Media bias and the intolerance of media outlets and citizens to deal with opposing points of
view pose a threat to the proper functioning of democratic processes. In this respect, we …

Unwanted and often harmful social biases are becoming ever more salient in NLP research,
affecting both models and datasets. In this work, we ask whether training on …

The way the media presents events can significantly affect public perception, which in turn
can alter people's beliefs and views. Media bias describes a one-sided or polarizing …

Models often easily learn biases present in the training data, and their predictions directly
reflect this bias. We analyze gender bias in dialogue data, and examine how this bias is …

Ideology is at the core of political science research. Yet, there still does not exist general-
purpose tools to characterize and predict ideology across different genres of text. To this …

Recent studies in the field of Machine Translation (MT) and Natural Language Processing
(NLP) have shown that existing models amplify biases observed in the training data. The …

Media coverage has a substantial effect on the public perception of events. Nevertheless,
media outlets are often biased. One way to bias news articles is by altering the word choice …

We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP
2024 conference co-located with ACL 2024. The shared task addresses bias and …

Media has a substantial impact on public perception of events, and, accordingly, the way
media presents events can potentially alter the beliefs and views of the public. One of the …

As polarization continues to rise among both the public and the news media, increasing
attention has been devoted to detecting media bias. Most recent work in the NLP community …