Extrapolation of Online News Spread on Social Media Riktesh Srivastava* * Associate Professor, MBA, City University, Ajman, UAE. Email: rsrivastava@cu.ac.ae Abstract Social media’s infuence on news dissemination has grown as it has gained in popularity. The news spreads more quickly on social media than it does on conventional media because it is so accessible and provides easy interaction. This study envisions the ongoing dialogue between experienced and ignorant users. As more people use social media, they are more likely to receive news that affects their views. Despite this, social media makes it simple for people to get a range of news stories from many sources. It is true that utilising social media to distribute news has its pros and cons, but it is important to take into account how things function there. Six hypotheses are used in the study’s mathematical derivation of the rate of change of news diffusion. It has been discovered that when an ‘informed’ user shares or likes the news, the rate at which it spreads on social media may be mathematically extrapolated. The diffcult part is fguring out how long it will take to reach the maximum populace. The research runs a thorough simulation analysis to determine when news will reach the people. The fndings could provide some insightful information about how news spreads on social media. Keywords: Informed Users, Non-Informed Users, Pandemic Model, Social Media, Mathematical Modelling Introducton We cannot ignore the datum that social media has become a dominant tool for online news distribution and consumption (Shearer & Matsa, 2018). Astonishingly, even newspapers are now using social media to spread the news faster (Kümpel et al., 2015). One of the main reasons International Journal of Business Analytics and Intelligence 10 (2) 2022, 20-24 http://publishingindia.com/ijbai/ for the news spreading on social media is that users can segment and share it simultaneously (Pourghomi et al., 2017). Therefore, it is not surprising that approximately 64.5% of people get their news through social media (Lee & Ma, 2012; Martin, 2018). In addition, the impression that negative news travels faster on social media (Kumar & Shah, 2018; Shin et al., 2018) was found irrelevant, as Berger & Milkman (2013), Kümpel et al. (2015), and Lee & Ma (2012) researched that the critical reason for news spread on social media is informativeness. There are several studies performed on news spread on social media through different mathematical models. Dhar et al. (2016) and Jin et al. (2013) researched, identifying the rumors through epidemic models. The frst categorised the news as original or a rumor and then evaluated the news spread rate. Davoudi and Chatterjee (2016) performed the research on identifying the quality of information spread through ordinary differential equations. More and Lingam (2019) used two parameters, structural characteristics, content analysis, greedy algorithm, and an SI (Suspected and Infected) epidemic model to analyse the news spread. Campan et al. (2017) deliberated that news spread depends on three factors – infuence maximisation, information diffusion, and epidemiological modelling. Budak et al. (2011) discovered that the news spread through the NP-hard problem through a greedy algorithm. In our study, we performed the news spread considering the continuous interaction between the informed, I(t) and the uninformed, NI(t), users. As this interaction is dynamic, it is paramount to identify the rate of change of interactions ()  to reach the maximum population, n, nεN. The interaction is defned in Fig. 1.