Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Misinformation poses a critical societal challenge, and current approaches have yet to
produce an effective solution. We propose focusing on generalization, uncertainty, and how …

We examine the disconnect between scholarship and practice in applying machine learning
to trust and safety problems, using misinformation detection as a case study. We survey …

Research on automated social media rumour verification, the task of identifying the veracity
of questionable information circulating on social media, has yielded neural models …

Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust,
confusion, and anxiety that cause damage on our daily life judgments and public policy …

The spread of fake news can have devastating ramifications, and recent advancements to
neural fake news generators have made it challenging to understand how misinformation …

Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing
with politically sensitive content in a responsible, non-partisan, and safe behavior way is …

We present a human-in-the-loop evaluation framework for fact-checking novel
misinformation claims and identifying social media messages that support them. Our …

Most approaches for text encoding rely on the attention mechanism, at the core of the
transformers architecture and large language models. The understanding of this mechanism …

Media news framing bias can increase political polarization and undermine civil society. The
need for automatic mitigation methods is therefore growing. We propose a new task, a …