Extracting Human Behaviour and Personality Traits from Social Media

Online social media has evolved as an integral part of human society. It facilitates collaboration and information flow, and has emerged as a crucial instrument for business and government organizations alike. Online platforms are being used extensively for work, entertainment, collaboration and communication. These positive aspects are, however, overshadowed by their shortcomings. With the constant evolution and expansion of social media platforms, a significant shift has occurred in the way some humans interact with others. Online social media platforms have inadvertently emerged as networking hubs for individuals exhibiting antisocial behaviour (ASB), putting vulnerable groups of people at risk. Online ASB is one of the most common personality disorders seen on these platforms, and is challenging to address due to its complexities. Human rights are the keystones of sturdy communities. Respect for these rights, based on the values of equality, dignity and appreciation, is vital and an integral part of strong societies. Every individual has a fundamental right to freely participate in all legal activities, including socializing in both the physical and online worlds. ASB, ranging from threatening, aggression, disregard for safety and failure to conform to lawful behaviour, deter such participation and must be dealt with accordingly. Online ASB is the manifestation of everyday sadism and violates the elementary rights (to which all individuals are entitled) of Its victims. Not only does it interfere with social participation, it also forces individuals into anxiety, depression and suicidal ideation. The consequences of online ASB for victims' and families' mental health are often far-reaching, severe and long-lasting, and can even create a social welfare burden. The behaviour can, not only inhibit constructive user participation with social media, it defies the sole purpose of these platforms: to facilitate communication and collaboration at scale. ASB needs to be detected and curtailed, encouraging fair user participation and preventing vulnerable groups of people from falling victim to such behaviour. Considering the large variety, high contribution speed and high volume of social media data, a manual approach to detecting and classifying online ASB is not a feasible option. Furthermore, a traditional approach based on a pre-defined lexicon and rule-based feature engineering may still fall short of capturing the subtle and latent features of the diverse and enormous volume of social media data. State-of-the-art deep learning, which is a sub-field of machine learning, has produced astonishing results in numerous text classification undertakings, and has outperformed the aforementioned techniques. However, given the complexity associated with implementing deep learning algorithms and their relatively recent development, models based on the technology have significantly been under-utilized when working with online behaviour studies. Specifically, no prior study has undertaken the task of fine-grained and user- generated social media content classification related to online ASB utilizing the deep learning technology. This thesis introduces a novel three-part framework, based on deep learning, with the objectives of: i) Detecting behaviour and personality traits from online platforms; (ii) Binary detection of online antisocial behaviour and (iii) Multiclass antisocial behaviour detection from social media corpora. A high accuracy classification model is presented proceeded by extensive experimentation with different machine learning and deep learning algorithms, fine tuning of hyper- parameters, and using different feature extraction techniques. Disparate behaviour and personality traits, including ASB and its four variants are detected with a significantly high accuracy from online social media platforms. Along the way, three medium-sized gold standard benchmark data set have been constructed. The proposed approach is seminal and offers a step towards efficient and effective methods of online ASB prevention. The approach and the findings within this thesis are significant and crucial as these lay the groundwork for detecting and eliminating all types of undesirable and unacceptable social behaviour traits from online platforms.