Analysing Russian Trolls via NLP tools

TL;DR

This paper uses NLP techniques to analyze Twitter data from the 2016 US election, identifying patterns and topics related to Russian interference through topic modeling, sentiment analysis, and supervised classification.

Contribution

It introduces a combined NLP approach with topic modeling and sentiment analysis to detect and analyze Russian troll activity on social media during the 2016 election.

Findings

Identified distinct topics associated with troll accounts

Revealed sentiment patterns supporting or opposing election topics

Demonstrated the effectiveness of supervised topic models in classifying troll types

Abstract

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.