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Authorship Attribution for Online Social Media
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Author(s): Ritu Banga (Jaypee Institute of Information Technology, India), Akanksha Bhardwaj (Jaypee Institute of Information Technology, India), Sheng-Lung Peng (National Dong Hwa University, Taiwan)and Gulshan Shrivastava (National Institute of Technology Patna, India)
Copyright: 2018
Pages: 25
Source title:
Social Network Analytics for Contemporary Business Organizations
Source Author(s)/Editor(s): Himani Bansal (Jaypee Institute of Information Technology, India), Gulshan Shrivastava (National Institute of Technology Patna, India), Gia Nhu Nguyen (Duy Tan University, Vietnam)and Loredana-Mihaela Stanciu (University Timisoara, Romania)
DOI: 10.4018/978-1-5225-5097-6.ch008
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Abstract
This chapter gives a comprehensive knowledge of various machine learning classifiers to achieve authorship attribution (AA) on short texts, specifically tweets. The need for authorship identification is due to the increasing crime on the internet, which breach cyber ethics by raising the level of anonymity. AA of online messages has witnessed interest from many research communities. Many methods such as statistical and computational have been proposed by linguistics and researchers to identify an author from their writing style. Various ways of extracting and selecting features on the basis of dataset have been reviewed. The authors focused on n-grams features as they proved to be very effective in identifying the true author from a given list of known authors. The study has demonstrated that AA is achievable on the basis of selection criteria of features and methods in small texts and also proved the accuracy of analysis changes according to combination of features. The authors found character grams are good features for identifying the author but are not yet able to identify the author independently.
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