Is Stance Detection Topic-Independent and Cross-topic Generalizable? – A Reproduction Study

Myrthe Reuver, Suzan Verberne, Roser Morante, Antske Fokkens

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Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et al., 2019), and systematically analyze its reproducibility. Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context. We ask: To what extent is stance detection topic-independent and generalizable across topics? We compare the model’s performance on various unseen topics, and find topic (e.g. abortion, cloning), class (e.g. pro, con), and their interaction affecting the model’s performance. We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection.

Original languageEnglish
Title of host publicationProceedings of the 8th Workshop on Argument Mining [ArgMining 2021]
EditorsKhalid Al-Khatib, Yufang Hou, Manfred Stede
Place of PublicationPunta Cana, the Dominican Republic
PublisherAssociation for Computational Linguistics, ACL Anthology
Number of pages10
ISBN (Electronic)9781954085923
Publication statusPublished - Nov 2021
Event8th Workshop on Argument Mining, ArgMining 2021 - Virtual, Punta Cana, Dominican Republic
Duration: 10 Nov 202111 Nov 2021


Conference8th Workshop on Argument Mining, ArgMining 2021
Country/TerritoryDominican Republic
CityVirtual, Punta Cana

Bibliographical note

Funding Information:
This research is funded through Open Competition Digitalization Humanities and Social Science grant nr 406.D1.19.073 awarded by the Netherlands Organization of Scientific Research (NWO). Our computing was done through SURF Research Cloud, a national supercomputer infrastructure in the Netherlands also funded by the NWO. We would like to thank dr. Nils Reimers for sending us their paper’s data. We would also like to thank the anonymous reviewers, whose very helpful comments improved the paper. All opinions and remaining errors are our own.

Publisher Copyright:
© 2021 Association for Computational Linguistics.


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