The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
SemEval-2016 Task 6: Detecting Stance in Tweets
Open accessAbstract
Here for the first time we present a shared task on detecting stance from tweets: given a tweet and a target entity (person, organization, etc.), automatic natural language systems must determine whether the tweeter is in favor of the given target, against the given target, or whether neither inference is likely.The target of interest may or may not be referred to in the tweet, and it may or may not be the target of opinion.Two tasks are proposed.Task A is a traditional supervised classification task where 70% of the annotated data for a target is used as training and the rest for testing.For Task B, we use as test data all of the instances for a new target (not used in task A) and no training data is provided.Our shared task received submissions from 19 teams for Task A and from 9 teams for Task B. The highest classification F-score obtained was 67.82 for Task A and 56.28 for Task B. However, systems found it markedly more difficult to infer stance towards the target of interest from tweets that express opinion towards another entity.
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Stance and Sentiment in Tweets
Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3003433 · Cited by 449
Self-citationMohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
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Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2023) · arXiv · doi:10.48550/arxiv.2305.03514 · Cited by 839
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585
Stance and Sentiment in Tweets
Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3003433 · Cited by 449
Self-citationMohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
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Metadata
- Authors
- Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., & Cherry, C.
- Venue
- Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
- Published
- 2016
- Pages
- 31-41
- OpenAlex
- W2460159515
- Cited by
- 844
- References
- 2
- FWCI
- 47.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., & Cherry, C. (2016). SemEval-2016 Task 6: Detecting Stance in Tweets. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). https://doi.org/10.18653/v1/s16-1003
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