Contrasting the Spread of Misinformation in Online Social Networks

Abstract

Online social networks are nowadays one of the most effective and widespread tools used to share information. In addition to being employed by individuals for communicating with friends and acquaintances, and by brands for marketing and customer service purposes, they constitute a primary source of daily news for a significant number of users. Unfortunately, besides legit news, social networks also allow to effectively spread inaccurate or even entirely fabricated ones. Also due to sensationalist claims, misinformation can spread from the original sources to a large number of users in a very short time, with negative consequences that, in extreme cases, can even put at risk public safety or health.
 In this work we discuss and propose methods to limit the spread of misinformation over online social networks. The issue is split in two separate sub-problems. We first aim to identify the most probable sources of the misinformation among the subset of users that have been reached by it. In the second step, assuming to know the misinformation sources, we want to locate a minimum number of monitors (that is, entities able to identify and block false information) in the network in order to prevent that the misinformation campaign reaches some “critical” nodes while maintaining low the number of nodes exposed to the infection.
 For each of the two issues, we provide both heuristics and mixed integer programming formulations. To verify the quality and efficiency of our suggested solutions, we conduct experiments on several real-world networks. The results of this extensive experimental phase validate our heuristics as effective tools to contrast the spread of misinformation in online social networks.
 Regarding the source identification step, our approach showed success rates above 80% in most of the considered settings, and above 60% in almost all of them.
 With respect to the second issue, our heuristic proved to be able to obtain solutions that exceeded (in terms of number of required monitors) the ones obtained through our MILP-based approach of more than 20% in only few test scenarios. Our heuristics for both problems also proved to outperform significantly some previously proposed algorithms.

Online misinformation about climate change

“…The broader research into the diffusion of misinformation on social media can be broadly split into theoretical and empirical approaches. Many of the theoretical models are based on the epidemic (or contagion) model, which is a mathematical model of the spread of infectious diseases in a population, whereby an “infected” person can spread the information to a person who is “susceptible” through social connections, who in turn can then infect another person, possibly resulting in an epidemic (Amoruso, Anello, Auletta, & Ferraioli, 2017; Qiu, Oliveira, Sahami Shirazi, Flammini, & Menczer, 2017; Shah & Zaman, 2011; Törnberg, 2018; Webb et al, 2016). From a different perspective, Karlova and Fisher (2012) investigate the spread of misinformation in terms of human information behavior, and introduce a social diffusion model of misinformation, whereby misinformation is a product of a social process where it is formed, spread to others, judged and used, all against a backdrop of social, cultural, and historical factors.…”

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“…Problems relating to maximizing opinion diffusion in social networks have been extensively studied in the literature [3], [20], [21]. A primary task in such problems is to determine influential nodes, which has been an important research area in the multiagent systems community [22], [23], [24], [25], [26], [27]. Gionis, Terzi, and Tsaparas [1] study the problem of identifying such nodes whose positive opinions would maximize the overall positive opinion in the network.…”

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“…Online sociálne siete nielenže slúžia na komunikáciu s priateľmi a značkám na marketing, ale taktiež predstavujú primárny zdroj denných správ pre podstatný počet používateľov. Neumožňujú šírenie iba dôveryhodných správ ale aj vymyslených/nepodložených informácii, ktoré sa jednoducho dajú zdieľať obrovskému počtu používateľov naraz (Amoruso et al, 2020). Užívatelia sociálnych sietí sa tak stretávajú aj s veľkým množstvom článkov a príbehov popisujúcich vedecké výskumy a inovácie.…”