Browsing by Author "Sawhney, Nitin, Prof., Aalto University, Department of Computer Science, Finland"

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Asymmetric Conversational Strategies - Methods for Detecting Manipulative Online Trolling
(2024) Paakki, Henna
School of Science | Doctoral dissertation (article-based)
This dissertation examines harmful forms of deceptive manipulative online trolling from a socio-technical perspective. Adopting a transdisciplinary approach grounded in computational linguistics, the work utilizes qualitative digital Conversation Analysis (CA), statistical analysis, Machine Learning (ML) and Natural Language Processing (NLP) methodologies. The focus is on effective identification of conversational trolling: qualitatively defining its central characteristics and developing computational trolling detection based on operationalizable conversational features. Trolling strategies are increasingly used for online harassment and manipulation. This highlights the importance of developing efficient computational trolling detection to protect deliberative online discussion and democratic decision-making. The detection of deceptive behaviors like trolling is a challenging task that earlier research has not managed to accurately accomplish. This is due to the elusive and changing strategies employed by trolls to stir disruption. Approaches to computational trolling detection have traditionally investigated networks of bots, or the valence and content of alone-standing messages separated from their conversational context. However, these methods manage to capture only a fraction of harmful trolling behaviors. Computer-Mediated Communication research has shed light on the context-dependent and interactive nature of trolling, and common trolling styles found online. Based on these insights, this work investigates conversational trolling on several types of asynchronous online forums, collecting a novel dataset. This enables a qualitative depiction of turn-by-turn conversational strategies used in trolling, and their computational analysis. This work shows that while interest-based conversations commonly attract overt trolling styles, political and societal discussions are usually targeted with covert styles. However, they all utilize similar manipulative conversational strategies. These include the use of asymmetric responses (ignoring, challenging, and mismatching) that violate common conversational norms. This dissertation introduces a novel approach to computationally analyzing the dynamics of asynchronous conversations by drawing from digital CA. Together with NLP methods, this allows the extraction of conversational features like asymmetries to train ML models for trolling detection. The work presents a trolling detection model that surpasses earlier models in performance, and suggests a process for detecting deceptive manipulative online behaviors. Due to the challenges related to judging the intentionality behind online behaviors, I suggest an intent-agnostic detection approach based on observable behaviors in interaction. These include repeated violations of common conversational norms, which characterize deceptive manipulative behaviors like trolling.