Dynamics Of Information Dissemination Research Articles

Modeling the dissemination of privacy information in online social networks

AbstractDriven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

Towards investigating influencers in complex social networks using electric potential concept from a centrality perspective

Influencers in complex social networks are crucial for shaping the dynamics of information dissemination, viral marketing strategies, and controlling public opinion. This raises an important question: which influencers wield the most power within these networks, and how can we effectively identify and strategically utilize a key group of disseminators to maximize their influence across various domains? To address this, various heuristic strategies have been developed, targeting specific scenarios. However, these strategies often fall short by focusing solely on nodal topology and ignoring the intricate interconnections among nodes. This oversight can lead to imprecise evaluations and reduce the effectiveness of information spread. To overcome these limitations, a comprehensive approach that considers both the local and global topological dynamics of networks is essential. This paper introduces a novel centrality measure, electric potential centrality (EPC), which incorporates both the degree of nodes and the distances between them. Inspired by the concept of electric potential, EPC is designed to identify key influencers by extensively analyzing topological features, including local connections and the overall network structure. The effectiveness of EPC is rigorously evaluated through experiments on both synthesized and real-world networks, which are undirected and unweighted. Our findings reveal that EPC significantly surpasses existing methodologies, exhibiting the strongest correlation with baseline metrics within the susceptible–infected–recovered model.

An Elaboration Likelihood Model of Information Dissemination Dynamics with the Influence of Opinion Leaders

An Elaboration Likelihood Model of Information Dissemination Dynamics with the Influence of Opinion Leaders

A new technique for influence maximization on social networks using a moth-flame optimization algorithm

In our modern digital era, social networks have seamlessly integrated into the fabric of our daily lives. These digital platforms serve as vital channels for communication, exchanging information, and cultivating valuable connections. The propagation of information within these social networks has emerged as a central focus for numerous sectors, including politics, marketing, research, education, and finance. Diverse models have been employed to depict the dynamics of information dissemination across these networks. Nevertheless, the notion of influence holds profound significance for both businesses and individuals. Influence maximization, particularly within the context of social networks, has garnered considerable attention owing to its potential to reach and impact a broad audience. This intricate challenge is commonly referred to as the “influence maximization problem,” a problem well-known for its NP-hard complexity. This paper proposes a cutting-edge technique that leverages the Moth-Flame Optimization Algorithm to enhance influence maximization. Influence maximization is an important issue in network analysis, which widely occurs in social networks. Influence can be seen as a cascading effect, where the actions of a few trigger a chain reaction, ultimately reaching a large portion of the network. Identifying these “influencers” is crucial for efficient resource allocation and information dissemination. One of the important issues in finding the maximum influence is choosing the best vertex among all the vertices in the graph. This research presents a new method to find the maximum influence in social networks based on the Moth-Flame Algorithm (MFA). The proposed method aims to find the maximum influence in the social network graph that has a good fitness degree. The algorithm can identify potential influencers. Our simulations across multiple networks have unequivocally showcased the superiority of this algorithm as the preeminent and scalable solution to the influence maximization problem. The experimental outcomes clearly delineate that the employment of the MFA (Maximal First Activation) approach effectively diminishes the execution time required to approximate the maximum influence. The proposed technique improved the accuracy and excucation time by 3.140 % and 12.2 % compared to other methods.

A Dynamic Information Dissemination Model Based on User Awareness and Evolutionary Games

In online social networks, users’ awareness of a topic and their personal consciousness can significantly influence the dissemination of information on that topic. Traditional information dissemination research has mainly introduced user awareness as a single factor and has failed to fully consider the impact between users with different awareness. By considering the differences in user awareness and their impact, we proposed a multiple layer spatial information dissemination model based on user awareness. First, for the multidimensional nature of the communication space, we consider differences in users’ cognitive level into consideration, and by setting the corresponding threshold, the users who participate in topic dissemination are divided into two states of high awareness and low awareness. By constructing different communication networks, we depict the propagation dynamics of information at different levels of awareness and introduce an evolutionary game mechanism to analyze the influence between users at different levels of awareness networks. Second, during information dissemination, isolating transmission sources can provide effective control over the spread of information. Based on the susceptible-infectious-recovery (SIR) model in infectious disease dynamics, we consider that susceptible users have a certain chance of being isolated, so we abstract the isolated user group as an independent state Q (quarantine) and introduce it into the traditional SIR model to construct an SQIR information dissemination model. Finally, considering the influence between the information disseminated by high awareness users and low awareness users and the differences in users’ communication behavior at different periods, a dynamic information dissemination model based on user awareness and evolutionary game is obtained through a time-slicing process to dynamically explore the dynamics of information dissemination. The experiments showed that the proposed model could effectively reveal the influence of different levels of awareness on the dynamic information dissemination trend.

Information dissemination dynamics through Vehicle-to-Vehicle communication built upon traffic flow dynamics over roadway networks

This research is dedicated to developing a discrete mathematical simulation framework to track information dissemination dynamics via Vehicle-to-Vehicle (V2V) communication factoring traffic flow dynamics, traffic intersection operation settings, and traffic intersection geometrical design over a road traffic network. Specifically, we develop information network flow models (INFMs), including IFNM-a and IFNM-r, respectively for tracking information wavefront spreading dynamics at arterial intersections and at highway-ramp intersections. Next, by integrating IFNMs with the information and traffic coupled cell transmission model (IT-CTM) model developed by Du et al. [16] for capturing the information front propagation dynamics on a road segment, we establish a discrete mathematical simulation framework (IFNM-CTM) to track the information front spreading dynamics over a road network at discrete time stamps. Furthermore, by combining the IFNM-CTM framework and the deep search algorithms, this study tracks the information coverage dynamics and investigates its correlation to traffic congestion evolution over a traffic network at discrete time stamps. Our experiments built upon Sioux Falls city network indicate that the IFNM-CTM is able to track the information front spreading, including location and coverage, accurately with the mean absolute error (MAE) less than 6% and 5%, respectively. More importantly, our studies found a strong correlation existing between information front spreading dynamics and traffic congestion evolution over the network. Specifically, a mild congestion condition (i.e., LOS C and D) provides the best traffic condition to sustain information spreading as compared to sparse (i.e., LOS A or B) and heavily congested (i.e., LOS E or F) traffic conditions since neither of them can sustain stable and constant wireless communication due to the limited transmission range of DSRC and interference issues.

Probing Reluctance to Green Consumerism in an Emerging Economy: An Empirical Evidence

Purpose: The paper aims to understand how eco labels influence attitude towards green product purchases. The study progresses to analyze why attitude does not translate to actual purchase behavior even when eco labels transpire green information to customers'. This is done using the Theory of Planned Behavior (TPB) Design/methodology/approach: The study used partial least square structural equation modeling (PLS SEM) to analyze the statistical data. The TPB was used as the root model to understand the green purchase behavior and how eco labels with product attributes influence attitude towards green products. The variables of TPB namely attitude, subjective norms and perceived behavioral control contributed to the constellation of green product behavior. The questionnaire was distributed using convenience sampling method to 241 youths (university students). Findings: Firstly, the study successfully established the validity of TPB in the Indian context towards green products. It classifies the model on product, perception and actual level which adds to the better rendition for consumers' green behavior. Secondly, with the addition of eco labels and product attributes of green products improved the prediction power of TPB from 53.1% to 58.5%. Thirdly, eco labels and product attributes came out to be cardinal antecedents to harbor favorable attitude towards green products. The study also found the attitude was not translating to green purchase behavior and mandated further inquiry to the concern. Finally, the study vouches for gender neutrality while promoting green products. Research implications: Firstly, the study adds to the mounting evidences towards the applicability of TPB towards green product purchase in India. Secondly, the study addresses the importance of ecol labels and product attributes towards harboring favorable attitude towards green products. Finally, unfolds the nuances for the inability to translate attitude towards green purchase behavior. Managerial implications: Firstly, the study advances the indispensability of eco labels to promote green products. Firms can materialize on this dynamics of information dissemination through eco labels and promote sustainable attitude among consumers'. Secondly, the firms can understand the dynamics of consumer behavior and work accordingly to frame policies conducive towards translating attitude into green purchase behavior. Thirdly, the study strongly documents the need to market green products in a gender neutral manner. Originality: The study draws novelty on two prospects. Firstly, the study uses eco labels and product attributes as antecedents towards attitude. Secondly, the study also aims to determine the actual green purchase behavior using the TPB model. Thirdly, the study investigates the moderating role of gender towards green product behavior.

The coupled dynamics of information dissemination and SEIR-based epidemic spreading in multiplex networks

The outbreak of coronavirus disease 2019 (COVID-19) threatens the health and safety of all humanity. This disease has a prominent feature: the presymptomatic and asymptomatic viral carriers can spread the disease. It is crucial to estimate the impact of this undetected transmission on epidemic outbreaks. Currently, disease-related information has been widely disseminated by the mass media. To investigate the impact of both individuals and mass media information dissemination on the epidemic spreading, we establish a new UAU-SEIR (Unaware–Aware–Unaware–Susceptible–Exposed–Infected–Recovered) model with mass media on two-layer multiplex networks. In the model, E-state individuals denote asymptomatic infections, and a single node connecting to all individuals denotes the mass media. In this work, we use the Microscopic Markovian Chain Approach (MMCA) to derive the epidemic threshold. Comparing the MMCA theoretical results with Monte Carlo (MC) simulations, we find that the MMCA has a good consistency with MC simulations. In addition, we also analyze the impact of model parameters on epidemic spreading and epidemic threshold. The results show that reducing the proportion of asymptomatic infections, accelerating the dissemination of information between individuals and the dissemination of information via the mass media can effectively inhibit the epidemic spreading and raise the epidemic threshold.

Linking the disease transmission to information dissemination dynamics: An insight from a multi-scale model study

During the outbreak of emerging infectious diseases, information dissemination dynamics significantly affects the individuals’ psychological and behavioral changes, and consequently influences on the disease transmission. To investigate the interaction of disease transmission and information dissemination dynamics, we proposed a multi-scale model which explicitly models both the disease transmission with saturated recovery rate and information transmission to evaluate the effect of information transmission on dynamic behaviors. Considering time variation between information dissemination, epidemiological and demographic processes, we obtained a slow-fast system by reasonably introducing a sufficiently small quantity. We carefully examined the dynamics of proposed system, including existence and stability of possible equilibria and existence of backward bifurcation, by using the fast-slow theory and directly investigating the full system. We then compared the dynamics of the proposed system and the essential thresholds based on two methods, and obtained the similarity between the basic dynamical behaviors of the slow system and that of the full system. Finally, we parameterized the proposed model on the basis of the COVID-19 case data in mainland China and data related to news items, and estimated the basic reproduction number to be 3.25. Numerical analysis suggested that information transmission about COVID-19 pandemic caused by media coverage can reduce the peak size, which mitigates the transmission dynamics during the early stage of the COVID-19 pandemic.

ASSESSING THE DYNAMICS OF DISSEMINATION OF INFORMATION MESSAGES ACCORDING TO THE DATA OF ELECTRONIC MASS MEDIA

The main directions of destructive information and psychological influence of the enemy on the population of Ukraine, leaders and personnel of the military administration, the Armed Forces of Ukraine and other law enforcement agencies, issues of the analysis of the dissemination of information messages (content) of the electronic media which contain the destructive information and psychological impact are considered. The model of social networks as a means of mass communication, which is used for the realization of destructive information and psychological influence is considered. The main components of the model are the user, his thoughts (views), influence, trust and reputation. The process of influencing to the user by means of social networks through innovation and its dissemination is considered. Indicators that can be used to characterize the process and to evaluate the impact: “likes”, “dislikes”, “reposts”, “views”, and “comments” are also provided. The process of tracking the destructive influences contained in information messages from the point of view of destructive informational and psychological influence is described. The system of indicators of dynamics of distribution of information messages on the Internet is analyzed. The necessity of fixing on certain points of time and use of additional indicators: “number of drawings”, “absolute growth”, “growth rate”, “growth rate” is grounded and the order of their calculation is given. The logical and structural scheme of calculating the dynamics of information message dissemination by means of the Internet has been developed. It is determined that to solve the problem of automation of tracking and visualization of the dynamics of information dissemination requires specialized software that will read the primary indicators from certain publications of electronic communications and social networks and calculate the proposed indicators of the dynamics of information message propagation.

Modeling the dynamics of information dissemination under disaster

Information dissemination has drawn increasing attention of scholars and the government for its importance to disaster response. Understanding the dynamics of information dissemination can help the government disseminate information effectively. Considering people tend to share information with the intimate under disaster, social distance which measures the intimacy between individuals is introduced in this paper. An information dissemination model with preference selection based on social distance is also proposed. Through extensive simulations, we discover the information dissemination process will be suppressed if people prefer disseminating information to individuals with short social distance. In order to facilitate the information dissemination, award to spreaders is found useful for its significant acceleration in dissemination speed. In addition, we examine the efficiency of strategies the government adopts to disseminate information. Comparing to inform-way (q fraction of nodes informed in descending order of H-index), the government is recommended to disseminate information by broadcast-way (all nodes accepting information with a probability λb). Our work contributes to understanding the dynamics of information dissemination, and provides the government with suggestions to disseminate information effectively under disaster.

Information dissemination in dynamic hypernetwork

Recently, the information diffusion models based on static hypernetworks have been proposed, in which the nodes and hyperedges represent the individuals and the social groups consisting of several individuals, respectively. However, the social networks represented by hypernetwork should be a dynamic network because of social activities. Hence, we establish an SIS model to present the dynamics of information spreading in a dynamic social hypernetworks with three dynamic processes. Then the theoretical analysis and simulation of the model are carried out, and theoretical results are accordant with the simulation results. In addition, the effects of different parameters on the information dissemination dynamics are analyzed in RP and CP strategy respectively. Most noteworthy, reorganization of social relations leads to a decrease in the proportion of informed individuals in stable state. By analyzing the effect of hyperedge reorganization on hyperdegree distribution, we discover the reorganization result in the increasing number of isolated nodes. These isolated nodes is hard to be joined in new social relationships, and cannot get information from the social network.

Temporal Dynamics of Information Diffusion in Twitter: Modeling and Experimentation

Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type.

Users' participation and social influence during information spreading on Twitter.

Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading.

Modelling information dissemination under privacy concerns in social media

Social media has recently become an important platform for users to share news, express views, and post messages. However, due to user privacy preservation in social media, many privacy setting tools are employed, which inevitably change the patterns and dynamics of information dissemination. In this study, a general stochastic model using dynamic evolution equations was introduced to illustrate how privacy concerns impact the process of information dissemination. Extensive simulations and analyzes involving the privacy settings of general users, privileged users, and pure observers were conducted on real-world networks, and the results demonstrated that user privacy settings affect information differently. Finally, we also studied the process of information diffusion analytically and numerically with different privacy settings using two classic networks.

Optimal control of epidemic information dissemination over networks.

Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.

Of Pins and Tweets: Investigating How Users Behave Across Image- and Text-Based Social Networks

Today, it is the norm for online social (OSN) users to have accounts on multiple services. For example, a recent study showed that 34% of all Twitter users also use Pinterest. This situation leads to interesting questions such as: Are the activities that users perform on each site disjoint? Alternatively, if users perform the same actions on multiple sites, where does the information originate? Given the interlinking between social networks, failure to understand activity across multiple sites may obfuscate the true information dissemination dynamics of the social web. In this study, we take the first steps towards a more complete understanding of user behavior across multiple OSNs. We collect a sample of over 30,000 users that have accounts on both Twitter and Pinterest, crawling their profile information and activity on a daily basis for a period of almost three months. We develop a novel methodology for comparing activity across these two sites. We find that the global patterns of use across the two sites differ significantly, and that users tend to post items to Pinterest before posting them on Twitter. Our findings can inform the understanding of the behavior of users on individual sites, as well as the dynamics of sharing across the social web.

The effect of pre-entry information on relational outcomes in franchising

Purpose– The purpose of this paper is to examine how pre-entry franchising information (i.e. franchisor-provided) influences the confirmation (or not) of franchisees' pre-entry expectations and their subsequent assessment of trust in their franchisor. Relationships with outcome variables, such as perceived conflict and relational satisfaction, are also examined. Finally, the authors compare their hypothesised model across male and female franchisees and different patterns of relationships result.Design/methodology/approach– Data were collected via a mail survey administered to a sample of 3,000 franchisees in Australia, which resulted in the collection of 339 useable responses (11 per cent response rate). The conceptual model was tested via structural equation modelling using AMOS 17.0 and all paths were significant, as hypothesised. However, when the sample was split across gender, some clear differences emerged.Findings– The findings reveal that male and female franchisees use pre-entry informational sources in different ways in formulating their expectations and trust in their franchisors. Furthermore, in terms of relational outcomes, perceived conflict plays a very different role in regard to overall relational satisfaction when examined across gender.Originality/value– This paper addresses a significant gap in the literature by examining the dynamics of information dissemination in influencing franchisees' pre-entry expectations of, and trust in, their franchisors. In addition, gender differences in pre-entry information dissemination, expectations (dis)confirmation and relational outcomes, not yet explored, are also reported. The results prompt significant implications and suggestions for future research in this under-researched area of franchising inquiry.

Diffusion Models for Information Dissemination Dynamics in Wireless Complex Communication Networks

Information dissemination has become one of the most important services of communication networks. Modeling the diffusion of information through such networks is crucial for our modern information societies. In this work, novel models, segregating between useful and malicious types of information, are introduced, in order to better study Information Dissemination Dynamics (IDD) in wireless complex communication networks, and eventually allow taking into account special network features in IDD. According to the proposed models, and inspired from epidemiology, we investigate the IDD in various complex network types through the use of the Susceptible-Infected (SI) paradigm for useful information dissemination and the Susceptible-Infected-Susceptible (SIS) paradigm for malicious information spreading. We provide analysis and simulation results for both types of diffused information, in order to identify performance and robustness potentials for each dissemination process with respect to the characteristics of the underlying complex networking infrastructures. We demonstrate that the proposed approach can generically characterize IDD in wireless complex networks and reveal salient features of dissemination dynamics in each network type, which could eventually aid in the design of more advanced, robust, and efficient networks and services.

Modeling Information Dissemination in Generalized Social Networks

Information dissemination dynamics is an important performance index for characterizing the speed and scope of information dissemination in a network. Several analytical models have been proposed to estimate the information dissemination dynamics for social networks adopting a susceptible-infected information propagation model and having a constant informed rate. This letter extends existing model to analyze the social networks adopting either a susceptible-infected or an independent-cascade information propagation model and having a time-varying informed rate. Validated by simulations, our model demonstrates its flexibility and applicability to approximate the complicated propagation behaviors of advertisement-like or malware-like information.