A Large Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets

Authors: Ryan Sequeira, Avijit Gayen, Niloy Ganguly, Sourav Kumar Dandapat and Joydeep Chandra

Abstract: In this paper, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify drug abuse, to characterize the spreading of drug abuse tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that cascades of drug abuse tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected non-fringe nodes that work together towards such spread, thus potentially complicating the process of arresting such cascades. Further, we discovered that the engagement of the users with respect to certain drugs like Vicodin, Percocet and OxyContin, that were observed to be most mentioned in Twitter, is instantaneous. On the other hand for drugs like Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.

Publishing Date: September, 2019

Published in: IEEE Transactions on Computational Social Systems