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"Rumor."
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A survey on rumor detection and prevention in social media using deep learning
In the current digital era, massive amounts of unreliable, purposefully misleading material, such as texts and images, are being shared widely on various web platforms to deceive the reader. Most of us use social media sites to exchange or obtain information. This opens a lot of space for false information, like fake news, rumors, etc., to spread that could harm a society’s social fabric, a person’s reputation, or the legitimacy of a whole country. Therefore, preventing the transmission of such dangerous material across platforms is a digital priority. However, the main goal of this survey paper is to thoroughly examine several current state-of-the-art research works on rumor control (detection and prevention) that use deep learning-based techniques and to identify major distinctions between these research efforts. The comparison results are intended to identify research gaps and challenges for rumor detection, tracking, and combating. This survey of the literature makes a significant contribution by highlighting several cutting-edge deep learning-based models for rumor detection in social media and critically evaluating their effectiveness on recently available standard datasets. Furthermore, to have a thorough grasp of rumor prevention to spread, we also looked into various pertinent approaches, including rumor veracity classification, stance classification, tracking, and combating. We also have created a summary of recent datasets with all the necessary information and analysis. Finally, as part of this survey, we have identified some of the potential research gaps and challenges that need to be addressed in order to develop early, effective methods of rumor control.
Journal Article
Rumor spreading model with a focus on educational impact and optimal control
Rumor spreading brings great misconception and harm to society. To control the spread of rumors, it is essential to model rumor propagation and provide appropriate interference in inhibiting the propagation. In this paper, we establish an extended rumor-spreading model with a focus on the influence of knowledge education and intervention strategies in reducing rumor propagation. The mathematical rationality of the proposed model is examined, which demonstrates the existence of equilibrium and local asymptotic stability. To simulate the dynamics of rumor spreading in the proposed model and calibrate its unknown variables to a real case, we employ a novel rumor-informed neural network (RINN), which is constructed based on the physics-informed neural network (PINN) and real rumor spreading. The numerical simulation experiments indicate that the reinforcement of education on rumor identification and timely refutation of false information is effective in controlling the propagation of rumors. Moreover, the optimal control strategies are further proposed to determine the efficient means of mitigating the risk associated with the rapid spread of rumors. Our findings present that proactive dissemination of publicity and educational content can effectively enhance individuals’ awareness of rumor information. Specifically, prompt dispelling of false information can result in a higher success rate of dispelling rumors, a shorter duration of rumor dissemination, and a lower peak in the number of rumor disseminators, thereby facilitating effective control of the spread of rumors.
Journal Article
What James said
A little girl ignores her best friend James after she hears rumors that he has been talking about her, but soon realizes that she misses his friendship.
Book
A model to measure the spread power of rumors
With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories “importance” (28 features) and “ambiguity” (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors.
Journal Article
Heidi Heckelbeck and the big mix-up
An embarrassing rumor about Lucy spreads at school and she thinks Bruce is behind it so Heidi gets involved and uses some magic to bring the trio of friends back together.
Book
Bayesian inference and ant colony optimization for multi-rumor mitigation in online social networks
With the increasing popularity of social media, Online Social Networks (OSNs) are being used for promoting or discrediting various products or persons. As such, rumors are spread in the networks to increase or decrease the popularity of the target. Limiting the spread of rumors is an important research problem. In a promotion or smear campaign, we see multiple rumors about the target. Many existing works have explored rumor propagation and mitigation in social networks for a single rumor. However, users become biased towards the topic due to multiple rumors about it. A user influenced by the previous rumors about a topic is more likely to believe a rumor with similar content. Therefore, in this work, we analyze the spread of multiple rumors about a topic and formulate an optimization problem to identify the top
k
rumor spreaders. A Bayesian Inference has been applied to model the user bias caused by multiple rumors based on rumor content and user opinion about the topic. An Adaptive Ant Colony Optimization algorithm has been proposed to determine the top
k
rumor spreaders, who may be isolated from the network to reduce the impact of the rumors in the OSN. The efficacy of the proposed approaches is shown through experimentation on two datasets by considering the budget
k
.
Journal Article
The confessional
\"Jenny Nguyen moves to a Wisconsin high school and, hoping to fit in, she posts a made-up story about a romance with a teacher on secret message board The Confessional\"-- Provided by publisher.
Book
Examining the antecedents of everyday rumor retransmission
PurposeThis study investigates factors that motivate social media users to retransmit rumors. We focus on everyday rumors rather than catastrophic rumors and develop a model of everyday rumor retransmission based on the uses and gratification theory, the rumor retransmission model, and the basic law of rumor.Design/methodology/approachAn Internet survey is conducted to collect data and test the proposed model. This study’s hypotheses are tested through partial least squares regression analysis.FindingsThe results show that socializing, information seeking and status seeking increase the intention to retransmit rumors. Perceived rumor credibility has a moderating effect on the impacts of socializing and status seeking on retransmission intention.Originality/valueOur research model provides a theoretical foundation for future studies that want to explore motivations or values that determine rumor-sharing intention on social media. The findings can help government agencies and businesses to manage rumor retransmission on social media.
Journal Article