Statistical Relational Learning Towards Modelling Social Media Users.

1 Motivation Nowadays web users actively generate content on different social media platforms. The large number of users requiring personalized services creates a unique opportunity for researchers to explore user modelling. To distinguish users, recognizing their attributes such as personality, age and gender is essential. To this end, substantial research has been done by utilizing user generated content to recognize user attributes by applying different classification or regression techniques. Among other things, we have inferred the personality traits of Facebook users based on their status updates using three different classifiers [Farnadi et al., 2013]. But as we concluded in [Farnadi et al., 2014b], user attributes are not isolated, as emotions expressed in Facebook status updates are related to age, gender and personality of the authors. Using multivariate regression or multi-target classification techniques is one approach to leverage these dependencies in the user attribute learning process. For example, to employ the dependencies between different personality traits, we applied five multivariate regression techniques to infer the personality of YouTube vloggers [Farnadi et al., 2014c]. The above mentioned techniques are powerful types of machine learning approaches, however they only partially model social media users. Users communicate directly as friends, or indirectly, by liking others content. These types of interaction between users are a valuable source but modelling them with the traditional machine learning approaches is challenging. Furthermore, user generated content in social media comes in different modalities such as visual and textual content, whereas different pieces of evidence might contradict each other. Moreover, in extracting features from each source, a reasonable amount of noise is expected. Hence, using the extracted features as a single feature space without considering features’ noise, dependencies or conflicts, reduces the quality of the learning process. To overcome these limitations, we introduce a new statistical relational learning (SRL) framework [Getoor and Taskar, 2007] suitable for modelling social media users, which we call PSL Q [Farnadi et al., 2014a].