TL;DR
This paper presents a machine learning approach to automatically classify political trolls on social media by their roles, using social network features and text analysis, applicable in both supervised and distant supervision settings.
Contribution
It introduces a novel methodology for classifying trolls' political roles using social media data, leveraging community structure and message content, and explores both supervised and distant supervision scenarios.
Findings
Method outperforms state-of-the-art in supervised setting.
Effective in distant supervision scenario without explicit troll labels.
Demonstrates applicability to real-world troll detection tasks.
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 labor-intensive, thus making it impractical as a first-response 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…
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