IEEE Technology and Society Magazine - September 2020 - 53

1
A fake profile created using the information of a real user to deceive other
users of the network.
2
A group of fake accounts controlled by a bot or human for malicious purpose.

SEPTEMBER 2020

∕

publishing a number of articles. Figure 1 presents the
trend of research publications per year during 2011-
2019. It includes all articles containing the word
socialbot or its variants3 either in title or in abstract.
The growth rate of papers over the year shows the
severity of the problem. Existing literature reports
massive misuse of socialbots in political astroturfing,
rumor diffusion, and spamming [2], [4]. A comprehensive analysis and study of socialbots is important from
both service providers and users' perspectives. In this
direction, Ferrara et al. [5] described both benign and
malicious socialbots, and discussed anecdotes of
malicious socialbots in political astroturfing, stock
market manipulation, personal information theft, and
misinformation diffusion. They also proposed a grouping of the existing socialbot detection approaches into
four categories.
In line to Ferrara et al. [5], we present a different taxonomy of the existing socialbot detection approaches
and group them into five categories. In addition to categorization, we also present a characterization of socialbots from different perspectives. Socialbots operate
like Web bots, an old phenomenon since the Turing
days of computers. Starting with the technological and
operational differences between the two variants of
bots, we present some analytical observations from a
socialbots injection experiment performed on Twitter
to perceive the potential of socialbots infiltration. Further, we categorize the areas that are under threat from
socialbots, along with a brief description of each threat
category. We also discuss enabling factors and challenges in defending against malicious socialbots and
group them into three categories: platform-, user-, and
detection-related challenges. Finally, we discuss the
trends of current socialbot research along with a brief
description of very recent studies analyzing the role of

Number of Publications

nline Social Networks (OSNs) are
the modern communication media
at the peak of Internet technology
that facilitate users' ability to connect with friends and celebrities,
and to disseminate breaking news
and updates on real-life events.
Connections and interactions among OSN users generate a massive amount of data containing a rich collection of knowledge that could be useful to various realworld data modeling and predictive analytics problems
like open-source intelligence, business intelligence,
event prediction, and product recommendations. On the
Web, adversaries explore and target products and services for misuse and vulnerabilities, and OSNs are no
exception. Their large user base, easy-to-use functionality, and open nature further attract antisocial elements
to these OSNs. In OSNs, cybercrimes and illicit activities
are generally committed using various forms of fake
profiles, such as clone profiles,1 sybil,2 and bots.
In OSNs, creating fake profiles is easy, but their
manual handling is neither economically feasible nor
scalable. Therefore, adversaries automate fake profiles, which are known as socialbots, which are given
different nomenclature like political bots, spambots,
clickbots, and cyborgs depending on their end misuse. Socialbots are sophisticated and advanced
threat entities from adversaries. They are programcontrolled OSN profiles that imitate human behavior
to camouflage their identity and project themselves
as human beings.
Socialbots are programmed to perform the required
OSN functionalities. In the initial stage of injection on
an OSN, socialbots behave like normal users; this is
called the reputation building process. Once the reputation is built, socialbots abuse it to perform illicit
activities of their masters. Socialbots do not have any
direct repercussions like service disruptions, but have
indirect consequences, such as online protest orchestration [1], electoral campaign distortion [2], terrorism
funding and recruitment [3], and content pollution [4]
that are damaging. Therefore, characterization and
detection of socialbots and other threat entities are
essential for the healthy growth of OSN platforms. To
design an effective socialbot prevention and detection
mechanism, analysis of their ecosystem, operating
characteristics, temporal evolution, and infiltration
impacts are of significant importance.
Recently, researchers have started analyzing the
socialbots problem from different perspectives,

50
40
30
20
10
0
2011 2012 2013 2014 2015 2016 2017 2018 2019
Year

FIGURE 1. Year-wise trend of research publications containing
the term socialbot and its variants (source: Google Scholar).
3
social bot, socialbots, social bots, socialbotnet, socialbotnets, social botnet, and social botnets.

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

IEEE Technology and Society Magazine - September 2020

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - September 2020