English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We investigate the political roles of "Internet trolls" in social media.Political trolls, such as the ones linked to the Russian Internet ResearchAgency (IRA), have recently gained enormous attention for their ability to swaypublic opinion and even influence elections. Analysis of the online traces oftrolls has shown different behavioral patterns, which target different slicesof the population. However, this analysis is manual and labor-intensive, thusmaking it impractical as a first-response tool for newly-discovered trollfarms. In this paper, we show how to automate this analysis by using machinelearning in a realistic setting. In particular, we show how to classify trollsaccording to their political role ---left, news feed, right--- by usingfeatures extracted from social media, i.e., Twitter, in two scenarios: (i) in atraditional supervised learning scenario, where labels for trolls areavailable, and (ii) in a distant supervision scenario, where labels for trollsare not available, and we rely on more-commonly-available labels for newsoutlets mentioned by the trolls. Technically, we leverage the communitystructure and the text of the messages in the online social network of trollsrepresented as a graph, from which we extract several types of learnedrepresentations, i.e.,~embeddings, for the trolls. Experiments on the "IRARussian Troll" dataset show that our methodology improves over thestate-of-the-art in the first scenario, while providing a compelling case forthe second scenario, which has not been explored in the literature thus far.

Predicting the Role of Political Trolls in Social Media