Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

Human personality traits are the key drivers behind our decisionmaking, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called "PERS" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction. CCS CONCEPTS• Computing methodologies → Machine learning; • Applied computing → Psychology.

Human personality traits are key drivers behind our decision making, influencing our lives on a daily basis. Inference of personality traits, such as the Myers-Briggs personality type, as well as an understanding of dependencies between personality traits and user behavior on various social media platforms, is of crucial importance to modern research and industry applications such as recommender systems. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse; the impact of different social network data on profiling performance and of personality traits on applications such as recommender systems is yet to be evaluated. Furthermore, large-scale datasets are also lacking in the research community. To fill these gaps, in this work we develop a novel multi-view fusion framework PERS that infers Myers-Briggs personality type indicators. We evaluate the results not just across data modalities but also across different social networks, and also evaluate the impact of inferred personality traits on recommender systems. Our experimental results demonstrate that PERS is able to learn from multi-view data for personality profiling by efficiently leveraging highly varied data from diverse social multimedia sources. Furthermore, we demonstrate that inferred personality traits can be beneficial to other industry applications. Among other results, we show that people tend to reveal multiple facets of their personality in different social media avenues. We also release a social multimedia dataset in order to facilitate further research on this direction.

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