Online social networks (OSNs) have emerged as the most convenient platforms for transmitting and communicating media, including news and electronic content. It is imperative to develop technology that can mitigate the spread of fake information/rumors, which badly harm society. This chapter employs an epidemic approach to develop a model for controlling and examining the dissemination of fake information on OSNs. The model is designed in the form of a system of fractional differential equations, exploring the real-world effects of misinformation propagation in OSNs with memory effect. It incorporates the concept of physics-informed neural networks with approximation based on the theory of functional connection and extreme learning machines. The proposed model elucidates the impact of various measures for correcting misinformation and shows how misinformation spreads across different groups. The validity of the suggested OSN model is confirmed through extensive computational analysis and investigation.
TopIntroduction
The advancement of internet technology has resulted in people receiving information more quickly in these days. Additionally, the methods of obtaining information have become more varied, with social media platforms like Twitter, WhatsApp, Instagram, Facebook, Google etc. becoming indispensable for the exchange of information (Wen et al., 2013). Nowadays, many users are interacted through online social networks (OSNs) and share their required information as well as personal information via data transfer at a low cost. However, data shared on OSN platforms may contain fake information that has an impact on people’s social life (Lebensztayn et al., 2011). Take COVID-19, for example, where the spread of false information about the Corona virus has made many individuals question any information they read about the infection on social media (Li et al., 2020). Recent false rumors of a COVID-19 treatment have been spreading on Facebook (Legon & Alsalman, 2020). People from all around the world have perished as a result of this sort of disinformation. In a similar way, Sommariva et al. (2018) examined how individuals are affected by incorrect information with regard to a well-known case study of the Zika virus. The writers found that bogus news frequently reaches a large audience on OSNs and spreads swiftly. One of the main issues with OSNs is verification, which involves the quantity of messages delivered and received as well as their legitimacy. A portion of the messages are spread through various social media platforms. Social media might have a disastrous effect on social harmony and peace. These messages, which are now called fake news, have the potential to be fatal. These messages are basically false information or rumors that are spread in a variety of ways (Sommariva et al., 2018), either maliciously or for amusement. Such information may cause unwarranted panic in the public and economic losses for governments (Banerjee, 1993; Dietz, 1967), as is now the case with COVID-19 spikes (Daley & Kendall, 1965). This might be attributed to the fact that OSN-related information spreads quickly and can instantly go worldwide (Dubey et al., 2020; Ren et al., 2017). On many occasions, the spread of false information over online social networks (OSNs) has had detrimental effects on society. On April 23, 2013, news propagate, two bombs detonated in the White House, injuring the US president and resulting $10 billion loss in economy (Wang et al., 2018). Another example comes from India, when a rumor on OSN stated that Sonam Gupta was disloyal. The personal life of a unknown girl named Sonam Gupta was touched as a result of this remark on social media. In a civilized society, such remarks should not be tolerated. This is a form of public humiliation for OSNs that, even if inadvertent, can have harmful repercussions. Dagher (2019) proposed a technique for blocking assaults on victims on Twitter to address these concerns. Basak et al. (2019) looked at the topic of rumor detection in microblogs. The researchers suggested a technique for detecting rumourmongers in microblogs. Their plan is based on consumers’ secret behavior. It reported incorrectly that the first patient treated with the vaccine had passed away, which had an impact on the COVID-19 medicine immunization research in the United Kingdom (Liang et al., 2015). According to references (Shu et al., 2019; Wu et al., 2013), rumors have a big impact on society. These facts increase people's vulnerability to becoming easy targets and increase their susceptibility to misleading information-related fear and sadness. Furthermore, they render terrible decisions based only on false information.
There are several mathematical models that examine the dynamics of the propogation of false information via online social media by employing the epidemic modeling technique. The vast scope and significance of social networks have made the identification of rumors and fake news a potentially important area of study. As a result, more varied mathematical models of rumour transmission are encouraged (Cannarella & Spechler, 2014; Shrivastava et al., 2020).