So, one mechanism that allows the rapid spread of rumors on social networks is that small-degree nodes quickly learn the rumor of an informed neighbor and then quickly forward it to all other neighbors. Some centers with many connections share with many people with few connections. Rumors have always existed, but now they are flourishing in the age of social media. While mainly produced and consumed by users, platforms are also part of the problem.
However, research yields conflicting evidence about the user's ability to distinguish between true and false rumors. The findings indicate that exchanges between related users can increase the likelihood of trusted agents transmitting rumor messages, which may promote the spread of useful information and corrective posts. We support this finding by analyzing information dissemination in the mathematically defined preferential connection network (PA) topology, a common model for real-world networks, which demonstrates that sublogarithmic time is sufficient to spread news to all nodes of a network. A key observation in the mathematical demonstration, besides being a good explanation for this phenomenon, is that small-degree nodes learn a rumor once one of their neighbors knows it, and then quickly forward it to their neighbors.
The rumor messages created by members of a population of numerable agents constitute the basic construction of the simulation model. Hybrid communication structures that allow agents to learn about a rumor both through interactions between other personally known persons and from sources of public information that obscure the credibility of message creators could therefore disrupt the chain of accountability between agents. related and change the proportion of biases and impartial actors in the inferred transmission path. This implies that, while false rumors would be effectively blocked, impartial agents would not support true rumors or corrective posts.
The first is network density, which describes the probability with which agents activate their relational links with others to learn about a rumor; it is below 10% in scenarios 1 to 4, on average, while scenario 5 is characterized by an average density of around 23%. Therefore, by relying on their relational connections with others they know are trustworthy, users can safely convey true rumors and rectifications. The transmission of rumors along agents' relational ties allows impartial agents to communicate reliable messages to each other. However, lower homophily values imply that the way impartial agents learn about a rumor doesn't make much of a difference.
Wang B, Zhuang J (201) Response to rumors, discrediting response and decision making of poorly informed Twitter users during disasters. This study has referred to actors who might be willing to spread false rumors of desires or preferences that are not related to the truth as biased agents. While it has been argued that SMN allows its users to identify and challenge false rumors through collective efforts to make sense of unverified information, a process commonly referred to as self-correcting evidence suggests that users often fail to distinguish between rumors before they have done so. resolved.
The analyses are specifically based on those parameter configurations for which the computational model can reproduce empirically observed patterns of rumor conversations. Karahanna E, Xu SX, Xu Y, Zhang N (201) The perspective of the needs-affordable-features for the use of social networks. .