Projects per year
Abstract
Disruptive behavior is a prevalent threat to constructive online engagement. Covert behaviors, like trolling, are especially challenging to detect automatically, because they utilize deceptive strategies to manipulate conversation. We illustrate a novel approach to their detection: analyzing conversational structures instead of focusing only on messages in isolation. Building on conversation analysis, we demonstrate that 1) conversational actions and their norms provide concepts for a deeper understanding of covert disruption, and that 2) machine learning, natural language processing and structural analysis of conversation can complement message-level features to create models that surpass earlier approaches to trolling detection. Our models, developed for detecting overt (aggression) as well as covert (trolling) behaviors using prior studies’ message-level features and new conversational action features, achieved high accuracies (0.90 and 0.92, respectively). The findings offer a theoretically grounded approach to computationally analyzing social media interaction, and novel methods for effectively detecting covert disruptive conversations online.
Original language | English |
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Journal | ACM Transactions on Computer - Human Interaction |
Volume | 31 |
Issue number | 2 |
ISSN | 1073-0516 |
DOIs | |
Publication status | Published - 2024 |
MoE publication type | A1 Journal article-refereed |
Fields of Science
- 113 Computer and information sciences
Projects
- 1 Finished
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Automated trolling and fake news generation in future social media: Computational and empirical investigations of the threat and its implications
Salovaara, A. (Project manager), Vepsäläinen, H. (Participant), Zafar, B. (Participant), Paakki, H. (Participant) & Ukkonen, A. (Project manager)
01/01/2019 → 31/12/2022
Project: Research project