Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators

Extensive efforts in automated approaches for content moderation have been focused on developing models to identify toxic, offensive, and hateful content -- with the aim of lightening the load for moderators. Yet, it remains uncertain whether improvements on those tasks truly address the needs that moderators have in accomplishing their work. In this paper, we surface the gaps between past research efforts that have aimed to provide automation for aspects of the content moderation task, and the needs of volunteer content moderators. To do so, we conduct a model review on Hugging Face to reveal the availability of models to cover various moderation rules and guidelines. We further put state-of-the-art LLMs to the test (GPT-4 and Llama-2), evaluating how well these models perform in flagging violations of platform rules. Overall, we observe a non-trivial gap, as missing developed models and LLMs exhibit low recall on a significant portion of the rules.