Human-Centeredness in Understanding and Detecting Online Harassment
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Kim, Seunghyun
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Abstract
Online harassment presents a pervasive and concerning issue, particularly impacting vulnerable groups like youth. However, prevailing detection systems often prioritize technical aspects, neglecting perspectives and experiences of the affected. Despite extensive research, these systems remain limited, necessitating more human-centered solutions. Recognizing online harassment's salient causes and its profound effects—such as psychological distress, reduced social support, and increase risk of mental health concerns—prioritizing victims' well-being through human-centered methods in understanding and detecting online harassment is crucial.
This dissertation focuses on key inquiries concerning human involvement in online harassment detection. These encompass the role of humans in automated detection, ground truth establishment, platform-based variations in detection, impact on victims, and vulnerable demographics. Leveraging unique datasets, especially from affected youth, including public peer-support interaction data, voluntarily-shared communications in private channels, and clinical records enrich the understanding of individual attributes affecting interactions and harassment on social media.
The dissertation offers many novel and critical insights. Having successfully assessed human-centric aspects in automated harassment detection algorithms, it has investigated existing harassment detection approaches, sources and semantics of ground truth annotations, dataset reliance, and connections between individual traits and harassment. Empirical analysis has compared harassment classifiers using varied victim-contributed and victim-distanced annotations, stressing the need to integrate stakeholders' experiences. The research has explored dataset biases across the public- and privateness of networked spaces, and has examined harassment’s broader mental health impact through a causal inference framework.
Importantly, the dissertation further unveils the relationship between mental health and harassment, focusing on contexts that increase or offset vulnerability to harassment. Focusing on linguistic and behavioral features from youth-contributed social media data and clinical records, the work has examined nuanced life circumstances influencing online experiences. Emphasizing contextual insights, it promises to guide tailored mental health support for affected individuals.
In summary, this thesis introduces a human-centered machine learning approach, enhancing harassment detection in ground truth establishment and dataset curation. It explores individual characteristics and online harassment experiences, offering insights into human, machine learning, and mental health dynamics. Advocating for a comprehensive approach considering diverse online experiences, it contributes to improving detection efficacy towards fostering a safer digital environment for all.
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Date
2024-07-27
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Dissertation