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Cross-lingual perspectives about crisis-related conversations on twitter

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.