Integrating Large Language Models and Knowledge Graphs to Capture Political Viewpoints in News Media
This work addresses the need for balanced media analysis in democratic societies, but it is incremental as it builds on prior pipeline improvements.
The paper tackles the problem of classifying political viewpoints in news media by improving a pipeline with fine-tuned Large Language Models and knowledge graph enrichments, achieving the best results through their integration on a UK immigration debate benchmark.
News sources play a central role in democratic societies by shaping political and social discourse through specific topics, viewpoints and voices. Understanding these dynamics is essential for assessing whether the media landscape offers a balanced and fair account of public debate. In earlier work, we introduced a pipeline that, given a news corpus, i) uses a hybrid human-machine approach to identify the range of viewpoints expressed about a given topic, and ii) classifies relevant claims with respect to the identified viewpoints, defined as sets of semantically and ideologically congruent claims (e.g., positions arguing that immigration positively impacts the UK economy). In this paper, we improve this pipeline by i) fine-tuning Large Language Models (LLMs) for viewpoint classification and ii) enriching claim representations with semantic descriptions of relevant actors drawn from Wikidata. We evaluate our approach against alternative solutions on a benchmark centred on the UK immigration debate. Results show that while both mechanisms independently improve classification performance, their integration yields the best results, particularly when using LLMs capable of processing long inputs.