchers, but it is incremental as it applies existing NLP tools to a specific dataset.
This paper tackled the problem of analyzing Russian troll activity on Twitter during the 2016 U.S. presidential election using NLP methods, finding interesting patterns and meaningful topics in the dataset.
The fifty-eighth American presidential election in 2016 still arouse fierce controversyat present. A portion of politicians as well as medium and voters believe that theRussian government interfered with the election of 2016 by controlling malicioussocial media accounts on twitter, such as trolls and bots accounts. Both of them willbroadcast fake news, derail the conversations about election, and mislead people.Therefore, this paper will focus on analysing some of the twitter dataset about theelection of 2016 by using NLP methods and looking for some interesting patterns ofwhether the Russian government interfered with the election or not. We apply topicmodel on the given twitter dataset to extract some interesting topics and analysethe meaning, then we implement supervised topic model to retrieve the relationshipbetween topics to category which is left troll or right troll, and analyse the pattern.Additionally, we will do sentiment analysis to analyse the attitude of the tweet. Afterextracting typical tweets from interesting topic, sentiment analysis offers the ability toknow whether the tweet supports this topic or not. Based on comprehensive analysisand evaluation, we find interesting patterns of the dataset as well as some meaningfultopics.