Rumors In Online Social Networks Research Articles

A Dual Rumor Spreading Model with Consideration of Fans versus Ordinary People

The spread of rumors in online social networks (OSNs) has caused a serious threat to the normal social order. In order to describe the rumor-spreading dynamics in OSNs during emergencies, a novel model with consideration of fans versus ordinary people is proposed in this paper. In contrast to previous studies, we consider the case that two rumors exist simultaneously. It is assumed that one is an entertainment rumor that fans care about, and the other is a common rumor. First, we derive the mean-field equations that describe the dynamics of this dual rumor propagation model and obtain the threshold parameter. Secondly, after finding the necessary and sufficient conditions for the existence of equilibriums, we examine the equilibrium’s local and global stability. Finally, simulations are used to explain how various parameters affect the process of spreading rumors.

A novel rumor detection with multi-objective loss functions in online social networks

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.

An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders

- In the ongoing COVID-19 pandemic, people spread various COVID-19-related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to controlling COVID-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel Reputation-based Opinion Leader Identification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user's reputation. The empirical reputation is utilized to calculate the user's trust, the post's entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach's rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate that the influence of OLs is exceptionally significant in controlling COVID-19 rumors.

An Entropy Based Method to Control Covid-19 Rumors in Online Social Networks Using Opinion Leaders

An Entropy Based Method to Control Covid-19 Rumors in Online Social Networks Using Opinion Leaders

Genetic algorithm based rumor mitigation in online social networks through counter-rumors: A multi-objective optimization

Immense use of social media platforms results in quicker and wider dissemination of not only legitimate information, but also rumors which may cause mental stress and reduce the productivity of the society. One of the important ways to counteract the rumors in online social networks (OSNs) is to spread counter-rumors. In this work, we propose a Precedence based Competitive Cascade (PCC) model for the propagation of competing rumor and counter-rumor cascades. We have presented a model to compute a belief based precedence value by which a user chooses to believe the rumor or counter-rumor received during information propagation. The influence of the rumor and the counter-rumor cascades in the OSNs is analyzed by considering a neighborhood based propagation approach. Another challenging issue which has been addressed in this work is to select the minimal seed set of users for the initiation of the counter-rumor so as to reduce the message overhead in the application. This has been formulated as a multi-objective optimization problem to select the minimal set of seed users for counter-rumor to minimize the effect of the rumor. We design a Decomposition based Multi-objective Genetic (DMOG) algorithm to solve the problem. Experiments are conducted on real-world data sets to evaluate the efficacy of the proposed PCC model and DMOG algorithm by considering important parameters such as the precedence, budget and time delay.

A social immunity based approach to suppress rumors in online social networks

Online social networks (OSNs) connect people around the globe under one virtual society. It helps people gather, communate and share their common interests. But many times, OSNs are also exploited and eventually become a major platform for rumor or false information propagation. Controlling such rumors in OSNs has been the most challenging research interest in recent days. Since OSNs are a platform of collective behavior, we focus on a collective rumor containment approach to control or eradicate rumors. In this paper, an anti-rumor information spreading approach is proposed to contain rumors collectively by following a bio-inspired immunization method called social immunity. First, A competitive information propagation model called competitive cascade (CC) model that spreads rumor and true information simultaneously is defined. This model continuously updates the trustworthiness of individuals in the network on every communication among the participants of OSNs. Then, the initial spreaders of anti-rumors are identified with the help of the intensity of the rumor in the network as well as the individual’s trustworthiness. Finally, a collective rumor containment approach is applied by considering the cost of rumor containment and a rumor intensity threshold. The proposed approach is compared with recent and well-known rumor control approaches and the results show that the proposed approach is effective in eradicating rumors.

Mathematical analysis of information propagation model in complex networks

In virtue of identifying the influence of nodes, the spatial distance of rumor propagation is defined with the partition and clustering in the network. Considering the temporal and spatial propagation characteristics of rumors in online social networks, we establish a delayed rumor propagation model based on the graph theory and partial functional differential equations. Firstly, the unique existence and uniform boundedness of the nonnegative solution are explored. Secondly, we discuss the existence of positive equilibrium points sufficiently. Thirdly, stabilities of the rumor-free and rumor-spreading equilibrium points are investigated according to the linearization approach and Lyapunov function. Finally, we perform several numerical simulations to validate theoretical results and show the influence of time delay on rumor propagation. Experimental results further illustrate that taking forceful actions such as increasing the time delay in the rumor-spreading process can control rumor propagation due to the timely effectiveness of the information.

Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks

The rampant propagation of rumors in online social networks leads to potential damage to society. This phenomenon has attracted significant attention to the researches on faster rumor detection. Most recent works on rumor detection adapted the neural network-based deep learning approaches because of its high success rate. Though neural network-based models have high success rates, these models are less efficient in finding rumors at the earliest. Such models require huge training data for better and accurate results. Unfortunately, the data available in the early stages of rumor propagation are sparse in nature. This nature makes rumor detection using neural networks a complex solution, therefore rendering less efficiency for early stage rumors. To address this issue, we have proposed a certainty-factor-based convolutional neural network (CNN) approach to efficiently classify events as rumor or not by leveraging the inherent features of the set of information in spite of data sparsity. The certainty-factor-based activation function requires a minimum number of training data to obtain a generalization. In the proposed approach, two parallel CNNs are employed for the rumor event classification task that efficiently utilizes the inherent features of information, such as temporal, content, and propagation features. A decision tree combines the outputs of both CNNs and provides the classification output. This rumor event classification approach is then compared with recent and well-known state-of-the-art rumor classification approaches, and the results prove that the proposed approach detects rumor efficiently and rapidly with minimal inputs compared to other approaches.

A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks

Along with true information, rumors spread in online social networks (OSN) on an unprecedented scale. In recent days, rumor identification gains more interest among the researchers. Finding rumors also poses other critical challenges like noisy and imprecise input data, data sparsity, and unclear interpretations of the output. To address these issues, we propose a neuro-fuzzy classification approach called the neuro-fuzzy rumor detector (NFRD) to automatically identify the rumors in OSNs. NFRD quickly transforms the input to fuzzy rules which classify the rumor. Neural networks handle larger input data. Fuzzy systems are better in handling uncertainty and imprecision in input data by producing fuzzy rules that effectively eliminate the unclear inputs. NFRD also considers the semantic aspects of information to ensure better classification. The neuro-fuzzy approach addresses the most common problems such as uncertainty elimination, noise reduction, and quicker generalization. Experimental results show the proposed approach performs well against state-of-the-art rumor detecting techniques.

Minimizing the influence of rumors during breaking news events in online social networks

The malicious rumors have tremendously attracted a more substantial number of researchers to join the fight against the propagation of these types of information in online social networks (OSNs). The spread of rumors has a severe impact on society, which can creates political conflicts, shape public opinion and weakens their trust in governments; therefore, it must be stopped as soon as it is detected. This paper investigates the problem of minimizing the influence of malicious rumors that emerge during breaking news, which are characterized by the dissemination of a large number of malicious information over a short period. Therefore, we introduce the problem of multi-rumors influence minimization (MRIM) in OSNs and propose a solution to it. To this end, we design a multi-rumor propagation model named the HISBMmodel that captures the propagation process of multi-rumors in OSNs. Moreover, we present a new formulation of an individual’s opinion toward a rumor based on a Markov chain representation, which adds a layer of realism to the proposed model. Subsequently, we propose a dynamic blocking period (DBP) approach as a solution for the MRIM problem. The main objective is to minimize both the spread and the influence of these rumors in OSNs. The proposed method selects and blocks nodes that most likely to spread a large number of rumors and support them. Different from existing methods, the proposed solution does not block nodes for an unlimited period, but this period is estimated according to the high activity of a node in an OSN. The survival theory has been exploited in this work to provide a solution formulated from the perspective of probabilistic inference of networks. Consequently, an algorithm has been proposed based on a likelihood principle to select the target nodes, which guarantees a (1−1∕e)-approximation of the optimal solution. The experimental results show that the HISBMmodel could capture the propagation of multi-rumor propagation more accurately than classical models and provides metrics to assess the impact of rumors efficiently. Moreover, the results show the outstanding performance of the proposed approach compared to the other solution in the literature. The experimental results show that in the worst-case, the DBP achieves on an average 37.66% reduction on the impact of rumors, compared to 18.46% obtained by the second-best method. However, in the best-case the performance of the proposed method reached 93.38% where second-best method achieved only 57.65% on an average. Besides, even though when the number of rumors is high, the DBP could achieve on an average 68.01% reduction on the impact of rumors.

The social mediation of political rumors: Examining the dynamics in social media and belief in political rumors

Using survey data of social media users in South Korea, this study investigates the dynamics of political rumors in online social networks. Findings of this study reveal the significant connection between the users’ reliance on social media as a source for news and their beliefs in political rumors. Taking a step further, this study underscores the need to understand how users process misinformation they receive through online social networks. Drawing attention to the role of network characteristics in the construction of beliefs around political rumors, this study provides a more nuanced understanding of the conditions under which rumors and misinformation can be regarded as more believable.

Identifying Propagation Sources in Networks: State-of-the-Art and Comparative Studies

It has long been a significant but difficult problem to identify propagation sources based on limited knowledge of network structures and the varying states of network nodes. In practice, real cases can be locating the sources of rumors in online social networks and finding origins of a rolling blackout in smart grids. This paper reviews the state-of-the-art in source identification techniques and discusses the pros and cons of current methods in this field. Furthermore, in order to gain a quantitative understanding of current methods, we provide a series of experiments and comparisons based on various environment settings. Especially, our observation reveals considerable differences in performance by employing different network topologies, various propagation schemes, and diverse propagation probabilities. We therefore reach the following points for future work. First, current methods remain far from practice as their accuracy in terms of error distance ( ${\delta}$ ) is normally larger than three in most scenarios. Second, the majority of current methods are too time consuming to quickly locate the origins of propagation. In addition, we list five open issues of current methods exposed by the analysis, from the perspectives of topology, number of sources, number of networks, temporal dynamics, and complexity and scalability. Solutions to these open issues are of great academic and practical significance.

To Shut Them Up or to Clarify: Restraining the Spread of Rumors in Online Social Networks

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data. We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.