NeMig - A Bilingual News Collection and Knowledge Graph about Migration

ews recommendation plays a critical role in shaping the public’s worldviews through the way in which it filters and disseminates
information about different topics. Given the crucial impact that media plays in opinion formation, especially for sensitive topics,
understanding the effects of personalized recommendation beyond accuracy has become essential in today’s digital society. In this work,
we present NeMig, a bilingual news collection on the topic of migration, and corresponding rich user data. In comparison to existing
news recommendation datasets, which comprise a large variety of monolingual news, NeMig covers articles on a single controversial
topic, published in both Germany and the US. We annotate the sentiment polarization of the articles and the political leanings of the
media outlets, in addition to extracting subtopics and named entities disambiguated through Wikidata. These features can be used to
analyze the effects of algorithmic news curation beyond accuracy-based performance, such as recommender biases and the creation of
filter bubbles. We construct domain-specific knowledge graphs from the news text and metadata, thus encoding knowledge-level
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connections between articles. Importantly, while existing datasets include only click behavior, we collect user socio-demographic
and political information in addition to explicit click feedback. We demonstrate the utility of NeMig through experiments on the
tasks of news recommenders benchmarking, analysis of biases in recommenders, and news trends analysis. NeMig aims to provide a
useful resource for the news recommendation community and to foster interdisciplinary research into the multidimensional effects of algorithmic news curation.