Senja Pollak & Boshko Koloski (Jožef Stefan Institute)
Computational analysis of news: methods and applications in keyword extraction, fake-news classification, sentiment and migrations discourse analysis
With the growing volume and influence of digital news media, computational methods for analysing news content have become essential. This talk addresses interconnected challenges spanning keyword extraction, misinformation detection, sentiment classification, and the study of migration discourse. For keyword extraction, we propose SEKE, a mixture-of-experts architecture that achieves state-of-the-art performance while offering interpretability through expert specialisation in distinct linguistic components. To combat misinformation, we demonstrate that ensembling heterogeneous representations from bag-of-words to knowledge graph-enriched neural embeddings substantially improves fake news classification. Extending beyond English, we develop zero-shot cross-lingual methods for both offensive language detection and news sentiment analysis, introducing novel training strategies that significantly outperform prior approaches for less-resourced languages. We apply these computational tools to the socially critical domain of migration discourse, analysing dehumanisation patterns and news framing in Slovene media coverage of Syrian and Ukrainian migrants — uncovering that while discourse has grown more negative over time, it is notably less dehumanising toward Ukrainian migrants. These contributions advance NLP methodology for news analysis while demonstrating its power to illuminate media narratives around pressing societal issues.