Users in social networks are often encouraged to complete their profile by providing personal attributes such as age, gender, interest, income, etc. Additionally, users are likely to join interest-based groups that are devoted to various topics: "2016 University Graduates", "Accordions Singapore", etc. These profiles and groups are often used as a basis for rendering better online services, marketing, and advertisement. However, in practice, the majority of users are reluctant to provide actual personal attributes, while the group participation is often relevant to their friendship connections, rather than interests. As a solution, user profiling at the individual and group level is explored to mine the demography and mobility information of a user. In this paper, we discuss different user profiling approaches on social networks, highlight the challenges, techniques, and future trends. We explain the weakness and strength of these methods and introduce an analytic platform to bridge the gap between social media users, business intelligence and the Big Data.
Nowadays, video games play a very important role in human life and no longer purely associated with escapism or entertainment. In fact, gaming has become an essential part of our daily routines, which give rise to the exponential growth of various online game platforms. By participating in such platforms, individuals generate a multitude of game data points, which, for example, can be further used for automatic user profiling and recommendation applications. However, the literature on automatic learning from the game data is relatively sparse, which had inspired us to tackle the problem of player profiling in this first preliminary study. Specifically, in this work, we approach the task of player gender prediction based on various types of game data. Our initial experimental results inspire further research on user profiling in the game domain.
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