Abstract
The social media usage among the internet users has been increasing constantly, producing user generated logical data's such as textual posts and images through different sources. Everyday people express their immediate thoughts, emotions and beliefs by writing, posting and sharing content online which is viewable by user's online social network. Digital footprints serve as a key tool to trace the online activities, communication, and actions left behind as a result of extensive use of internet. Henceforth, the data of the user is collected in real time to analyse his state of mental behaviour based on certain parameters which would forecast his stage of addiction towards the internet world. We analyse five different traits namely openness to experiences, conscientiousness, extraversion, agreeableness and neuroticism. The digital footprints collected are thus processed to extract features to be employed in predictive model. Calculating the mean predictive value of the digital footprints lead to the required outcome. Meta-analyses of the acquired data were compared with the traits to bring out the predictive results.
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