1 research outputs found
A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets
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. Furthermore,
we discovered that the engagement of the users with respect to certain drugs,
such as Vicodin, Percocet, and OxyContin, that were observed to be most
mentioned in Twitter is instantaneous. On the other hand, for drugs, such as
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.Comment: 13 pages, 9 figures, and accepted by IEEE Transactions on
Computational Social System