Rumor Spreading Research Articles

Rumor Source Detection in Subgraphs: An ML Approach

Social networking sites offer a vast platform for sharing various types of content, such as information, photos, videos, and audio clips. However, the credibility of the information shared on these sites is a significant concern since there is no prior verification process as the information spreads across the networks. This increases the chances of disseminating false or misleading information, often called rumors. The spread of rumors in today's information environment creates severe problems for the credibility of information and public confidence. The quick spread of rumors, frequently conveyed via social media and other online platforms, can affect public opinion and potentially have real-world repercussions. Maintaining the integrity of information ecosystems and averting the potentially damaging impacts of disinformation require detecting and mitigating rumors. In today's social media and online communication age, it is crucial to identify rumors to prevent their spread and mitigate their potential negative impact. In this study, our primary goal is to discover and identify individual sources of rumors. This work addresses two aspects of rumor management: firstly, identifying the difference between rumors and facts, and secondly, tracking the source of the rumor. In conjunction with the attributes, state-of-the-art methods such as SVM, AdaBoost, Random Forest, Logistic Regression, and Graph Convolutional Networks (GCN) are used to accomplish these goals. This approach contributes to the broader goal of combating disinformation and preserving the Accuracy of information.

RBOTUE: Rumor Blocking Considering Outbreak Threshold and User Experience

When a rumor breaks out in online social network (OSN), it can lead to significant negative impact on human society, especially in the context of public emergencies, such as pandemic. Toward restraining rumor outbreak in OSN, one of the effective containment measures is to block influential users to minimize the spread of rumors. However, most of previous efforts ignore the imbalance between the cost and effect of rumor suppression. To fill this gap, from the perspective of public opinion crisis, a dynamic rumor spread model called PISIR model is established, which takes into account the overall popularity and individual tendency of rumors. Based on this model, two rumor blocking algorithms considering outbreak threshold and user experience, called 1-Hop and 2-Hop RBOTUE algorithms, are proposed, respectively. In the algorithms, a hyperbolic discount effect-based user experience mode is introduced as the constraint to ensure the user experience in OSN, then the blocking strategy is implemented on the selected subset of nodes to keep the rumor spread scale always below the outbreak warning line. The experimental results in two synthetic networks and four real OSNs indicate that both 1-Hop and 2-Hop RBOTUE algorithms have lower rumor infection rate and require less number of blocked nodes, which means that proposed algorithms can achieve better blocking performance with less restraining cost of rumors in mainstream social networks, and the two algorithms also have different adaptability for different OSNs.

Cross-Domain Social Rumor-Propagation Model Based on Transfer Learning.

Rumors in different topic domains have different text characteristics but similar emotional tendencies. To resolve the scarce-data problem in some rumor-topic domains, this study proposes a cross-domain rumor-propagation model, which is based on transfer learning. First, given the diversity and complexity of the rumor-propagation landscape, this study introduces a novel method, User-Retweet-Rumor2vec (URR2vec), which leverages the power of representation learning to uncover latent features within rumor topics. It also displays the forwarding relationship between users and rumors, user node information, and rumor-topic information in low-dimensional space. To capture the impact of human emotional cognition during rumor spreading, we also introduce a deep-learning model based on the natural language texts of rumor topics, which analyzes the sentiment in the text and uncovers the emotional correlations among users. Furthermore, a rumor-propagation prediction model based on the text-sentiment analysis-graph convolutional network (TSA-GCN) is proposed and pre-trained on existing rumor-topic data to ensure its prediction accuracy. Finally, considering the data sparsity at a rumor-topic outbreak, the trained propagation model is transferred to the rumor topic for prediction. Meanwhile, the rumor topic in different domains has different edges and conditional distribution, similar emotional characteristics, and network structure among the rumor topics. After fine-tuning the parameter and adding a domain adaptation layer in TSA-GCN, a domain adaptation model based on parameter and graph-structure migration is obtained.

Analysis of rumor spreading with different usage ranges in a multilingual environment

<p>This paper investigates rumor propagation in a multilingual environment, taking into account language usage variations. Firstly, a 2I2S2R model is proposed within a heterogeneous network framework that incorporates both immunologic and cross-transmitted mechanisms. Secondly, the paper calculates the basic reproduction number $ R_0 $ by the next-generation matrix method. Thirdly, the local asymptotic stability and the global asymptotic stability are further explored, which indicate that whether the rumor continuously spreads or becomes extinct is determined by the threshold. Finally, the numerical simulation and sensitivity analysis are given to illustrate the effectiveness of theoretical results and the influence of model parameters on rumor spreading.</p>

Rumor Generation and Spreading on WeChat and Facebook During the COVID-19 Pandemic

Rumor Generation and Spreading on WeChat and Facebook During the COVID-19 Pandemic

The impact of rumors on the success of Covid-19 vaccination programs in a Coronavirus-infected environment: optimal control approach

In this paper, we propose a mathematical model that describes the effect of rumors on the success of vaccination programs against Covid-19 in an environment infected by the coronavirus. The aim of this study is to highlight the role of addressing the spread of rumors regarding vaccination risks and booster doses in the success of vaccination programs and in achieving herd immunity. Additionally, we formulate an optimal control problem by proposing several strategies, including awareness and anti-rumor programs, to assist country officials in achieving successful vaccination programs with optimal effort. The existence of optimal controls is investigated, and Pontryagin's maximum principle is used to characterize them. The optimality system is solved using an iterative method. Finally, we conduct numerical simulations to verify the theoretical analysis using Matlab.

The dynamics and control of a multi-lingual rumor propagation model with non-smooth inhibition mechanism.

In this paper, the dynamic behaviors and control strategies of a rumor propagation model are studied in multi-lingual environment. First, an S2E2I2R rumor propagation model is proposed, which incorporates a non-smooth inhibition mechanism. Meanwhile, the existence and stability of the equilibrium are analyzed, grounded in the spreader threshold of the government intervention. Finally, the optimal control and the event-triggered impulsive control strategies are proposed to mitigate the spread of rumors, and the comparison of their effectiveness is further presented by the numerical simulation and a practical case.

КРИЗИСНЫЕ КОММУНИКАЦИИ ВЛАСТИ И ОБЩЕСТВА В КНР НА ПРИМЕРЕ КЕЙСА «ДАВКА В ШАНХАЕ 2014»

The modern digital world is dominated by new media, which, compared to traditional ones, are characterized by promptness, interactivity, which often leads to distortions of transmitted information. The latter circumstance is especially important in the communication between the authorities and society in crisis situations: to promptly place an official statement from the authorities, to stop the spread of rumors and knock down the wave of panic. The article analyzes the crisis communication of the authorities and society on the example of the “2014 Shanghai Stampede” case on December 31st, 2014, as a result of which 36 people died and 49 suffered. Photos and videos of this event spread uncontrollably around the world through social media, which caused a whole wave of alarming rumors. As an empirical material for the analysis, traditional Chinese media were involved: the “People’s daily” newspaper, the “Liberation Daily” newspaper, the “Shanghai Issue” account on Weibo from January 1 to January 21, 2015. The purpose of the article is to analyze the crisis communication practices of Shanghai municipal authorities with the public, to identify the used situational communication strategies and to evaluate them taking the Chinese specifics into account.

Can the indifferent population affect the spread of rumors?

Can the indifferent population affect the spread of rumors?

The global existence of the solution to the stage-structured SIR system for rumor spreading and its regularity

The global existence of the solution to the stage-structured SIR system for rumor spreading and its regularity

Rumor model on social networks contemplating self-awareness and saturated transmission rate

<p>The propagation of rumors indisputably inflicts profound negative impacts on society and individuals. This article introduces a new unaware ignorants-aware ignorants-spreaders-recovereds $ (2ISR) $ rumor spreading model that combines individual vigilance self-awareness with nonlinear spreading rate. Initially, the positivity of the system solutions and the existence of its positive invariant set are rigorously proved, and the rumor propagation threshold is solved using the next-generation matrix method. Next, a comprehensive analysis is conducted on the existence of equilibrium points of the system and the occurrence of backward bifurcation. Afterward, the stability of the system is validated at both the rumor-free equilibrium and the rumor equilibrium, employing the Jacobian matrix approach as well as the Lyapunov stability theory. To enhance the efficacy of rumor propagation management, a targeted optimal control strategy is formulated, drawing upon the Pontryagin's Maximum principle as a guiding framework. Finally, through sensitivity analyses, numerical simulations, and tests of real cases, we verify the reliability of the theoretical results and further consolidate the solid foundation of the above theoretical arguments.</p>

The dynamics and control of an ISCRM fractional-order rumor propagation model containing media reports

<abstract> <p>Modern social networks are especially beneficial for spreading rumors since they perform as multichannel communication platforms. The spread of false information has a detrimental impact on people, communities, and businesses. Media reports significantly affect rumor propagation by providing inhibiting factors. In this paper, we propose a new ISCRM fractional-order model to analyze the law of rumor propagation and provide appropriate control strategies. First, under fractional differential equations, the boundedness and non-negativeness of the solutions are obtained. Second, the local and global asymptotic stability of the rumor-free equilibrium and rumor-permanence equilibrium are proved. Third, employing Pontryagin's maximum principle, the conditions necessary for fractional optimum control are derived for the rumor model, and the optimal solutions are analyzed. Finally, several numerical simulations are presented to verify the accuracy of the theoretical results. For instance, while media reports can mitigate the propagation of rumors across various dynamic regions, they are unable to completely restrain rumor spread.</p> </abstract>

Governing Online Rumor: An Uncertain Intervention Timing Model for Online Rumor Propagation from the Perspective of Government

In view of different types of online rumors, when the governance institutions (government departments) intervene to achieve the least cost and the best effect is a core issue of rumor governance. Based on the perspective of the government, under the premise of limited human, material and financial resources, and considering the different impacts of government intervention opportunities on the spread of different types of rumors, this paper establishes an uncertain multi-objective network rumor transmission intervention timing model, so as to achieve the optimal allocation of government intervention opportunities for different types of online rumors, minimize the total cost and maximize the inhibition effect of rumor spreading. Then, the equivalent form of the model is given, and the model is solved. In addition, numerical experiments are conducted on three types of rumors during the COVID-19 pandemic. The experimental results show that when the types of online rumors and the focus of government departments on cost and governance effect are different, the optimal time for intervention will change. Finally, combined with the experimental results, corresponding countermeasures and suggestions are put forward for the supervision of online rumors by government departments.

Social media’s effect on today's teenagers' mental and physical health.

The impact of social media on teenagers was explored through a comprehensive survey-based research initiative. Employing a well- organized electronic survey method, our study was conducted at major university colleges in Panvel and various other locations, focusing on individuals aged 15-20. The research delves into students' daily interactions with social media, marking the publication of a longitudinal study investigating the influence of social media on the health of today's teenagers. This investigation unveils the multifaceted effects of social media on teens, shedding light on its potential negative consequences such as distraction, sleep disruption, exposure to bullying, rumor spreading, unrealistic perceptions of others' lives, and peer pressure. The extent of these risks appears to be correlated with the frequency of social media use among teenagers. Through the survey, we meticulously analyzed the initial plans of recent graduates during the COVID-19 pandemic. We compared them to their actual decisions, providing valuable insights into the dynamic nature of their post-graduation choices.

“Amusing ourselves to death”: Mechanisms in cyberbullying prompted by rumors and denigration amidst the COVID-19 pandemic in China

The use of rumors and denigration in cyberbullying have been studied in the contexts of public opinion and public policy, but questions remain. The study presented here examines the association of rumors and denigration with cyberbullying during the COVID-19 pandemic and provides a sentiment evolution and social network map of cyberbullying features at different stages of an episode. This study applied latent dirichlet allocation (LDA) topic modeling, sentiment analysis and semantic network analysis to analyze three Chinese cases where rumors and denigration triggered cyberbullying, with 7691 comments. The authors found that cyberbullying caused by rumors and denigration often started with the release of fake information, and core topics and sentiment values varied between stages of the cyberbullying episode. Collective moral disengagement theory suggests collective behavior may reduce moral self-control, generating online interactive behaviors that enhance the spread of rumors and denigration. Spiral of silence theory also offers perspectives on cyberbullying, which are considered in this study. While the cases in this study occurred within the context of COVID-19, cyberbullying via rumor and denigration may occur in many scenarios, so the current paper will interest multiple audiences. The findings of this study suggest that researchers and decision-makers could helpfully design programs to supervise public opinion, raise awareness of cyberbullying and avoid secondary harms.

Hierarchical graph attention networks for multi-modal rumor detection on social media

The wide spread of rumors across online microblogs has caused a series of adverse impacts on our daily lives. Traditional multi-modal rumor detection models ignore the investigation of deep fusion of different granularity within inter-modality and intra-modality, leading to the information loss and model inexplicability. In this paper, we propose a novel Hierarchical Graph Attention network based framework for Multi-Modal Rumor Detection (HGA-MMRD). More specifically, for a given multi-modal post, we first construct a fine-grained text-image graph which consists of three sub-graphs where the nodes include four types of information (e.g., words, entities, objects from images, and image patches), while six types of edges are built to capture the different semantic interactions of the intra-modality and the inter-modality simultaneously. In order to fuse multi-modal information at different levels, we adopt a hierarchical graph attention network to model each sub-graph of the fine-grained text-image graph, and employ a global feature alignment module to fuse an entire image and a text. Our extensive experimental results and visualization demonstrate that our HGA-MMRD outperforms state-of-the-art methods on five benchmark datasets (i.e., two English datasets and three Chinese datasets) in rumor detection.

Rumor spreading model with a focus on educational impact and optimal control

Rumor spreading model with a focus on educational impact and optimal control

Rumor detection methodology based on sentiment analysis and the transformer model with decision-level fusion

The use of transformer models in natural language processing (NLP) has gained significant attention in recent years due to their exceptional performance in various language tasks. This paper explores the application of transformer models in rumor detection, the relevant research on rumor detection, the use of transformer models, and the techniques used to boost the models performance. Ultimately, the purpose of this paper is to provide insight into the potential of transformer models in detecting rumors on social media. Unlike other rumor-detecting models, the author adds a sentiment analysis model as a supplement to rumor detection. Also, to address the issue of insufficient information in early-stage comments on rumors, this paper proposes a decision-level fusion method before the output layer, which effectively utilizes information from different sources and minimizes the negative impact of insufficient data sources. The early-stage rumor detection accuracy of the model is greatly enhanced by this method, therefore, the articles main contributions can be regarded as follows: First, this paper proposes a combination of an aspect level text sentiment analysis method according to syntactic features, gated recurrent units, and a self-attention mechanism. Experimental findings demonstrate that, compared to the original model without taking the sentiment analysis method into account, the proposed network model has advantages in accuracy and Macro F1 evaluation indexes. Second, a cross-text rumor-detecting method based on Decision-level fusion is proposed. Its advantage is that when the cross-text data source is incomplete and a certain text is missing, another text can be used to continue the analysis. Experimental findings show the effectiveness of this method in improving the accuracy of emotion recognition by integrating data from different modes. Third, a comparison is conducted between the performance of the Transformer-sentiment model and other related models, Text-CNN, Bi-LSTM, etc. The result shows that this integrated Transformer-sentiment model can not only solve the rumor detection tasks at higher accuracy, but can also overcome the shortcomings of the lack of datasets, which means that the model is more robust, and is able to detect rumors at the early stage of the rumor spreading process.

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.