ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
This addresses the challenge of online harassment impacting adolescent mental health, but it is incremental as it provides a new dataset rather than a novel method.
The paper tackles the problem of detecting toxic behavior among adolescents on social media by introducing ALONE, a multimodal dataset of toxic interactions between high school students on Twitter, which includes tweets, images, emoji, and metadata, and shows that context is crucial for accurate detection.
The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive explanation. Each instance of interaction includes tweets, images, emoji and related metadata. Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity.