Capturing the Viewpoint Dynamics in the News Domain

Despite the seismic changes brought about by the web and social me- dia, mainstream news sources still play a crucial role in democratic societies. In particular, a healthy democracy requires a balanced and diverse media landscape, able to provide an arena in which the various topics and viewpoints relevant to the political discourse of the day are presented and discussed. Unfortunately, there is currently little effective computational support available to the various classes of users, who are interested in monitoring the topic and viewpoint dynam- ics in the news — e.g., for regulatory or research purposes. As a result, current analyses by researchers and practitioners tend to be small scale and, by and large, rely on manual investigations of topic and viewpoint coverage. To address this issue, we have developed a hybrid human-machine approach, which uses a Large Language Model (LLM) first to help analysts to identify the range of viewpoints relevant to the debate around a given topic, and then to classify the claims ex- pressed in the news corpus of interest with respect to the identified viewpoints. We tested a variety of LLMs on a benchmark corpus of news items drawn from British media sources. Our results indicate that GPT4o outperforms the other al- ternatives and can already provide effective support for this classification task, even when run in a zero-shot learning modality.