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Published December 11, 2017 | Version v1
Conference paper Open

A Language-agnostic Approach to Exact Informative Tweets during Emergency Situations

  • 1. Politecnico di Torino
  • 2. Istituto Superiore Mario Boella


In this paper, we propose a machine learning approach to automatically classify non-informative and informative contents shared on Twitter during disasters caused by natural hazards. In particular, we leverage on previously sampled and labeled datasets of messages posted on Twitter during or in the aftermath of natural disasters. Starting from results obtained in previous studies, we propose a language-agnostic model. We define a base feature set considering only Twitter-specific metadata of each tweet, using classification results from this set as a reference. We introduce an additional feature, called the Source Feature, which is computed considering the device or platform used to post a tweet, and we evaluate its contribution in improving the classifier accuracy.


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I-REACT – Improving Resilience to Emergencies through Advanced Cyber Technologies 700256
European Commission