About: Mining Text Patterns over Fake and Real Tweets

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Mining Text Patterns over Fake and Real Tweets
Creator	Diaz-Garcia, Jose Dolores Ruiz, M Fernandez-Basso, Carlos Martin-Bautista, Maria
Source	PMC
abstract	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.
has issue date	2020-05-15(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50143-3_51
has license	no-cc
sha1sum (hex)	073f7241bf01664dc8b59471c82c55acce1c6ecd
schema:url	https://doi.org/10.1007/978-3-030-50143-3_51
resource representing a document's title	Mining Text Patterns over Fake and Real Tweets
has PubMed Central identifier	PMC7274655
schema:publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems
resource representing a document's body	covid:073f7241bf01664dc8b59471c82c55acce1c6ecd#body_text
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