Can We Trust Social Media Data?

Abstract

The size of a social media account's audience -in terms of followers or friends count -is believed to be a good measure of its influence and popularity. To gain quick artificial popularity on online social networks (OSN), one can buy likes, follows and views, from social media fraud (SMF) services. SMF is the generation of likes, follows and views on OSN such as Facebook, Twitter, YouTube, and Instagram. Using a research method that combines computer sciences and social sciences, this paper provides a deeper understanding of the illicit market for SMF. It conducts a market price analysis for SMF, describes the operations of a supplier -an Internet of things (IoT) botnet performing SMF -and provides a profile of the potential customers of such fraud. The paper explains how an IoT botnet conducts social network manipulation and illustrates that the fraud is driven by OSN users, mainly entertainers, small online shops and private users. It also illustrates that OSN strategy to suspend fake accounts only cleans the networks a posteriori of the fraud and does not deter the crime -the botnet -or the fraud -SMFfrom happening. Several solutions to deter the fraud are provided. CCS ConceptsHuman-centered computing → Collaborative and social computing → Collaborative and social computing theory, concepts and paradigms → Social media • Security and privacy → Intrusion/anomaly detection and malware mitigation → Malware and its mitigation • → Human and societal aspects of security and privacy

The role of bots in U.S. Real estate development online communication

“…The risks associated with social media use are well explored in the digital communications literature. One of the primary arguments within this literature is that the nature of social media platforms (cheap, accessible, live, and limited moderation) makes them vulnerable to manipulation (Paquet-Clouston et al, 2017;Wald et al, 2013;Woolley & Howard, 2018). As a result, users of The Role of Bots in U.S. Real Estate Development Online Communication such platforms may easily misrepresent themselves and key ideas, and intentionally influence public discourse around major political or social issues.…”

Visual media analysis for Instagram and other online platforms

“…The comparative display of political media feeds has become prevalent in the past few years, given concern with filter bubbles (Pariser, 2011a) (see Figure 17), or homophilic recommendation systems (Chun, 2018), especially Facebook’s news feed. The ‘Blue Feed, Red Feed’ project by the Wall Street Journal (2016–2019) interactively displayed the stories published by conservative and progressive media sources on Facebook, refreshed hourly (Keegan, 2016).…”