Data-driven and machine learning based approaches for detecting, categorising and
measuring abusive content such as hate speech and harassment have gained traction due …

Mitigating bias in algorithmic systems is a critical issue drawing attention across
communities within the information and computer sciences. Given the complexity of the …

Social media sites use content moderation to attempt to cultivate safe spaces with accurate
information for their users. However, content moderation decisions may not be applied …

Current large-scale language models can be politically biased as a result of the data they
are trained on, potentially causing serious problems when they are deployed in real-world …

Social media users create folk theories to help explain how elements of social media
operate. Marginalized social media users face disproportionate content moderation and …

Purpose Accurate information is the basis for well-informed decision-making, which is
particularly challenging in the dynamic reality of a pandemic. Search engines are a major …

The proliferation of online toxicity, characterized by offensive and disrespectful language,
has been a pervasive issue in Indonesia's digital environment, impacting users' mental …

ISTORICALLY, societies have always established norms of coexistence that impose limits
on individual rights. This was the case with the fundamental right to freedom of expression …

Shadow banning consists for an online social net-work in limiting the visibility of some of its
users, without them being aware of it. Twitter declares that it does not use such a practice …

We present a data set consisting of German news articles labeled for political bias on a five-
point scale in a semi-supervised way. While earlier work on hyperpartisan news detection …