Recognizing Social Cues in Crisis Situations

Di Wang, Yuan Zhuang, Ellen Riloff, Marina Kogan

Abstract
During crisis situations, observations of other people’s behaviors often play an essential role in a person’s decision-making. For example, a person might evacuate before a hurricane only if everyone else in the neighborhood does so. Conversely, a person might stay if no one else is leaving. Such observations are called social cues. Social cues are important for understanding people’s response to crises, so recognizing them can help inform the decisions of government officials and emergency responders. In this paper, we propose the first NLP task to categorize social cues in social media posts during crisis situations. We introduce a manually annotated dataset of 6,000 tweets, labeled with respect to eight social cue categories. We also present experimental results of several classification models, which show that some types of social cues can be recognized reasonably well, but overall this task is challenging for NLP systems. We further present error analyses to identify specific types of mistakes and promising directions for future research on this task.
Anthology ID:
2024.lrec-main.1194
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
13677–13687
Language:
URL:
https://aclanthology.org/2024.lrec-main.1194
DOI:
Bibkey:
Cite (ACL):
Di Wang, Yuan Zhuang, Ellen Riloff, and Marina Kogan. 2024. Recognizing Social Cues in Crisis Situations. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13677–13687, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Recognizing Social Cues in Crisis Situations (Wang et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2024.lrec-main.1194.pdf
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