News Story

Best Paper Award at EKAW 2024 for groundbreaking work on capturing the political discourse in the news

Nancy Pontika, Tuesday 3 Dec 2024

A paper authored by KMi researchers Enrico Motta, Francesco Osborne, Angelo Salatino, and Iman Naja, and Martino Pulici from the Bosch Centre for Artificial Intelligence, has received the prestigious Best Research Paper Award at the 24th International Conference on Knowledge Engineering and Knowledge Management (EKAW 24). The EKAW series of conferences provides the premier European forum for research in Knowledge-Based Systems.

The paper, which is entitled Capturing the Viewpoint Dynamics in the News Domain, introduces an innovative approach to capturing the way the political debate around important topics is represented in news media. This is a very important issue, given that a healthy democracy requires a balanced news landscape, providing a fair account of the variety of political positions relevant to a particular issue. Indeed, in the past few years concerns about the lack of balance in the way the political debate is covered in UK’s news media have increased and this paper provides an innovative solution to analysing media coverage of the political discourse, allowing academics and practitioners to assess to what extent this media coverage provides a fair and balanced representation of the debate.

Technically, our solution employs a hybrid human-machine approach, which leverages Large Language Models (LLMs) to analyse news, combining human expertise with AI. It identifies the spectrum of viewpoints within a debate and classifies claims in a news corpus according to these perspectives, enabling a comprehensive understanding of the narratives that shape public discourse.

In an age of misinformation and polarisation, such tools are invaluable for journalists, researchers, and policymakers. Indeed, the proposed solution has the potential for widespread societal impact, by enabling not just researchers but also regulatory authorities, policy actors, media organisations, and a wide range of civil society stakeholders to better monitor media performance, identify potential harms and fine tune policy, potentially contributing to improving the health of our media ecosystem, and therefore of our democracy.

This research was supported by a grant from the OU’s Open Societal Challenges Programme — Project 192, Unlocking computational media research: Innovative AI technologies to assess fairness, balance and diversity in the media