Beginning with exploring the way for rumor free social networks. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Ahmad, Tameem; Ahmed, Sayyed Usman; Ali, Syed Omar; Khan, Rifa
• Is Part Of: Journal of statistics & management systems. Volume 23:Issue 2(2020)
• Page Start: 231
• Page End: 238
• Publication Date: 2020-02-17
• Subjects: 68-04; Social network -- Rumor detection -- Backtracking the source -- Recovering the network; Statistics -- Periodicals; Mathematical models -- Periodicals; Mathematical models; Statistics; Periodicals; 519.5
• DOI: 10.1080/09720510.2020.1724623
• Languages: English
• ISSNs: 0972-0510

Abstract

Abstract: In the modern context, social media platforms provide a lucrative avenue for the intermingling of millions of digital avatars at any given time. The ingenuity of such systems has enabled the promulgation of data generated from a vast number of disparate and multifarious sources. Though the proliferation of social media platforms has been an unprecedented boon for the spread of ideas, it is far from an unqualified success. In such a complex and evolving ecosystem, it is patently obvious that the flow of data, specifically the authenticity of the data that is shared, cannot be vetted in real-time. As a consequence, the antithesis is true, that is to say, disinformation is as rampant as information is widespread. We qualify such disingenuous data as "rumors". In an effort to mitigate the spread of "rumors" various heuristic models have been incorporated into codebase of public use websites. The modus operandi of most social media websites is to employ the use of authentic data to stifle the propagation of misinformation - here a heuristic model is implemented to contrast the various distinct features of a known "rumor" and information that is known to be true. The 'rumor', once identified, is flagged for posterity, and the originator of the fraudulent post is penalized for misconduct, defamation, etc.