6533b851fe1ef96bd12a9604
RESEARCH PRODUCT
Information Abstraction from Crises Related Tweets Using Recurrent Neural Network
Mehdi Ben LazregOle-christopher GranmoMorten Goodwinsubject
Computer science02 engineering and technologyCrisis managementcomputer.software_genreData scienceTask (project management)World Wide WebInformation extractionRecurrent neural network020204 information systems0202 electrical engineering electronic engineering information engineeringText generation020201 artificial intelligence & image processingInformation abstractionSocial mediaOpen communicationcomputerdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |