Information Diffusion In Social Networks Research Articles

Modeling Group Opinion Evolution on Online Social Networks: A Gravitational Field Perspective

The research on group behavior is effective for establishing a good network environment since people in social networks tend to form groups spontaneously. Most studies on group behavior on online social networks assume that all individuals are reduced to one cluster, ignoring the existence of potential clusters and their importance in group opinion dynamics. This article introduces a novel group-gravitational field (GGF) model to investigate the opinion evolution based on group behavior by the following aspects: 1) the GGF model reduces a cluster in the social network into a charge and the whole network into a gravitational field; 2) the GGF model calculates the initial influence of a cluster according to the topology information and further constructs a gravity matrix of the network based on the Coulomb law; and 3) opinion-leader clusters exert the internal field force on common opinion clusters inside the gravitational field. The GGF model simulates the evolution of opinions among clusters in a network and studies the law of group behavior according to the influence between clusters based on Coulomb’s law. Experiments on real social networks verify that the GGF model enhances the speed of opinion evolution significantly. The simulation experiments indicate that the existence of clusters promotes the rapid convergence of opinions, a gathering of followers influences information dissemination in social networks, and the GGF model fits the reality better. This article provides a new approach to network supervision and control.

Optimization of network flows for rural extension of fruit and vegetable agricultural technologies

Abstract This paper focuses on the rural promotion of fruit and vegetable agricultural technology and proposes a network flow optimization model based on linear programming for the diffusion of fruit and vegetable agricultural technology. Based on the Bass model, it describes the process of natural growth of network users and the word-of-mouth effect among them. Fruit and vegetable agricultural technology diffusion in real social networks is fitted and predicted by the social network information dissemination model, and the network flow problem of fruit and vegetable agricultural technology promotion is optimized using linear programming equations. Based on this basis, the algorithm designed in this paper and the effect of rural promotion of fruit and vegetable agricultural technology are analyzed with relevant data. The results show that the coefficient of the variable of the number of promotion times in the behavior of agrotechnology promotion is 0.064, which has a significant positive effect on the evaluation of the rural promotion performance of fruit and vegetable agricultural technology, indicating that increasing the number of agricultural technology training and enriching the content of agricultural technology promotion will help to improve the evaluation of the rural promotion performance of fruit and vegetable agricultural technology by farmers. The network flow optimization model constructed in this paper can enable farmers to master more modern fruit and vegetable agricultural technology to meet the needs of modern production of fruit and vegetable agriculture so as to improve the level of local fruit and vegetable agricultural development.

The dynamic coupling mechanism of information and epidemic propagations considering individual mobility in two-layer network framework

The epidemic had taken massive hurt on human beings for their lethal viruses and rapid spread, such as the outbreak of Corona Virus Disease 2019 (Covid-19). How to control the virus spreading came to be a tough question and drew a lot of research attentions at that time. By the empirical data analysis from Shanghai China, we find information diffusion is co-evolving with epidemic spread, and vice versa. In order to reveal the coupling mechanism of epidemic spread and information diffusion, or the influencing factors on epidemic spread, we present a two-layer network framework to investigate the coupling mechanism of information diffusion and epidemic spread, and discuss the influencing factors. Thus, from the perspective of considering individual behaviors (such as individual mobility), a coupling propagation model of information diffusion and epidemic spread in two-layer network framework is proposed by dynamic equations. With the theoretical and numerical analysis, it is found epidemic spread might be influenced by epidemic itself mechanism and individual mobility indicated by mobility frequency and range to a certain extent from the micro perspective. Moreover, the role of individual mobility in promoting or delaying epidemic spread depends on the distribution of epidemic network. While from the macro perspective, it is found that promoting information diffusion in social networks could reduce the epidemic spread scales effectively by calculating the epidemic threshold and comparing with Monte Carlo (MC) simulation experiments. In addition, the global control strength from government or social organizations is also a key factor for reducing epidemic spread. Results in this work provide some reasonable and practical strategies for management on epidemic prevention and control in future.

Assessment of the main features of the model of dissemination of information in social networks

<span lang="EN-US">Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.</span>

An Extension of the Susceptible–Infected Model and Its Application to the Analysis of Information Dissemination in Social Networks

Social media significantly influences business, politics, and society. Easy access and interaction among users allow information to spread rapidly across social networks. Understanding how information is disseminated through these new publishing methods is crucial for political and marketing purposes. However, modeling and predicting information diffusion is challenging due to the complex interactions between network users. This study proposes an analytical approach based on diffusion models to predict the number of social media users engaging in discussions on a topic. We develop a modified version of the susceptible–infected (SI) model that considers the heterogeneity of interactions between users in complex networks. Our model considers the network structure, abandons the assumption of homogeneous mixing, and focuses on information diffusion in scale-free networks. We provide explicit algorithms for modeling information propagation on different types of random graphs and real network structures. We compare our model with alternative approaches, both those considering network structure and those that do not. The accuracy of our model in predicting the number of informed nodes in simulated information diffusion networks demonstrates its effectiveness in describing and predicting information dissemination in social networks. This study highlights the potential of graph-based epidemic models in analyzing online discussion topics and understanding other phenomena spreading on social networks.

CTL-DIFF: Control Information Diffusion in Social Network by Structure Optimization

A critical side effect of online social networks' flourishing is fast-spreading rumors on the Internet, making the information diffusion control on social networks a fundamental requirement. While information diffusion control has received extensive attention at the global level, there have been fewer user-level diffusion control studies under minimal budget. In this article, we study the information diffusion at the user level and propose a diffusion control method based on gradient information to generate an optimized network structure, namely ConTroL information DIFFusion (CTL-DIFF). CTL-DIFF targets a user through subtle modifications of its local network structure. It first selects the edges with the largest absolute gradient based on the prediction model to optimize the original network's structure. It then employs several prediction methods to verify whether the target user's social action status is controlled. CTL-DIFF achieves state-of-the-art control performance with a minimum budget, comparing with five baselines based on edge centrality strategies on four real-world datasets. We extend the diffusion control from user-level to global-level, comparing with four baselines on three datasets. Experimental results show that CTL-DIFF can effectively control information diffusion in the global social network by identifying and controlling the most influential users.

Integrating Multiple Linear Regression and Infectious Disease Models for Predicting Information Dissemination in Social Networks

Social network is the mainstream medium of current information dissemination, and it is particularly important to accurately predict its propagation law. In this paper, we introduce a social network propagation model integrating multiple linear regression and infectious disease model. Firstly, we proposed the features that affect social network communication from three dimensions. Then, we predicted the node influence via multiple linear regression. Lastly, we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks. The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.

PROTECTION OF HUMAN HONOUR AND DIGNITY IN SOCIAL NETWORKS: PROBLEMS AND POSSIBLE SOLUTIONS

This article is devoted to the analysis of the problems of protecting human honour and dignity in social networks. The article reveals three problems on the subject of the study – the widespread use of social networks, virtually unlimited freedom of information dissemination in social networks, as well as the latency of offenses committed in the virtual space. Based on the analysis of the reasons and opinions of some scientists, the author of the article proposes to implement measures in four directions – i) the formation of reasonable legal regulation, ii) strengthening international cooperation, iii) taking organizational and technical measures, iv) increasing the level of legal culture and ethics in social networks. Based on the results of the article, the author concludes that the legal regulation of social networks for the protection of human honour and dignity should be as specific and sufficient as possible.

Representation Learning Method Based on Improved Random Walk for Influence Maximization

The purpose of the influence maximization problem is to determine a subset to maximize the number of affected users. This problem is very crucial for information dissemination in social networks. Most traditional influence maximization methods usually focus too heavily on the information diffusion model and randomly set influence parameters, resulting in inaccurate final outcomes. Driven by the recent criticisms of the diffusion model and the rapid development of representation learning, this paper proposes a representation learning method based on improved random walk for influence maximization (IRWIM) to maximize the influence spread. The IRWIM algorithm improves the traditional random walk and adopts multi-task neural network architecture to predict the propagation ability of nodes more accurately. Moreover, the greedy strategy is utilized to continuously optimize the marginal gain while retaining the theoretical guarantee. IRWIM is tested on four genuine datasets. Experimental results show that the accuracy of the proposed algorithm is superior to various competitive algorithms in the field of influence maximization.

The effect of opinion emotion on information dissemination in social networks

When popular topics arise, Internet users typically share their opinions and interact with others on social networking platforms, and the comments generated on social networks contribute to the spread of popular trends. In this paper, we focus on the popular ChatGPT topic and analyze the evolutionary trend of public opinion and the emotional polarity of comments related to ChatGPT on the Weibo and WeChat social media platforms. In this paper, emotions are divided into six categories, the emotions contained in Weibo and WeChat comments are discussed, and the main opinions reflected in each emotion are examined. We expect that our finding will help reveal the relationship between opinion and sentiment.

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order.

Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach.

Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy.

An approach to detect backbones of information diffusers among different communities of a social platform

Information diffusion in social networks is a classic and, at the same time, very current problem. In fact, information diffusers are always looking for new techniques to disseminate information of their interest by creating backbones among them. In this paper, we focus on a specific, but very current and relevant, scenario regarding this way of proceeding. In fact, we propose an approach for the detection of possible backbones of information diffusers among different communities of a social network. Our approach is based on a new centrality measure that we call disseminator centrality. It is specifically designed to detect the so-called disseminator bridges, i.e., users belonging to multiple communities of a single social network, who want to disseminate information of their interest from one community to another by supporting each other. This paper describes the proposed approach, presents the disseminator centrality, illustrates the differences with respect to the related literature and presents the results of the experiments carried out to evaluate its performance.

Controlling the Spread of Two Secrets in Diverse Social Networks (Student Abstract)

Information diffusion in social networks is a well-studied concept in social choice theory. We propose the study of the diffusion of two secrets in a heterogeneous environment from the complexity perspective, that is, there are two different networks with the same set of agents (e.g., the structure of the set of followers might be different in two distinct social networks). Formally, our model combines two group identification processes for which we do have independent desiderata---either constructive, where we would like a given group of agents to be exposed to a secret, or destructive, where a given group of agents should not be exposed to a secret. To be able to reach these targets, we can either delete an agent or introduce a previously latent agent. Our results are mostly negative---all of the problems are NP-hard. Therefore, we propose a parameterized study with respect to the natural parameters, the number of influenced agents, the size of the required/protected agent sets, and the duration of the diffusion process. Most of the studied problems remain W[1]-hard even for a combination of these parameters. We complement these results with nearly optimal XP algorithms.

Behavior feature extraction method of college students’ social network in sports field based on clustering algorithm

Abstract In order to improve the integrity of the social network behavior feature extraction results for sports college students, this study proposes to be based on the clustering algorithm. This study analyzes the social network information dissemination mechanism in the field of college students’ sports, obtains the real-time social behavior data in the network environment combined with the analysis results, and processes the obtained social network behavior data from two aspects of data cleaning and de-duplication. Using clustering algorithm to determine the type of social network user behavior, setting the characteristics of social network behavior attributes, and finally through quantitative and standardized processing, get the results of college students’ sports field social network behavior characteristics extraction. The experimental results showed that the completeness of the method feature extraction results improved to 9.93%, and the average extraction time cost was 0.344 s, with high result integrity and obvious advantages in the extraction speed.

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis.

In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.

Posters and protesters

Do social media incite protest movements, how? How does onsite protest shape online communication dynamics on social media? We examine the oft-debated relationship between social media activity and protest, considering the Yellow Vests or Gilets Jaunes movement in France. We use data of temporal and spatial distribution of Facebook activity and onsite events over a year of social mobilization to address these questions within a system of interacting online and offline activity. This study contrasts with others by analyzing mutual influences between online and offline domains while considering collective information diffusion in social networks. The results show that the relationship between social media activity and onsite protests is not constant nor one-sided, but mutual, and that it changes over the course of the collective action in specific and measurable ways. Using online and offline data, we show different mechanisms by which both domains of activity influence each other. Specifically, the creation of Facebook groups impacts the outbreak of protests, and in turn, this outbreak modifies the parameters of information diffusion between users on Facebook. In addition, Facebook content that pertains to the frames of the movement and mobilization affects onsite mobilization efforts. Our work contributes to the modeling of social movements as systems of online and offline events, expanding on studies that traditionally examine the impact of online activity over onsite protest.

Markov chain model representation of information diffusion in social networks

Abstract The spread of information in a social network has received renewed interest as social media becomes an increasingly popular channel of communication. We are interested in the phenomenon of social diffusion of a piece of information in the presence of a contradicting information in the network. Specifically we explore the use of formal methods for verification in studying this phenomena. Using Monte Carlo simulation and the probabilistic model checker (PRISM) we are able to represent social networks and confirm an earlier conjecture that disseminating new information rapidly is resistant to the presence of contradicting information.

Modeling information diffusion in social networks with ordinary linear differential equations

Nowadays, online social networks such as Twitter, Digg, and so on have become extremely popular thanks to the provision of unprecedented quantities of data to network users. This event has encouraged many studies of information diffusion in online social networks, especially in the mechanism and dynamics of the information diffusion process over such networks. Many previous studies of information diffusion in social networks have been based on experimental analyses or differential equations with temporal dimensions. Some recent studies have developed the information diffusion process using the heat transfer equations, the reaction–diffusion equations, or the hydrodynamic equations.This paper proposes a model called Ordinary Differential Information Diffusion or ODID to study both temporal and spatial patterns of the information diffusion process in online social networks. By using the formulation of ordinary differential equations, this model is constructed with friendship, relationships, and interactions between network users without quite relying on geometric linkages of the underlying network.The model is validated by the Digg data set whereby it is confirmed that with 95% confidence the ODID computations are not significantly different from the actual data and the average prediction accuracy of the model is about 98.78%.

Density Peaks Clustering Based on Potential Model and Diffusion Strength

Density peaks clustering (DPC) is a simple and efficient density-based clustering algorithm without complex iterative procedures. However, DPC needs to manually choose clustering centers via a decision graph, which often can’t identify real centers and breaks the continuous flow of the algorithm. In addition, DPC is highly sensitive to the cut-off distance and suffers from the domino chain reaction. To surmount the aforementioned deficiencies, an improved density peaks clustering based on potential model and diffusion strength (DPC-PMDS) is proposed in this paper. Firstly, we utilize the potential and centrality of data points to calculate the density instead of the cut-off distance. Secondly, inspired by the information diffusion in social networks, we define the influence of data points and the diffusion strength between data points, and realize the diffusion of label from each center to the core data points while selecting clustering centers. Through this process, the core structure of each clustering is obtained and the centers are accurately identified. Finally, the distances from the boundary points to each cluster computed based on centrality are applied to assign boundary points to avoid chain reaction. Extensive experiments on synthetic, UCI and Olivetti Faces datasets demonstrate that DPC-PMDS can achieve excellent clustering results over other state-of-art algorithms, especially on datasets with complex shapes and uneven density distribution.