The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content
This addresses the problem of improving algorithmic moderation for social media platforms, but it is incremental as it builds on existing detection models.
The paper tackles the disconnect between social media platform policies that consider author intent in content moderation and current detection models that lack intent capture, proposing design changes to improve alignment with ethical and policy standards.
As social media has become a predominant mode of communication globally, the rise of abusive content threatens to undermine civil discourse. Recognizing the critical nature of this issue, a significant body of research has been dedicated to developing language models that can detect various types of online abuse, e.g., hate speech, cyberbullying. However, there exists a notable disconnect between platform policies, which often consider the author's intention as a criterion for content moderation, and the current capabilities of detection models, which typically lack efforts to capture intent. This paper examines the role of intent in content moderation systems. We review state of the art detection models and benchmark training datasets for online abuse to assess their awareness and ability to capture intent. We propose strategic changes to the design and development of automated detection and moderation systems to improve alignment with ethical and policy conceptualizations of abuse.