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 …

Toxicity detection algorithms, originally designed with reactive content moderation in mind,
are increasingly being deployed into proactive end-user interventions to moderate content …

Content moderation is the process of flagging content based on pre-defined platform rules.
There has been a growing need for AI moderators to safeguard users as well as protect the …

Computational social science research has made advances in machine learning and natural
language processing that support content moderators in detecting harmful content. These …

Machine learning (ML) is widely used to moderate online content. Despite its scalability
relative to human moderation, the use of ML introduces unique challenges to content …

Over recent years, the stakes and complexity of online content moderation have been
steadily raised, swelling from concerns about personal conflict in smaller communities to …

Online platforms face the challenge of moderating an ever-increasing volume of content,
including harmful hate speech. In the absence of clear legal definitions and a lack of …

Content moderation is a critical service performed by a variety of people on social media,
protecting users from offensive or harmful content by reviewing and removing either the …

Content moderators play a key role in keeping the conversation on social media healthy.
While the high volume of content they need to judge represents a bottleneck to the …

Hateful content online is a concern for social media platforms, policymakers, and the public.
This has led high-profile content platforms, such as Facebook, to adopt algorithmic content …