Understanding Psycholinguistic Behavior of predominant drunk texters in Social Media

TL;DR

This paper explores how social media data, specifically Twitter, can be used to identify and analyze drunk texters based on their linguistic and behavioral patterns, achieving high classification accuracy.

Contribution

It introduces a novel approach to classify drunk texters versus non-drunk texters using psycholinguistic features with high accuracy, aiding health research and content moderation.

Findings

Achieved 96.78% accuracy in classifying drunk texters.

Identified distinct psycholinguistic features of drunk texters.

Potential applications in addiction prevention and content moderation.

Abstract

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.