The outbreak of the coronavirus disease (COVID-19) pandemic, a significant health threat, influenced information-related behaviors and induced increased rumor-sharing behaviors on social media. ...Fighting COVID-19 thus entails the need to fight the rumors as well, providing a strong motivation to explore rumor-related behavior during this extraordinary period. From the perspective of information acquisition, we predicted that information acquisition from social and traditional media would interactively influence rumor-related decisions (i.e., rumor belief and sharing) and that critical thinking would shape this relationship. Through a survey of 2424 individuals who used social media during the pandemic, we found that information acquisition from social media was negatively related to rumor sharing and that rumor belief mediated this relationship. Meanwhile, information acquisition from traditional media weakened the negative effect of information acquisition from social media on rumor belief, and critical thinking alleviated the positive effect of rumor belief on rumor sharing. This study contributes to the literature by explaining the diffusion of COVID-19 rumors on social media from an information perspective and revealing how different information sources and thinking styles come into conflict in rumor decisions.
•Information acquisition from social media (IASM) hinders rumor sharing (RS).•Rumor belief (RB) mediates this relationship between IASM and RS.•Information acquisition from traditional media (IATM) weakens the effect of IASM on RB.•Critical thinking (CT) alleviates the positive effect of RB on RS.
As online rumors have the potential to greatly affect areas such as social order, stock prices, and presidential elections, there is an emerging necessity for the automation of rumor verification. ...Although the current methods have achieved satisfactory performance, they still suffer from the following problems. First, the current methods simply concatenate the representations of different subthreads in their models, which may result in omitting some important information. Second, although stance information has been considered for the rumor verification task, it has not been fully utilized. To solve the problems, we propose the Subthreads Stance–Rumor Interaction Network (SSRI-Net) model for rumor verification. The proposed SSRI-Net model first introduces the Subthreads Interaction Attention mechanism between different subthreads to capture the interaction information between subthreads for a better understanding on user posts. Moreover, we also design the Stance–Rumor Interaction Network to fully integrate users’ stance information with rumor verification. We have conducted experiments on two public datasets, namely SemEval-2017 and PHEME datasets, for performance evaluation. Our SSRI-Net model outperforms the previous best models by 5.8% and 7.1% in Macro-F1 and Accuracy respectively on the SemEval-2017 dataset. In addition, our SSRI-Net model also outperforms the previous best models by 4.7% and 5.4% in Macro-F1 and Accuracy respectively on the PHEME dataset. The experimental results have shown that our proposed SSRI-Net model has outperformed the baseline models and achieved the state-of-the-art performance for rumor verification.
Although studies have investigated cyber-rumoring previous to the pandemic, little research has been undertaken to study rumors and rumor-corrections during the COVID-19 (coronavirus disease 2019) ...pandemic. Drawing on prior studies about how online stories become viral, this study will fill that gap by investigating the retransmission of COVID-19 rumors and corrective messages on Sina Weibo, the largest and most popular microblogging site in China. This study examines the impact of rumor types, content attributes (including frames, emotion, and rationality), and source characteristics (including follower size and source identity) to show how they affect the likelihood of a COVID-19 rumor and its correction being shared. By exploring the retransmission of rumors and their corrections in Chinese social media, this study will not only advance scholarly understanding but also reveal how corrective messages can be crafted to debunk cyber-rumors in particular cultural contexts.
With the rapid development of information networks, negative impacts of rumor propagation become more serious. Nowadays, knowing the mechanisms of rumor propagation and having an efficient official ...rumor refutation plan play very important roles in reducing losses and ensuring social safety. In this paper we first develop the dynamic 8-state ICSAR (Ignorance, Information Carrier, Information Spreader, Information Advocate, Removal) rumor propagation model to study the mechanism of rumor propagation. Eight influencing factors including information attraction, objective identification of rumors, subjective identification of people, the degree of trust of information media, spread probability, reinforcement coefficient, block value and expert effects which are related to rumor propagation were analyzed. Next, considering these factors and mechanisms of rumor propagation and refutation, the dynamic 8-state ICSAR rumor propagation model is verified by the SIR epidemic model, computer simulation and actual data. Thirdly, through quantitative sensitivity analysis, the detailed function of each influencing factor was studied and shown in the figure directly. According to these mechanisms, we could understand how to block a rumor in a very efficient way and which methods should be chosen in different situations. The ICSAR model can divide people into 8 states and analyze rumor and anti-rumor dissemination in an accurate way. Furthermore, official rumor refutation is considered in rumor propagation. The models and the results are essential for improving the efficiency of rumor refutation and making emergency plans, which help to reduce the possibility of losses in disasters and rumor propagation.
•We create a model to simulate 8 states of people in rumor propagation.•Higher subjective and objective identification will prevent rumor propagation.•Spread probability of information carrier determines the time of rumor propagation.•Expert has persuasiveness will speed up information dissemination in a large scale.•High spread probability and low block value will lead to rumor refutation.
Exploring the dynamic mechanism of rumor reversal in several public emergency events can support managers to control and guild the spread of public opinion. In this study a two-stage rumor model, ...namely, susceptible, positive, negative, and recovered state (SPNR) is built to analyze rumor spread and reversal of rumors regarding emergencies on Weibo. The model considers the hysteresis of official news and public swing mentality based on epidemic models. To validate the proposed model, we compare the simulated data and the spread curve of rumor reversal on the basis of the real event “Chongqing bus plunging into the river”. The result shows the proposed model well simulates this real situation of news breakout, confirming the change of rumor-infected rate and probability of rumor disseminators transforming into positive public opinion disseminators, and the time of official statement for truth affecting rumor propagation in varying degrees. To effectively control and dispel rumors, proper guidance of official statement or authority release for truth in the public event is necessary.
•Propose a two-stage SNPR model to simulate the spread of public opinion.•Model considers the hysteresis of official news and people’s swing mentality based on epidemic models.•Simulate the spread and reversal of rumors based on SNPR.•Represent real situation of news breakout.
With the growing popularity of online social networks, an environment has been set up that can spread rumors in a faster and wider manner than ever before, which can have widespread repercussions on ...society. Nowadays, individuals are joining multiple online social networks and rumors simultaneously propagating amongst them, thereby creating a new dimension to the problem of rumor propagation. Motivated by these facts, this paper attempts to address the rumor influence minimization in multiplex online social networks. In this work, we consider modeling the propagation process of such fictitious information as a significant step toward minimizing its influence. Thus, we analyze the individual and social behaviors in social networks; subsequently, we propose a novel rumor diffusion model, named the HISBmodel. In this model, we propose a formulation of an individual behavior towards a rumor analog to damped harmonic motion. Following this, the opinions of individuals in the propagation process are incorporated. Furthermore, the rules of rumor transmission between individuals in multiplex networks are incorporated by considering individual and social behaviors. Further, we present the HISBmodel propagation process that describes the spread of rumors in multiplex online social networks. Based on this model, we propose a truth campaign strategy in minimizing the influence of rumors in multiplex online social networks from the perspective of network inference and by exploiting the survival theory. This strategy selects the most influential nodes as soon as the rumor is detected and launches a truth campaign to raise awareness against it, so as to prevent the influence of rumors. Accordingly, we propose a greedy algorithm based on the likelihood principle, which guarantees an approximation within 63% of the optimal solution. Systematically, experiments have been conducted on real single networks crawled from Twitter, Facebook, and Slashdot as well as on multiplex networks of real online social networks (Facebook, Twitter, and YouTube). First, the results indicate the HISBmodel can reproduce all the trends of real-world rumor propagation more realistically than the models presented in the literature. Moreover, the simulations illustrate that the proposed model highlights the impact of human factors accurately in accordance with the literature. Second, compared to the methods in the literature, the experiments prove the efficiency of our strategy in minimizing the influence of rumors in the cases of single network and multiplex social network propagation. The results prove that the proposed method can capture the dynamic propagation process of the rumor and select the target nodes more accurately in order to minimize the influence of rumors.
The rapid development of the internet has resulted in a significant rise in online rumor spreading. Therefore, effectively reducing or eliminating the harm resulting from these rumors has become a ...new research focus. This paper proposed a variant of classic Susceptible–Infected–Recovered (SIR) disease transmission model to explore the influence of official rumor-refutation information quantity and content on rumor spread. A nonlinear differential equation model was constructed to describe the rumor propagation process and a next-generation matrix method employed to calculate rumor propagation threshold R0. Two types of equilibrium points are solved, and it is proven that when R0<1, the rumor-free equilibrium is globally asymptotically stable, and when R0>1, the rumor propagation equilibrium is locally asymptotically stable. A sensitivity analysis was performed on R0, and the impact of official rumor-refutation information content on the sensitivity of the R0 related parameters is considered. Then certain numerical values were assigned to the relevant parameters and the evolutionary mechanism simulated in different situations, with the theoretical results being verified using numerical simulation. It was found that the quantity of official rumor-refutation information played a positive but non-critical role in the rumor dissemination process, it even has a backfire effect in rumor controlling process under certain conditions, while the official rumor-refutation information content was a key factor affecting the dissemination threshold; therefore, official rumor-refutation information content needs to be improved to effectively control rumor spread. Finally, some policy suggestions that can contribute to rumor management in an emergency event were given.
•The model considers the influence of quantity and content of rumor-refutation information on rumor.•The quantity of rumor-refutation information is a non-critical factor.•Improving the persuasion of rumor-refutation information is a critical control measure.•Correction rate can directly and indirectly affect propagation threshold.
In view of the high propagation space and the complex networking of rumors, this paper proposes a group behavior prediction model based on sparse representation and interaction of complex messages. ...Firstly, to solve the difficulty in model training caused by the high dimension and complexity of rumor space, sparse representation is considered as the theoretical basis to construct sparse vectors for user node features, and to construct the node feature prediction submodel. Secondly, aiming at the dynamic interactive behavior among complex messages in the rumor space, the driving force of complex messages is quantified with the evolutionary game, the dynamic rumor propagation network is reconstructed, and the structure attribute prediction submodel is constructed. Finally, considering the advantages of model fusion in improving the generalization ability of the single model, the node feature prediction submodel—Submodel Based on SRC and the structure attribute prediction submodel—Submodel Based on Node2Vec are fused. Meanwhile, a dynamic group behavior prediction model under the influence of complex messages is constructed for the time-sensitive nature of rumor propagation. The experimental results show that the model not only effectively explores the interaction between complex messages but also accurately predicts the group behavior and depicts the rules of rumor propagation.
Traditional prediction models of rumor forwarding are based solely on explicit network topology, and with no consideration for homogeneity and antagonism among multi-type rumor messages. To solve ...these problems, this study proposes a user behavior prediction model based on implicit links and multi-type rumor messages. First, because most existing studies are based on explicit network topology and ignore the influence of implicit links on information transmission, this study considers the interaction and similarity among users comprehensively and uses the K-dimension-tree algorithm to mine implicit links among non-friends, thereby improving the network topology. Second, given fuzziness and complexity of user forwarding behavior in multi-type rumor messages, considering the advantages of graph convolutional networks (GCNs) model in network representation, rumor information, user characteristics and network structure are fully represented with features. Finally, considering the high integration ability and adaptive ability of model fusion, a softmax layer is added to finalize the basic multi-classification, and then multiple GCN-based models are fused by a voting mechanism to realize the prediction of user forwarding behavior. Experiments show that the proposed model can effectively predict a user’s forwarding behavior under multi-type rumor topics, and the model has improved generalization ability.
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