A feature selection method for author identification in interactive communications based on supervised learning and language typicality
View/ Open
Bibliography Export




Statistics
View Usage StatisticsFull record
Show full item recordDate
2016-11Keywords
Authorship identification
Natural Language Processing
Supervised learning
Feature selection
Impersonation
Role identification
Abstract
Authorship attribution, conceived as the identification of the origin of a text between different authors, has been a very active area of research in the scientific community mainly supported by advances in Natural Language Processing (NLP), machine learning and Computational Intelligence. This paradigm has been mostly addressed from a literary perspective, aiming at identifying the stylometric features and writeprints which unequivocally typify the writer patterns and allow their unique identification. On the other hand, the upsurge of social networking platforms and interactive messaging have undoubtedly made the anonymous expression of feelings, the sharing of experiences and social relationships much easier than in other traditional communication media. Unfortunately, the popularity of such communities and the virtual identification of their users deploy a rich substrate for cybercrimes against unsuspecting victims and other forms of illegal uses of social networks that call for the ...
Type
article