Date on Master's Thesis/Doctoral Dissertation

5-2014

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Computer Engineering and Computer Science

Committee Chair

Yampolskiy, Roman Vladimirovich

Committee Co-Chair (if applicable)

Elmaghraby, Adel S.

Committee Member

Imam, Ibrahim N.

Committee Member

Chang, Dar-Jen

Committee Member

Hardin, Charles Timothy

Author's Keywords

Authorship identification; Chat bots; Stylometry; Text mining; Behavioral drift; Biometrics; N-Gram; TFIDF; TF-ITF; BLN-Gram-TF-ITF; Text similarity; Quality of Writing

Subject

Authorship--Data processing; Computer crimes--Investigation; Data mining

Abstract

Authorship identification is a technique used to identify the author of an unclaimed document, by attempting to find traits that will match those of the original author. Authorship identification has a great potential for applications in forensics. It can also be used in identifying chat bots, a form of intelligent software created to mimic the human conversations, by their unique style. The online criminal community is utilizing chat bots as a new way to steal private information and commit fraud and identity theft. The need for identifying chat bots by their style is becoming essential to overcome the danger of online criminal activities. Researchers realized the need to advance the understanding of chat bots and design programs to prevent criminal activities, whether it was an identity theft or even a terrorist threat. The more research work to advance chat bots’ ability to perceive humans, the more duties needed to be followed to confront those threats by the research community. This research went further by trying to study whether chat bots have behavioral drift. Studying text for Stylometry has been the goal for many researchers who have experimented many features and combinations of features in their experiments. A novel feature has been proposed that represented Term Frequency Inverse Document Frequency (TFIDF) and implemented that on a Byte level N-Gram. Term Frequency-Inverse Token Frequency (TF-ITF) used these terms and created the feature. The initial experiments utilizing collected data demonstrated the feasibility of this approach. Additional versions of the feature were created and tested for authorship identification. Results demonstrated that the feature was successfully used to identify authors of text, and additional experiments showed that the feature is language independent. The feature successfully identified authors of a German text. Furthermore, the feature was used in text similarities on a book level and a paragraph level. Finally, a selective combination of features was used to classify text that ranges from kindergarten level to scientific researches and novels. The feature combination measured the Quality of Writing (QoW) and the complexity of text, which were the first step to correlate that with the author’s IQ as a future goal.

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