Abstract:
Online social networks have attracted billions of active users over the past decade.
These systems play an integral role in the everyday life of many people around the
world. As such, these platforms are also attractive for misinformation, hoaxes, and fake
news campaigns which usually utilize social trolls and/or social bots for propagation.
Detection of so-called social trolls in these platforms is challenging due to their large
scale and dynamic nature where users’ data are generated and collected at the scale of
multi-billion records per hour. In this paper, we focus on fckle trolls, i.e., a special type
of trolling activity in which the trolls change their identity frequently to maximize their
social relations. This kind of trolling activity may become irritating for the users and
also may pose a serious threat to their privacy. To the best of our knowledge, this is the
frst work that introduces mechanisms to detect these trolls. In particular, we discuss
and analyze troll detection mechanisms on diferent scales. We prove that the order of
centralized single-machine detection algorithm is O(n3) which is slow and impractical
for early troll detection in large-scale social platforms comprising of billions of users.
We also prove that the streaming approach where data is gradually fed to the system
is not practical in many real-world scenarios. In light of such shortcomings, we then
propose a massively parallel detection approach. Rigorous evaluations confrm that
our proposed method is at least six times faster compared to conventional parallel
approaches.