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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.

Islamic extremism is the dominant security concern of many contemporary governments, spanning the industrialized West to the developing world.Narrative Landminesexplores how rumors fit into and extend narrative systems and ideologies, particularly in the context of terrorism, counter-terrorism, and extremist insurgencies. Its concern is to foster a more sophisticated understanding of how oral and digital cultures work alongside economic, diplomatic, and cultural factors that influence the struggles between states and non-state actors in the proverbial battle of hearts and minds. Beyond face-to-face communication, the authors also address the role of new and social media in the creation and spread of rumors. As narrative forms, rumors are suitable to a wide range of political expression, from citizens, insurgents, and governments alike, and in places as distinct as Singapore, Iraq, and Indonesia-the case studies presented for analysis. The authors make a compelling argument for understanding rumors in these contexts as \"narrative IEDs,\" low-cost, low-tech weapons that can successfully counter such elaborate and expansive government initiatives as outreach campaigns or strategic communication efforts. While not exactly the same as the advanced technological systems or Improvised Explosive Devices to which they are metaphorically related, narrative IEDs nevertheless operate as weapons that can aid the extremist cause.

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.

Misinformation such as fake news and rumors is a serious threat for information ecosystems and public trust. The emergence of large language models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double‐edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emerging question is: can we utilize LLMs to combat misinformation? On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: how to combat LLM‐generated misinformation? In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions, respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM‐generated misinformation.

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 .

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.

PurposeDealing with online rumors or fake information on social media is growing in importance. Most academic research on online rumors has approached the issue from a quantitative modeling perspective. Less attention has been paid to the psychological mechanisms accounting for online rumor transmission behavior on the individual level. Drawing from the theory of stimulus–organism–response, this study aims to explore the nature of online rumors and investigate how the informational characteristics of online rumors are processed through the mediation of psychological variables to promote online rumor forwarding.Design/methodology/approachAn experimental approach to this issue was taken; the researchers investigated how the informational characteristics of online rumors and the psychological mediators promote online rumor transmission.FindingsFour information characteristics (sense-making, funniness, dreadfulness and personal relevance) and three psychological motivators (fact-finding, relationship enhancement and self-enhancement) promote online rumor-forwarding behavior.Originality/valueBecause any online rumor transmitted on social media can go viral, companies may eventually encounter social media-driven crises. Thus, understanding what drives rumor-forwarding behavior can help marketers mitigate and counter online rumors.

PurposeThe spread of rumors on social media has caused increasing concerns about an under-informed or even misinformed public when it comes to scientific issues. However, researchers have rarely investigated their diffusion in non-western contexts. This study aims to systematically examine the content and network structure of rumor-related discussions around genetically modified organisms (GMOs) on Chinese social media.Design/methodology/approachThis study identified 21,837 rumor-related posts of GMOs on Weibo, one of China's most popular social media platforms. An approach combining social network analysis and content analysis was employed to classify user attitudes toward rumors, measure the level of homophily of their attitudes and examine the nature of their interactions.FindingsThough a certain level of homophily existed in the interaction networks, referring to the observed echo chamber effect, Weibo also served as a public forum for GMO discussions in which cross-cutting ties between communities existed. A considerable amount of interactions emerged between the pro- and anti-GMO camps, and most of them involved providing or requesting information, which could mitigate the likelihood of opinion polarization. Moreover, this study revealed the declining role of traditional opinion leaders and pointed toward the need for alternative strategies for efficient fact-checking.Originality/valueIn general, the findings of this study suggested that microblogging platforms such as Weibo can function as public forums for discussing GMOs that expose users to ideologically cross-cutting viewpoints. This study stands to provide important insights into the viral processes of scientific rumors on social media.

Building an effective rumor propagation model is the key to blocking rumor propagation. This article is based on the quantum superposition theory, analyzing the propagation decision of unknown individuals from the perspectives of consciousness and behavior, determining their time-varying propagation state, characterizing the propagation process of online rumors, and constructing a rumor propagation model to solve the basic reproduction number. It analyzes the local stability of equilibrium points without rumor propagation and equilibrium points with rumor propagation. The experimental results better fit the rule of change of rumor propagation group state, and verify the effects of the average rumor belief level in society and the degree of social management of rumors on the scale of rumor propagation, providing theoretical support for suppressing rumor propagation.

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.