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Stance Detection on Social Media: State of the Art and Trends

Paper · 2020 · Abeer AlDayel and Walid Magdy

Summary

This survey maps research on stance detection in social media, meaning the task of working out whether a user supports or opposes a target such as a topic, a person or a claim. The authors draw on work from NLP, web science and computational social science. They separate stance from sentiment and argue that using sentiment as a stand-in for support gives weaker results, because the two dimensions are largely independent. The review sorts prior work in several ways. One is whether the stance is stated in a post (detection) or inferred for a user who never stated it (prediction). Another is the kind of target: a single topic, several related targets, or a claim. It also compares feature types (textual, network, contextual) and learning methods (supervised, transfer, unsupervised), and it reports state-of-the-art scores on benchmark datasets. The main finding is that network and interaction features usually beat content-only features, and supervised SVM models remain the strongest performers across datasets. This matters for any team measuring public opinion online, including work on fake news and rumours.

Key points

  • Publication counts from Web of Science (2008–2020) rise sharply after 2016. The authors credit this to SemEval 2016 Task 6, the first stance benchmark built from Twitter data, covering topics such as atheism, feminism and politicians.
  • The SemEval 2016 dataset averages only about 500 training tweets per topic. Some of its tweets have since been deleted, so later studies can no longer collect the users' network data.
  • There are three target settings: target-specific, multi-related-targets (for example Clinton vs. Trump) and claim-based. Claim-based stance, as in RumourEval and headline-to-article matching, often serves as a first step in fake-news detection.
  • Topic-based transfer learning still scores below in-topic supervised training. The authors still consider it promising as a route to a general stance classifier that needs no new labelled data.
  • The authors' proposed directions are larger benchmarks in more domains and more languages, network-aware models that cost less to compute, and stronger protection of user privacy.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of EdinburghEdinburgh · United Kingdom381 works · 80,747 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 1,035 works

Cite

Cite as Abeer AlDayel and Walid Magdy. 2020. Stance Detection on Social Media: State of the Art and Trends. arXiv:2006.03644 · Abeer ALDayel, Walid Magdy, Stance detection on social media: State of the art and trends, Information Processing & Management, Volume 58, Issue 4, 2021, 102,597, ISSN 0306-4573.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1016/j.ipm.2021.102597 · arXiv 2006.03644

@misc{aldayel2020stance,
  title = {{Stance Detection on Social Media: State of the Art and Trends}},
  author = {Abeer AlDayel and Walid Magdy},
  year = {2020},
  doi = {10.1016/j.ipm.2021.102597},
  eprint = {2006.03644},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1016/j.ipm.2021.102597}
}

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