Information Abstraction from Crises Related Tweets Using Recurrent Neural Network

Abstract : Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.441-452, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_38〉
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Mehdi Ben Lazreg, Morten Goodwin, Ole-Christoffer Granmo. Information Abstraction from Crises Related Tweets Using Recurrent Neural Network. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.441-452, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_38〉. 〈hal-01557644〉

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