Mining Text Patterns over Fake and Real Tweets
| Autor: | Jose Angel Diaz-Garcia, M. Dolores Ruiz, Maria J. Martin-Bautista, Carlos Fernandez-Basso |
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| Rok vydání: | 2020 |
| Předmět: |
Information retrieval
Presidential election Association rule learning Computer science ComputingMilieux_LEGALASPECTSOFCOMPUTING 02 engineering and technology Test (assessment) Biology and political orientation Social media mining 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Fake news InformationSystems_MISCELLANEOUS |
| Zdroj: | Information Processing and Management of Uncertainty in Knowledge-Based Systems ISBN: 9783030501426 IPMU (2) |
| DOI: | 10.1007/978-3-030-50143-3_51 |
| Popis: | With the exponential growth of users and user-generated content present on online social networks, fake news and its detection have become a major problem. Through these, smear campaigns can be generated, aimed for example at trying to change the political orientation of some people. Twitter has become one of the main spreaders of fake news in the network. Therefore, in this paper, we present a solution based on Text Mining that tries to find which text patterns are related to tweets that refer to fake news and which patterns in the tweets are related to true news. To test and validate the results, the system faces a pre-labelled dataset of fake and real tweets during the U.S. presidential election in 2016. In terms of results interesting patterns are obtained that relate the size and subtle changes of the real news to create fake news. Finally, different ways to visualize the results are provided. |
| Databáze: | OpenAIRE |
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