Rumor Spreading Research Articles

Sharia Rules and Controls for Publishing In Social Media

This research addressed the principles and legal guidelines for disseminating various content, whether in written form, images, or videos, through social media platforms. It also discussed the main purposes behind users' intentions to publish content. The researcher reached several research findings and recommendations, including:
 
 Social media platforms exhibit both negative and positive behavioral aspects.
 Sharing judgments and stories is permissible as long as they do not involve forbidden actions.
 It is obligatory to distance oneself from those who spread vice or promote rumors. Neglecting their content and refraining from watching their broadcasts is key to minimizing their harm and diminishing their influence.
 Dissemination of scientific information is recommended when it is presented by a reliable expert. It is prohibited for non-experts to share content within their field of expertise if they lack confidence in it. If someone is not an expert and their credibility is in question, dislike is the least that can be done.
 Spreading rumors, even if they are true, that pertain to public affairs is not permissible.
 It is not permissible to share any post unless the user is reasonably confident in its accuracy.
 The use of social media platforms is subject to Sharia guidelines, which users must be aware of.
 
 
 The research also provided the following recommendations:
 
 Preachers and religious organizations should engage in inviting people and encouraging them not to follow individuals who produce harmful or weak content.
 Governments need to pay significant attention to social media as the “new media”, supporting it and allocating appropriate budgets to preserve Islamic and societal values and maintain identity.
 Implementing strict regulations to restrict the dissemination of content that harms individuals or entities, and more.

From statistical physics to social sciences: the pitfalls of multi-disciplinarity

This is the English version of my inaugural lecture at Collège de France in 2021. I reflect on the difficulty of multi-disciplinary research, which often hinges on unexpected epistemological and methodological differences, for example about the scientific status of models. What is the purpose of a model? What are we ultimately trying to establish: rigorous theorems or ad-hoc calculation recipes; absolute truth, or heuristic representations of the world? I argue that the main contribution of statistical physics to social and economic sciences is to make us realise that unexpected behaviour can emerge at the aggregate level, that isolated individuals would never experience. Crises, panics, opinion reversals, the spread of rumours or beliefs, fashion effects and the zeitgeist, but also the existence of money, lasting institutions, social norms and stable societies, must be understood in terms of collective belief and/or trust, self-sustained by interactions, or on the contrary, the rapid collapse of this belief or trust. The appendix contains my opening remarks to the workshop ‘More is Different’, as a tribute to Phil Anderson.

Stigma, The Horrors of the Past: How Covid-19 Shaped Our Social Story?

In December 2019, a cluster of pneumonia cases emerged in Wuhan, China, eventually linked to the discovery of a novel coronavirus. On March 11, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. This declaration triggered anxiety, fear, and depression among many people. However, the lack of knowledge and the spread of rumours about this new virus also gave rise to social stigma. Frontline healthcare workers, individuals who contracted and recovered from COVID-19, as well as migrants, were particularly affected by this social stigma. Many of them experienced heightened levels of anxiety, fear, and guilt as a result. These feelings often led to symptoms of burnout and depression, and in some extreme cases, even suicidal attempts. In this article, we delve into the various dimensions of social stigma that emerged during the COVID-19 pandemic in India as well as worldwide. Conclusion: Societies that faced stigmatization were more vulnerable and had a greater risk of COVID-19 infection. Social stigma has detrimental effects on both mental and physical health, particularly affecting younger individuals, which could potentially hinder the productivity and economic growth of future generations and the nation as a whole. Providing support, be it social, financial, or mental, to one another, plays a critical role in mitigating stigma. To fully grasp the enduring consequences of social stigma on mental health, future studies should investigate this aspect using prospective research designs.

Inconsistent Matters: A Knowledge-Guided Dual-Consistency Network for Multi-Modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN.

Stability Analysis of a Delayed Rumor Propagation Model with Nonlinear Incidence Incorporating Impulsive Vaccination.

The presence of information asymmetry can hinder the public's ability to make well-informed decisions, resulting in unwarranted suspicion and the widespread dissemination of rumors. Therefore, it is crucial to provide individuals with consistent and dependable scientific education. Regular popular science education is considered a periodic impulsive intervention to mitigate the impact of information asymmetry and promote a more informed and discerning public. Drawing on these findings, this paper proposes a susceptible-hesitant-infected-refuting-recovered (SHIDR) rumor-spreading model to explain the spread of rumors. The model incorporates elements such as time delay, nonlinear incidence, and refuting individuals. Firstly, by applying the comparison theorem of an impulsive differential equation, we calculate two thresholds for rumor propagation. Additionally, we analyze the conditions of global attractiveness of the rumor-free periodic solution. Furthermore, we consider the condition for the rumor's permanence. Finally, numerical simulations are conducted to validate the accuracy of our findings. The results suggest that increasing the proportion of impulsive vaccination, reducing the impulsive period, or prolonging the delay time can effectively suppress rumors.

The Role of New Media in the Public Cognition: Take the Weibo Platform as an Example

With the rapid development of new media, especially the rise of Weibo platforms, its influence on public cognition has become more and more significant. Taking the Weibo platform as an example, this paper deeply discusses the role of new media in public cognition. First of all, by introducing the basic characteristics and functions of the Weibo platform, it also expounds its unique advantages in information transmission. Secondly, the paper analyzes the satisfaction degree of the Weibo platform's demand for obtaining knowledge and information, including information diversity, convenience, and interactivity. However, there are also some problems with the role of new media in public cognition, such as rumor dissemination, information overload, and information distortion. This paper presents solutions and suggestions for these problems. The government and relevant departments should strengthen information monitoring and screening, and reduce the spread of rumors by providing reliable information sources. New media platforms need to take effective measures to improve the credibility of information, such as establishing clear norms and mechanisms for news publication and strengthening the verification of news authenticity. It is also crucial for Weibo platforms to provide valuable and influential content. Platforms should encourage the production and dissemination of quality content while reducing the interference of spam and low-quality content. Finally, improving the public's media literacy is also the key. Through education and training, strengthen the public's ability to judge and distinguish between media information, in order to better cope with information overload and information distortion.

A novel lightweight decentralized attribute-based signature scheme for social co-governance

Social co-governance is an effective way to solve public safety incidents such as COVID-19 incident. But the insiders wouldn’t share the firsthand information for fear of cyber-violence. Though most of researches paid attention to the data access control and security, less attention focused on the identity privacy. So, in this work, it constructed a lightweight decentralized attribute-based signature scheme with unconditional full anonymity (LDABS-UFA) scheme to protect user’s identity privacy security. It confuses the user’s private key with fuzzy factors, and prevents user’s privacy in the signature from being disclosed. It outsources some complex computing to cloud servers to reduce the user device performance overhead. The security analysis result shows that the scheme is UFA security, unforgeability and non-collusion. Besides, a social co-governance system based on lightweight blackbox-based traceable decentralized attribute-based signature (LWBT-DABS) scheme is proposed. By generating fingerprints for the data owner in the signature, regulators can monitor the entire system and prevent malicious user from spreading rumors. And it constructs an accountability mechanism to prevent the abuse of supervisory power. The performance analysis shows that the data signature of our scheme size is constant, and the scheme is feasibility and practicability.

Spread and control of medical rumors in a social network: A generalized diffusion model with a highly asymmetric network structure

Medical rumors have become a threat to modern society. To study the spread and control of rumors, nonlinear differential equations modeling with the well‐mixed assumption is commonly used. However, this approach ignores the underlying network structure which plays an important role in information spreading. We establish a generalized differential equations model to study the spread and control of medical rumors in a highly asymmetric social network. In our model, each node represents a group of people and a “weighted” and “directed” network describes the communications between these nodes. This network can be generated from real‐world data by community detection algorithms. We provide methods to numerically calculate the final size of a rumor in each node and its derivatives with respect to each parameter. With these methods, if the government has resources to influence the parameters subject to certain constraints or cost functions, one can obtain the optimal resources allocation easily through nonlinear programming algorithms. We show that the implications on the government's resources allocation from the well‐mixed special case in the literature or conventional wisdom may become inapplicable in the general situation. Therefore, the underlying network should not be ignored. Because the final size of a medical rumor is not always the best measure of its damage, we extend our results to a wide class of objectives and show that different objectives result in very different implications. While the lack of a rule of thumb may sound negative, our flexible framework provides a powerful workhorse for interested parties to work out the details in their specific situations. Finally, we provide a sufficient condition for no outbreak of rumors. This condition can serve as a heuristic that a government with abundant resources can use to prevent the outbreak of rumors.

Bifurcation control for a fractional-order delayed SEIR rumor spreading model with incommensurate orders

Bifurcation control for a fractional-order delayed SEIR rumor spreading model with incommensurate orders

A Separate Conviction Study on Online Rumors and False Information Dissemination in China

With the rapid development of the Internet in the 21st century, more and more people choose to conduct social and other related activities in the online world, so the spread of online rumors and false information has become more frequent and threatening. In view of these practical factors, this paper mainly uses the method of legal normative analysis to study the separate criminalization of online rumors and false information dissemination in China. How to better face the rampant Internet chaos and the proliferation of criminal behavior. At present, Chinas criminal law norms have undergone two amendments and two judicial interpretations, but there are still many problems. The victims of personal and property rights that have not been properly protected are increasing with the development of the Internet, and it is all the more necessary to separately criminalize the spread of online rumors and false information. This article argues that it is necessary to add separate crimes to the criminal law to regulate it. This article focuses on the contradiction between Chinas current technological development and the old regulations. This has led to difficulties such as the criminal law cannot correspond to all online rumor-mongering behaviors, and the interpretation of the regulatory scope of false information is not clear enough. Attempts were made to change the status quo by separate legislation and additional interpretations. At the same time, the question of defining the boundaries of the offence, focusing on separate convictions, illustrates the necessity of such a move. In order to improve the green environment of the Internet, improve the stability of the Criminal Law, and improve laws and regulations.

Research on the Difference of Artificial Intelligence Use between Chinese News We-media and Traditional News Media--Taking Xinhua News Agency as an Example

With the continuous development and popularization of AI technology, in the field of news communication, more and more media begin to use AI technology for news creation and communication. Although AI has greatly improved the speed of collecting news data and generating news articles, as the application of AI is still in the exploratory stage at present, the extensive use of AI has resulted in the spread of rumors and infringement of intellectual property rights in the network. Through the experimental method of case analysis, this study selected Xinhua News Agency with typical characteristics of traditional news media and typical news we media as the case, made a comparative analysis of these two different media, and concluded that there are differences in AI technology sources and news production links between traditional news media and news we media in China at present. In addition, most news we media using AI lack the important part of self-censorship in the production process, thus resulting in negative social impacts. Based on the above analysis, some suggestions are put forward to solve this problem. The case analysis of the use of AI in news we media in this study fills some gaps in this field and provides a foundation for future research in this field.

Continuous-Time Stochastic Analysis of Rumor Spreading with Multiple Operations

Continuous-Time Stochastic Analysis of Rumor Spreading with Multiple Operations

Conformity versus credibility: A coupled rumor-belief model

The opinion individuals hold about circulating information moderates their spreading behavior, as they may choose to spread rumors they believe in and remain silent about those they do not. Meanwhile, these opinions may be influenced by peers’ opinions and observed behaviors. We present coupled dynamics that integrate opinion formation and information spreading within two-layer multiplex social networks. In an influence layer, opinions are shaped by information credibility and social influence, the degree of which is determined by individual tendencies to conform. In a communication layer, information spreads conditioned on whether individuals believe in it. Using analytical and numerical tools, we demonstrate consistent behavior of the model by identifying the criteria for an evolutionary advantage in opinion formation and a critical spreading rate for rumor outbreaks. We then proceeded to examine two scenarios distinguished by considering credibility as either an intrinsic or a social property of information. Our results show that, for intrinsic credibility, higher degrees of conformity promote the spread of incredible information and inhibit the diffusion of credible information; for social credibility, higher degrees of conformity have an amplifying effect on the evolution of belief and rumor spreading. Additionally, the former scenario exhibits rich behaviors in the interplay between opinion evolution and information spreading, including paradigm shift, passive support, and fringe belief. We thus contribute to the understanding of the dynamics of opinion formation and information propagation in social groups. Empirical application may inform efforts to assess and combat the spread of misinformation.

Predicting viral rumors and vulnerable users with graph-based neural multi-task learning for infodemic surveillance

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) method to improve user and propagation graph representations. Furthermore, we employ two multi-task training strategies to mitigate negative transfer effects among tasks in different settings, enhancing the overall performance of our approach. We also construct two datasets with ground-truth annotations on information virality and user vulnerability in rumor and non-rumor events, which are automatically derived from existing rumor detection datasets. Extensive evaluation results of our joint learning model confirm its superiority over strong baselines in all three tasks: rumor detection, virality prediction, and user vulnerability scoring. For instance, compared to the best baselines based on the Weibo dataset, our model makes 3.8% and 3.0% improvements on Accuracy and MacF1 for rumor detection, and reduces mean squared error (MSE) by 23.9% and 16.5% for virality prediction and user vulnerability scoring, respectively. Our findings suggest that our approach effectively captures the correlation between rumor virality and user vulnerability, leveraging this information to improve prediction performance and provide a valuable tool for infodemic surveillance.

A local rewiring strategy for accelerating information propagation under complex propagation mechanism

Controlling the spread of information or rumors in complex networks has always been a subject of extensive attention and research. Generally speaking, taking the most influential node in the network as the propagation spreader or cutting off the most important link in the propagation process can maximize the promotion or suppression of information transmission. Therefore, many scholars have put forward many measurement methods to identify specific nodes and links in the network, and their theories are becoming more and more mature. However, in the face of large-scale networks, most of these methods are exposed to high computational complexity, low accuracy, and only applicable to specific networks. Some scholars try to enhance (inhibit) the dissemination of information by adding or deleting links, but the structural characteristics of the original network will be changed to a large extent, which also means that some basic functions related to the network have been changed. In this work, we propose a rewiring strategy to accelerate information propagation under complex propagation mechanism, which increases the number of redundant links by rewiring the second-order neighborhood of the initial propagation node. At the same time, the rewiring strategy preserves the degree distribution of nodes and modularization feature of the original network to a large extent. We apply our strategy to simulate the complex propagation of the information propagation (IST(I)) model on different networks. The simulation results show that the rewired network can accelerate the propagation of information more effectively, and the number of spreaders in the network increases significantly, and the propagation rate of information is enhanced significantly. In addition, this paper also proposed for the first time an index that quantifies both the transmission ability and infectivity of the information at the micro level, namely the number of regenerated infections which was optimally estimated by statistical inference. We also compare the changes of various topological characteristics of the network before and after the rewiring. We find that the structural characteristics related to the propagation dynamics of the network after the rewiring are significantly enhanced.

From localized to well mixed: How commuter interactions shape disease spread.

Interactions between commuting individuals can lead to large-scale spreading of rumors, ideas, or disease, even though the commuters have no net displacement. The emergent dynamics depend crucially on the commuting distribution of a population, that is how the probability to travel to a destination decays with distance from home. Applying this idea to epidemics, we will demonstrate the qualitatively different infection dynamics emerging from populations with different commuting distributions. If the commuting distribution is exponentially localized, then we recover a reaction-diffusion system and observe Fisher waves traveling at a speed proportional to the characteristic commuting distance. If the commuting distribution has a long tail, then no finite-velocity waves can form, but we show that, in some regimes, there is nontrivial spatial dependence that the well-mixed approximation neglects. We discuss how, in all cases, an initial dispersal-dominated regime can allow the disease to go undetected for a finite amount of time before exponential growth takes over. This "offset time" is a quantity of huge importance for epidemic surveillance and yet largely ignored in the literature.

Dual-channel early rumor detection based on factual evidence

Social media facilitates people's free expression and communication, but it also provides a platform for the generation and dissemination of rumors. In order to curb the spread of rumors in time and minimize their harm, early rumor detection methods were born and became a research hotspot. Early rumor detection requires a balance between accuracy and timeliness, but most existing methods only focused on accuracy and neglected efficiency. In this regard, we analyze a large number of Internet rumors and found that some rumors have been repeatedly spread after a small amount of modification. Such old rumors can be quickly and easily identified through factual evidence, while other new rumors require more clues and time to crack. Based on this insight, we propose a dual-channel early rumor detection model named ERD-DC (Early Rumor Detection-Dual-Channel), which leverages deep learning algorithms. Specifically, ERD-DC consists of two distinct channels, a fast channel referred to as channel-E (channel-Easy) and a slow channel named channel-D (channel-Difficult). In the ERD-DC, posts are divided into ‘easy-to-identify’ and ‘difficult-to-identify’ based on the availability of relevant evidence online. Subsequently, both types of posts are directed to their respective channels for detection. Channel-E focuses on identifying easy-to-identify rumors through a straightforward and swift post-evidence matching process, primarily aimed at enhancing timeliness. In parallel, channel-D is tasked with uncovering difficult-to-identify rumors, leveraging a more comprehensive analysis of user behaviors and posts transmission structures to elevate overall accuracy. Experimental results on two real datasets demonstrate that ERD-DC outperforms other state-of-the-art models significantly in early rumor detection with an accuracy rate of 84%.

A Social Topic Diffusion Model Based on Rumor, Anti-Rumor, and Motivation-Rumor

The spread of online rumor poses challenges to social peace and public order. Traditional research on rumor diffusion commences from the rumor itself, without considering the symbiosis and confrontation of anti-rumor and motivation-rumor. This study proposed a diffusion method for online rumor based on three messages: rumor, anti-rumor, and motivation-rumor. First, considering the ability of representation learning to learn unsupervised features, we decided to use representation learning method to the diversity and complexity of the content and structure feature space. In particular, we designed a new representation method—Rumor2vec—for the potential structural feature of the rumor diffusion network. Second, considering the mutual promotion and suppression of the three messages, we constructed a new network topology using the cooperative and competitive relationships based on the evolutionary game theory. Finally, considering the ability of graph convolutional network (GCN) to convolute non-Euclidean structure data such as social network, and in view of the time effectiveness of topic evolution, this study proposed a dynamic and game-GCN (evolutionary game theory GCN)-based rumor diffusion model. Experiments show that the model can not only predict the group behavior under the rumor topic but also accurately reflect the cooperation and competition among multiple messages.

Too Real to be Questioned: Analysis of the Factors Influencing the Spread of Online Scientific Rumors in China

With the popularity of mobile terminals and social media increasing, misinformation about science has increased in China. To understand the nature of the popularity of scientific rumors, we analyzed 206 typical cases released by four authoritative platforms in China from 2010 to 2020. Content analysis revealed that the majority of scientific rumors are related to health and safety (76.8%), use a visual format (61.2%), are published on social media (62.2%), and provide more than three narrative elements (78.2%). In addition, rumors from unidentified netizens’ claims or homemade experiments are the most common (35.9%), followed by highly credible sources, such as expert assertions (20.9%) or scientific research results (19.4%). A further qualitative comparative analysis indicated that a combination of details and fear-mongering are significant conditions that make rumors receive significant attention. Visual presentation also plays an important role, while state media and the presence of scientific terminology have a weak effect.

Unsupervised Rumor Detection Based on Propagation Tree VAE

The wide spread of rumors inflicts damages on social media platforms. Detecting rumors has become an emerging problem concerning the public and government. A crucial problem for rumors detection on social media is the lack of reliably pre-annotated dataset to train classification models. To solve this problem, we propose an unsupervised model that detects rumors by measuring how well the tweets follow the normal patterns. However, the problem is challenging in how to automatically discover the normal patterns of tweets. To tackle the challenge, we first propose a novel tree variational autoencoder model that reconstructs the sentiment labels along the propagation tree of a factual tweet. Then we propose a cross-alignment method to align the multiple modalities, i.e. tree structure and propagation features, and output the final prediction results. We conduct extensive experiments on a real-world dataset collected from Weibo. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised methods and adapts better to the concept drift than state-of-the-art supervised methods.