In the last decade, social media has evolved as one of the leading platform to create, share, or exchange information; it is commonly used as a way for individuals to maintain social connections. In this online digital world, people use to post texts or pictures to express their views socially and create user-user engagement through discussions and conversations. Thus, social media has established itself to bear signals relating to human behavior. One can easily design user characteristic network by scraping through someone's social media profiles. In this paper, we investigate the potential of social media in characterizing and understanding predominant drunk texters from the perspective of their social, psychological and linguistic behavior as evident from the content generated by them. Our research aims to analyze the behavior of drunk texters on social media and to contrast this with non-drunk texters. We use Twitter social media to obtain the set of drunk texters and non-drunk texters and show that we can classify users into these two respective sets using various psycholinguistic features with an overall average accuracy of 96.78% with very high precision and recall. Note that such an automatic classification can have far-reaching impact - (i) on health research related to addiction prevention and control, and (ii) in eliminating abusive and vulgar contents from Twitter, borne by the tweets of drunk texters.

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IEEE Symposium on Computers and Communications

Maity, S.K, Mullick, A, Ghosh, S, Kumar, A, Dhamnani, S, Bahety, S, & Mukherjee, A. (2018). Understanding psycholinguistic behavior of predominant drunk texters in social media. In IEEE Symposium on Computers and Communications. doi:10.1109/ISCC.2018.8538637