Identification of Rumor Sources in Social Network: Reverse Propagation Approach
Keywords:
Rumor, Rumor Source, Reverse Propagation, Source Estimation, Social NetworkAbstract
data from social networks becomes widely available; it can lead to the spread of rumors based on unconfirmed claims. People were frightened, anxious, and negatively affected by the rumors that circulated throughout the COVID-19 pandemic situation. In order to prevent or lessen the impact of rumor dissemination, social networks benefit from the capability to trace rumors back to their sources. Finding out where a rumor started in a social network is the main goal of this study's algorithm. Prior research mostly used the network partitioning method and each partition's head to identify several origins. Additionally, methods for detecting many sources and those for detecting a single source are distinct. In order to determine where rumors originated in the social network, the projected method first finds the intermediate rumor detectors and then uses back-propagation. A dataset consisting of real-life online social networks, such as Twitter and Facebook, is used for the experiment. Modern source identification techniques for both single and many rumor sources are used to test the suggested method. Previous research has shown a distance inaccuracy of 0 - 4 hops for a singular source and 0 - 6 for multiple sources. The results of the experiment demonstrate that in a real-life social network such as Facebook or Twitter, the true source may be found in 0 - 1 hops, while several sources in 0.5 - 2 hops. The experimental results show that the suggested procedures are superior to the current ones.
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