The Impact of Key Ideas on Automatic Deception Detection in Text

Ángel Hernández Castañeda, Rene Arnulfo García Hernández, Yulia Ledeneva, Christian Eduardo Millán Hernández


In recent years, with the rise of the Internet, the automatic deception detection in text is an important task to recognize those of documents that try to make people believe in something false. Current studies in this field assume that the entire document containscues to identify deception; however, as demonstrated in this work, some irrelevant ideas in text could affect the performance of the classification. Therefore, this research proposes an approach for deception detectionin text that identifies, in the first instance, key ideas in a document based on a topic modeling algorith mand a proposed automatic extractive text summarization method, to produce a synthesized document that avoids secondary ideas. The experimental results of this study indicate that the proposed method outperform previous methods with standard collections.


Clustering algorithms, topic modeling, genetic algorithms, deep learning

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