Synews: a synergy-based rumor verification system

• Published in: Social network analysis and mining Vol. 14; no. 1; p. 57
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 15.03.2024
• Subjects: Acceptance; Connectedness; Crowdsourcing; Election results; Empirical analysis; False information; Gossip; Information sharing; Machine learning; Natural language processing; News; Ontology; Presidential elections; Propagation; Public relations; Reliability; Reputation management; Self-efficacy; Semantic web; Semantics; Simulation; Social networks; Synergy; Verification
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-024-01214-z

Online social networks (OSNs) are now implied as an important source of news and information besides establishing social connections. However, such information sharing is not always authentic because people, sometimes, also share their perceptions and fabricated information on OSNs. Thus, verification of online posts is important to maintain reliability over this useful communication medium. To address this concern, multiple approaches have been investigated including machine learning, natural language processing, source authentication, empirical studies, web semantics, and modeling/simulations, but the problem still persists. This research proposes an effective synergy-based rumor verification method along with a weighted-mean reputation management system to mitigate the spread of rumors over OSN. The model was formally verified through Colored Petri-Nets while its semantic behavior was analyzed through ontologies. Moreover, a third-party Facebook application was developed for proof of concept, and users’ acceptance and usability analysis was performed through Technology Acceptance Model and Self-Efficacy scale. The results indicate that the proposed approach can be used as an effective tool for the identification of rumors and it has the potential to improve the quality of users’ online experience.