An Entropy Based Method to Control Covid-19 Rumors in Online Social Networks Using Opinion Leaders

47 Pages Posted: 25 Apr 2022

See all articles by Lokesh Jain

Lokesh Jain

affiliation not provided to SSRN

Abstract

In the ongoing Covid-19 pandemic, people spread various COVID-19 related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to control Covid-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel R eputation-based O pinion L eader I dentification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user’s reputation. The empirical reputation is utilized to calculate the user’s trust, the post’s entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach’s rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate the influence of OL is exceptionally significant in controlling COVID-19 rumors.

Keywords: COVID-19 rumors, Opinion leader, Online social network, Entropy, Reputation, Health misinformation.

Suggested Citation

Jain, Lokesh, An Entropy Based Method to Control Covid-19 Rumors in Online Social Networks Using Opinion Leaders. Available at SSRN: https://ssrn.com/abstract=4068295 or http://dx.doi.org/10.2139/ssrn.4068295

Lokesh Jain (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
26
Abstract Views
144
PlumX Metrics