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Cross-lingual Perspectives about Crisis-Related Conversations on Twitter

Published: 13 May 2019 Publication History

Abstract

The role of social networks during natural disasters is becoming crucial to share relevant information and coordinate relief actions. With the reach of the social networks, any user around the world has the possibility of interact in crisis-events as these unfold. A large part of the information posted during a disaster uses the native language where the disaster occurred. However, there are also users from other parts of the world who can comment about the event, often in another language. In this work, we conducted a study of crisis-related tweets about the earthquake that occurred in Ecuador in April 2016. To that end, we introduce a new annotated dataset in both Spanish and English languages with approximately 8K tweets; half of them belong to conversations. We evaluate several neural architectures to identify crisis-related tweets in a multi-lingual setting, and we found that deep contextual multi-lingual embeddings outperform other strong baseline models. We then explore the type of conversations that occur from the perspective of different languages. The results show that certain types of conversations occur more in the native language and others in a foreign language. Conversations from foreign countries seek to gather situation awareness and give emotional support, while in the affected country the conversations aim mainly to humanitarian aid.

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Cited By

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  • (2024)Exploration of summary informativeness and topic richness through mining multilingual tweets: a study on turkey earthquake 2023Social Network Analysis and Mining10.1007/s13278-024-01386-814:1Online publication date: 5-Dec-2024
  • (2022)Automated Detection of Doxing on TwitterProceedings of the ACM on Human-Computer Interaction10.1145/35551676:CSCW2(1-24)Online publication date: 11-Nov-2022
  • (2022)GNoM: Graph Neural Network Enhanced Language Models for Disaster Related Multilingual Text ClassificationProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531561(55-65)Online publication date: 26-Jun-2022
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cover image ACM Other conferences
WWW '19: Companion Proceedings of The 2019 World Wide Web Conference
May 2019
1331 pages
ISBN:9781450366755
DOI:10.1145/3308560
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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Author Tags

  1. NLP
  2. Neural Networks
  3. Social Computing

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WWW '19
WWW '19: The Web Conference
May 13 - 17, 2019
San Francisco, USA

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Cited By

View all
  • (2024)Exploration of summary informativeness and topic richness through mining multilingual tweets: a study on turkey earthquake 2023Social Network Analysis and Mining10.1007/s13278-024-01386-814:1Online publication date: 5-Dec-2024
  • (2022)Automated Detection of Doxing on TwitterProceedings of the ACM on Human-Computer Interaction10.1145/35551676:CSCW2(1-24)Online publication date: 11-Nov-2022
  • (2022)GNoM: Graph Neural Network Enhanced Language Models for Disaster Related Multilingual Text ClassificationProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531561(55-65)Online publication date: 26-Jun-2022
  • (2021)Transfer Learning for the Multilingual and Multi-Domain Classification of Messages Relating to CrisesProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463272(2708-2708)Online publication date: 11-Jul-2021

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