Social Behavioral Biometrics using Personality Traits-aware Tweet Embedding
Date
2021-07-21
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Abstract
User recognition in online social networks has emerged as an important problem in the domain of social behavioral biometrics and social media forensics. In this thesis, the linguo-stylistic and semantic analysis of textual data used to predict a user's personality traits information is combined with the graph structure of social interaction to build a social behavioral biometric user recognition system. A Deep Neural Network (DNN) is first trained for the task of personality traits classification using only the textual content from online social network (OSN) profiles. Next, a novel weighted graph representation scheme is proposed to encode social interactions within OSNs, incorporating information regarding the psychological similarity between interacting users. Finally, a Graph Neural Network (GNN) is trained for the task of closed-set user recognition, and the utility of the proposed system is evaluated on two different datasets demonstrating its superiority to state-of-the-art methods aimed at user recognition.
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Keywords
Social Behavioral Biometrics, Personality Traits, Closed-set User Identification, User Similarity, Tweet Embedding, Social Network Analysis
Citation
Karkekoppa Narayanaswamy, P. K. (2021). Social Behavioral Biometrics using Personality Traits-aware Tweet Embedding (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.