Connecting the Dots in News Analysis: A Cross-Disciplinary Survey of Media Bias and Framing

Anonymous

TL;DR: This survey paper explores existing data sets, methodologies, and evaluation techniques for Media Bias and Framing, highlighting disconnects and opportunities to bridge the gap between NLP and social sciences in addressing these issues.

Abstract:

The manifestation and effect of bias in news reporting have been central topics in the social sciences for decades, and have received increasing attention in the NLP community recently.
While NLP can help to scale up analyses or contribute automatic procedures to investigate the impact of biased news in society, we argue that methodologies that are currently dominant fall short of capturing the complex questions and effects addressed in theoretical media studies. This is problematic because it diminishes the validity and safety of the resulting tools and applications.
Here, we review and critically compare task formulations, methods and evaluation schemes in the social sciences and NLP. We discuss open questions and suggest possible directions to close identified gaps between theory and predictive models, and their evaluation. These include model transparency, considering document-external information, and cross-document reasoning.

Paper Type: long

Research Area: Computational Social Science and Cultural Analytics

Contribution Types: Surveys

Languages Studied: Mostly US English