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 …

The internet has become a central medium through which 'networked publics' express their
opinions and engage in debate. Offensive comments and personal attacks can inhibit …

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 …

In this paper, we present new empirical evidence to demonstrate the near impossibility for
existing machine learning content moderation methods to keep pace with, let alone stay …

Millions of users are active on several social media platforms such as Twitter, Facebook, and
Google. Due to this, there are bound to be polarizing views of people on certain topics …

Technology companies play a major role in governing the internet. The rules of platforms,
content hosts, search engines, and telecommunications providers govern how we interact …

We describe the current content moderation strategy employed by Meta to remove policy-
violating content from its platforms. Meta relies on both handcrafted and learned risk models …

Contemporary policy debates about managing the enormous volume of online content have
taken a renewed focus on upload filtering, automated detection of potentially illegal content …

Content moderation, the AI-human hybrid process of removing (toxic) content from social
media to promote community health, has attracted increasing attention from lawmakers due …

Social media platforms struggle to protect users from harmful content through content
moderation. These platforms have recently leveraged machine learning models to cope with …