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Harnessing Synergy in Context and Emoji for Joint Detection of Harmful Online Content in Multi-turn Conversations

Detecting harmful content, such as cyberbullying, self-harm, and grooming, in self-generated content or conversations is an emerging research area with significant potential for positive social impact. However, challenges such as the scarcity of real-world conversational data, labor-intensive annotation processes, and inconsistent content policies hinder understanding and evaluating the performance of harmful content detection systems. In this study, we utilize openly available forum data to construct conversation proxies, facilitating the analysis and detection of harmful content. We undertook extensive efforts to label the conversational data using a consistent content policy developed by experts, with ten annotators contributing to the labeling process. Our experiments investigated the impact of context window size and found that performance in joint detection improved gradually up to a context window of 16 sentences, after which performance plateaued. Additionally, experiments with emojis demonstrated that using a tokenizer capable of decoding emojis yielded the best performance, while either removing emojis or converting them to text resulted in inferior outcomes.