A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets
Ryan Sequeira, Avijit Gayen, Niloy Ganguly, Sourav Kumar Dandapat and, Joydeep Chandra

TL;DR
This large-scale study analyzes how prescription drug abuse tweets spread across Twitter's follower network, revealing dense connectivity, long-distance cascades, and collective user behavior that complicate efforts to curb such messages.
Contribution
It provides the first large-scale analysis of prescription drug abuse tweet dissemination, highlighting network structures and user engagement patterns that facilitate spread.
Findings
99% of users form a giant connected component
Long-distance cascades involve multiple user groups
Engagement with certain drugs is instantaneous and increases with exposure
Abstract
In this article, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify DA, to characterize the spreading of DA 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 the cascades of DA 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 nonfringe nodes that work together toward such spread, thus potentially complicating the process of arresting such cascades.…
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