Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience.
In this thesis, I intend to investigate the possible relationship between users' Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and to show what the third parties can reveal about their private traits from what they share and act on different social networking platforms.
In the second part of the thesis, the variation in personality development has been studied within a cross-platform environment such as Facebook and Twitter. I have also compared the results of the constructed personality profiles in these social platforms to evaluate the effect of the used platforms on one user's personality development. Also, a personality continuity and stability analysis was performed using samples from social media platforms. The implemented experiments were based on ten-year longitudinal samples aiming to understand users' long-term personality development to open the chance for potential cooperation between psychology and big data worlds.