Argument Mining in Tweets: Comparing Crowd and Expert Annotations for Automated Claim and Evidence Detection
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This moderate level of agreement is similar to other tasks aiming to identify links between two texts and points to the difficulty of the task similar to annotation scenarios in argumentation mining.
“This moderate level of agreement is similar to other tasks aiming to identify links between two texts (e.g.implicit linking in Kuznetsov et al. (2022)) and point to the difficulty of the task similar to annotation scenarios in argumentation mining (Lawrence and Reed, 2020;Iskender et al., 2021).”
From STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond (2025) · cites this paper as Iskender et al., 2021
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond
Dycke, N., Zečević, M., Kuznetsov, I., Suess, B., Kersting, K., & Gurevych, I. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.1107 · Cited by 1 · see citing works
- Authors
- Iskender, N., Schaefer, R., Polzehl, T., & Möller, S.
- Affiliations
- Technische Universität Berlin (DE)
- University of Potsdam (DE)
- German Research Centre for Artificial Intelligence (DE)
- Corresponding author
- Iskender, Neslihan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 275-288
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3175973996
- Cited by
- 9
- References
- 1
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 1 · 2022: 2 · 2021: 1
- FWCI
- 0.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Arts and Humanities › Philosophy › Linguistics and Discourse Analysis
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- argument mining, tweets, crowdsourcing, expert annotation, claim detection, social media discourse, climate change discourse, sequence labeling, supervised classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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