As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper We define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions.
Preprint version Neurocomputing, Spring 2021.
© The Authors, 2021.
Sheth, A., Shalin, V. L., & Kursuncu, U. (2021, April). Defining and detecting toxicity on social media: Context and knowledge are key. Neurocomputing.
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