Exposing Paid Opinion Manipulation Trolls

Abstract

Recently, Web forums have been invaded by opinion manipulation trolls. Some trolls try to influence the other users driven by their own convictions, while in other cases they can be organized and paid, e.g., by a political party or a PR agency that gives them specific instructions what to write. Finding paid trolls automatically using machine learning is a hard task, as there is no enough training data to train a classifier; yet some test data is possible to obtain, as these trolls are sometimes caught and widely exposed. In this paper, we solve the training data problem by assuming that a user who is called a troll by several different people is likely to be such, and one who has never been called a troll is unlikely to be such. We compare the profiles of (i) paid trolls vs. (ii)"mentioned" trolls vs. (iii) non-trolls, and we further show that a classifier trained to distinguish (ii) from (iii) does quite well also at telling apart (i) from (iii).

opinion manipulation trolls, trolls, opinion manipulation, community forums, news media

References in corpus (3)

• Finding Deceptive Opinion Spam by Any Stretch of the Imagination
• Feature-Rich Part-of-speech Tagging for Morphologically Complex Languages: Application to Bulgarian
• Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields

Cited by in corpus (8)

• A Survey on Predicting the Factuality and the Bias of News Media
• MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
• Can We Spot the "Fake News" Before It Was Even Written?
• SemEval-2017 Task 3: Community Question Answering
• In Search of Credible News
• Hunting for Troll Comments in News Community Forums
• SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
• SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings