Predicting the Role of Political Trolls in Social Media

Abstract

We investigate the political roles of "Internet trolls" in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and laborintensive, thus making it impractical as a firstresponse tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role -left, news feed, right-by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e., embeddings, for the trolls. Experiments on the "IRA Russian Troll" dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far.

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

“…In future work, we plan to perform user profiling with respect to polarizing topics such as gun control , which can then be propagated from users to media (Atanasov et al, 2019;. We further want to model the network structure, e.g., using graph embeddings .…”

“…Previous research has used the followers' networks and the retweeting behavior in order to infer the political bias of news media (Wong et al, 2013;Atanasov et al, 2019;. Here, we analyze the self-description (bio) of Twitter users that follow the target news medium.…”

A Survey on Troll Detection

“…In practice, features of trolls and legitimate users are collected, through the analysis of: writing style, sentiment, behaviours, social interactions, linked media and publication time. A machine learning approach is finally used to identify trolls with very high accuracy [12,38,39].…”

Predicting the Topical Stance and Political Leaning of Media using Tweets

“…Graph embeddings. We further use graph embeddings, generated by building a User-to-Hashtag graph (U2H) and a User-to-Mention (U2M) graph and then running node2vec on both (Atanasov et al, 2019), producing two types of graph embeddings. When using graph embeddings, we got worse results compared to our previous setup with valence scores (see Table 6).…”