Information Diffusion Analysis Research Articles

Differentiating broadcast from viral: a causal inference approach for information diffusion analysis

Classifying information diffusion patterns is critical to many information analysis areas, e.g., misleading information detection. However, diffusion pattern classification remains challenging when multiple users are involved. To address this challenge, this study aims to classify how information diffuses, distinguishing between broadcast and viral spreading, solely through the analysis of observational data from retweet networks on X (formerly known as Twitter). In broadcasting, most users directly receive information. However, viral spreading allows users the opportunity to receive information from a variety of sources. Therefore, viral spreading increases the likelihood of identifying misleading information. Existing methods classify diffusion types mainly through structural virality, which relies on the average distance between the users. However, when dealing with diffusion networks involving two or more information sources, these approaches can potentially lead to confusion regarding causality. To tackle this problem, we develop a deterministic causal inference method for categorizing information diffusion types. To the best of our knowledge, this is the first study investigating information diffusion types based on causality. This approach can be used to assess source credibility and assist in detecting misleading information. It can also be extended to other social media platforms.Graphical

Multi-level emotion propagation in natural disaster events: diverse leadership of super-spreaders in different levels of hierarchy

PurposeIn social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.Design/methodology/approachWe collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.FindingsOfficial media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.Originality/valueThis study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.

A novel regularized weighted estimation method for information diffusion prediction in social networks

In recent years, social networks have become popular among Internet users, and various studies have been performed on the analysis of users’ behavior in social networks. Information diffusion analysis is one of the leading fields in social network analysis. In this context, users are influenced by other users in the social network, such as their friends. User behavior is analyzed using several models designed for information diffusion modeling and prediction. In this paper, first, the problem of estimating the diffusion probabilities for the independent cascade model is studied. We propose a method for estimating diffusion probabilities. This method assigns a weight to each individual diffusion sample within a network. To account for the different effects of diffusion samples, several weighting schemes are proposed. Afterward, the proposed method is applied to real cascade datasets such as Twitter and Digg. We try to estimate diffusion probabilities for the independent cascade model considering the continuous time of nodes’ infections. The results of our evaluation of our methods are presented based on several datasets. The results show the high performance of our methods in terms of training time as well as other metrics such as mean absolute error and F-measure.

Audience Analysis for Competing Memes in Social Media

Existing tools for exploratory analysis of information diffusion in social media focus on the message senders who actively diffuse the meme. We develop a tool for audience analysis, focusing on the people who are passively exposed to the messages, with a special emphasis on competing memes such as propagations and corrections of a rumor. In such competing meme diffusions, important questions include which meme reached a bigger total audience, the overlap in audiences of the two, and whether exposure to one meme inhibited propagation of the other. We track audience members’ states of interaction, such as having been exposed to one meme or another or both. We analyze the marginal impact of each message in terms of the number of people who transition between states as a result of that message. These marginal impacts can be computed efficiently, even for diffusions involving thousands of senders and millions of receivers. The marginal impacts provide the raw material for an interactive tool, RumorLens, that includes a Sankey diagram and a network diagram. We validate the utility of the tool through a case study of nine rumor diffusions. We validate the usability of the tool through a user study, showing that nonexperts are able to use it to answer audience analysis questions.

COVID-19 pandemic and information diffusion analysis on Twitter.

The COVID‐19 pandemic has impacted all aspects of our lives, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible‐Infected‐Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model (SIRsim), observed SIR model based on actual COVID‐19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas) by using network analysis and diffusion modeling. We propose three primary research questions: (a) What are the diffusion patterns of COVID‐19 virus spread, based on SIRsim and SIRemp? (b) What are the diffusion patterns of information cascades on Twitter (INFOcas), with respect to retweets, quote tweets, and replies? and (c) What are the major differences in diffusion patterns between SIRsim, SIRemp, and INFOcas? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.

Tweedie-Hawkes Processes: Interpreting the Phenomena of Outbreaks

Self-exciting event sequences, in which the occurrence of an event increases the probability of triggering subsequent ones, are common in many disciplines. In this paper, we propose a Bayesian model called Tweedie-Hawkes Processes (THP), which is able to model the outbreaks of events and find out the dominant factors behind. THP leverages on the Tweedie distribution in capturing various excitation effects. A variational EM algorithm is developed for model inference. Some theoretical properties of THP, including the sub-criticality, convergence of the learning algorithm and kernel selection method are discussed. Applications to Epidemiology and information diffusion analysis demonstrate the versatility of our model in various disciplines. Evaluations on real-world datasets show that THP outperforms the rival state-of-the-art baselines in the task of forecasting future events.

Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling

In recent days, social media plays a significant role in the ecosystem of the big data world and its different types of information. There is an emerging need for collection, monitoring, analyzing, and visualizing the different information from various social media platforms in different domains like businesses, public administration, and others. Social media acts as the representative with numerous microblogs for analytics. Predictive analytics of such microblogs provides insights into various aspects of the real-world entities. In this article, a predictive model is proposed using the tweets generated on Twitter social media. The proposed model calculates the potential of a topic in the tweets for the prediction purposes. The experiments were conducted on tweets of the regional election in India and the results are better than the existing systems. In the future, the model can be extended for analysis of information diffusion in heterogeneous systems.

ChronoGraph: Enabling Temporal Graph Traversals for Efficient Information Diffusion Analysis over Time

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.

Evolutionary information dynamics over social networks: a review

Purpose The purpose of this paper is to have a review on the analysis of information diffusion based on evolutionary game theory. People now get used to interact over social networks, and one of the most important functions of social networks is information sharing. Understanding the mechanisms of the information diffusion over social networks is critical to various applications including online advertisement and rumor control. Design/methodology/approach It has been shown that the graphical evolutionary game theory (EGT) is a very efficient method to study this problem. Findings By applying EGT to information diffusion, the authors could predict every small change in the process, get the detailed dynamics and finally foretell the stable states. Originality/value In this paper, the authors provide a general review on the evolutionary game-theoretic framework for information diffusion over social network by summarizing the results and conclusions of works using graphical EGT.

What makes cancer information viral on social media?

Although critical in practice, as well as prevalent, limited effort has gone into understanding the driving factors behind the diffusion of cancer information on social media. This study seeks to comprehensively examine both the content and sender factors that determine cancer information diffusion. Specifically, a multidimensional measure is proposed to capture diffusion characteristics of cancer information, including the scale of information diffusion, structural virality of information diffusion, and the engagement in cancer discussion. A conceptual framework consisting of content and sender factors is proposed based on the heuristic-systematic model to account for cancer information diffusion. Through analyzing information diffusion networks of cancer-related information on Weibo, the results revealed that sender factors played determining roles in affecting information diffusion. The content factors also significantly predicted information diffusion. The factors driving engagement are substantially different from those of scale and virality of information diffusion. Implications for both information diffusion theories and cancer education on social media are discussed.

D-Map+

Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user’s posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.

What an Entangled Web We Weave: An Information-centric Approach to Time-evolving Socio-technical Systems

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems.

Study on Information Diffusion Analysis in Social Networks and Its Applications

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximization and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffusion analysis are listed for further study.

Discovering the Hidden Structure of a Social Network: A Semi Supervised Approach

Contagions such as information, rumors, infectious diseases, actions, and influence diffuse as cascades in large networks. Each contagion appears in some node and spreads through the nodes over the underlying network. In most cases, the network structure is hidden and we can only observe the times at which nodes are infected by contagions. So, inferring network structures and analyzing information diffusion processes are required in various domains. The vast majority of existing methods are parametric with the assumption that the diffusion patterns of contagions follow a particular distribution. In this paper, we propose a nonparametric method to infer the network topology, regardless of the contagion propagation model. We consider a static network and a set of given observed cascades, then, we try to infer not only the edges of the network; but also their strengths. First, we model the diffusion network as a Markov decision process (MDP), where the transition probabilities are assumed as pairwise transmission rates between the nodes. Later, a reinforcement learning paradigm is employed to solve this MDP. As compared to other methods, we do not make any assumption about the information diffusion pattern in our method; making it more general purpose. Experimental results on both synthetic and real datasets show that our method outperforms several state-of-the-art methods for different types of network structures and propagation models.

Strategy evolution of information diffusion under time-varying user behavior in generalized networks

Modern generalized networks, where physical devices and social players are combined to support seamless and more flexible services, are strongly driven by user behavior and its evolution. A typical example of behavior-affected processes is the information diffusion by users in generalized networks. In this paper, we focus on user strategy selection with respect to information diffusion in generalized networks, and study its evolutionary dynamics and their benefits, as user behavior/interests change over the course of time. We model information diffusion in generalized networks through graphical evolutionary game theory (graphical EGT) and analyze the evolution of the system and its Evolutionary Stable States (ESS) for time-varying fitness values due to time-varying user interests. We perform extensive evaluations based on numerical and simulation results, and study the impact of several parameters on the derived ESS curves (i.e., ESS points over time). Such parameters involve user interests and their fitness values, and the degree distributions and network type of the layers of the generalized network. We thoroughly evaluate via numerical analysis and appropriate examples the existence of ESS loci for the system dynamics depending on the associated parameters. Also, we discuss the relation between (graphical) EGT-based information diffusion analysis and the epidemics-based information diffusion modeling and analysis that is frequently applied in the literature. Our modeling and evaluations can be used to predict the evolution of the cumulative behavior, allowing for efficient design and control of information campaigns.

User Identification Across Social Media

People use various social media sites for different purposes. The information on each site is often partial. When sources of complementary information are integrated, a better profile of a user can be built. This profile can help improve online services such as advertising across sites. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We provide evidence on the existence of a mapping among identities of individuals across social media sites, study the feasibility of finding this mapping, and illustrate and develop means for finding this mapping. Our studies show that effective approaches that exploit information redundancies due to users’ unique behavioral patterns can be utilized to find such a mapping. This study paves the way for analysis and mining across social networking sites, and facilitates the creation of novel online services across sites. In particular, recommending friends and advertising across networks, analyzing information diffusion across sites, and studying specific user behavior such as user migration across sites in social media are one of the many areas that can benefit from the results of this study.

Analysis of information diffusion for threshold models on arbitrary networks

Diffusion of information via networks has been extensively studied for decades. We study the general threshold model that embraces most of the existing models for information diffusion. In this paper, we first analyze diffusion processes under the linear threshold model, then generalize it into the general threshold model. We give a closed formula for estimating the final cascade size for those models and prove that the actual final cascade size is concentrated around the estimated value, for any network structure with node degrees ω(log n), where n is the number of nodes. Our analysis analytically explains the tipping point phenomenon that is commonly observed in information diffusion processes. Based on the formula, we devise an efficient algorithm for estimating the cascade size for general threshold models on any network with any given initial adopter set. Our algorithm can be employed as a subroutine for numerous algorithms for diffusion analysis such as influence maximization problem. Through experiments on real-world and synthetic networks, we confirm that the actual cascade size is very close to the value computed by our formula and by our algorithm, even when the degrees of the nodes are not so large.

Modeling and performance analysis of information diffusion under information overload in Facebook‐like social networks

SUMMARYResearch on social networks has received remarkable attention, because an increasing number of people use social networks to broadcast information and stay connected with their friends. However, because of the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook‐like social networks into account and proposes models to capture the characters such as the network, the user behaviors, and the process of information diffusion under information overload. The term type influence is introduced to characterize the information diffusion efficiency for users of a given type, which can be analyzed theoretically on the basis of the proposed models. Having noticed the inaccuracy of using type influence to estimate the information diffusion efficiency for a given user, we further introduce the term individual influence and propose a scalable approach to estimate it. We verify the accuracy of this approach by simulations and show that considering more nearby users leads to more computational costs, but more accurate results. Copyright © 2014 John Wiley & Sons, Ltd.

Health Information and the Like—Response

Reddi's thoughtful comments highlight the importance to global health policy of understanding the spread of health behaviors in society. Social media sites and online communication platforms are becoming increasingly popular sources for the dissemination of health information about prevention, medical conditions, and treatments. They are therefore central to the promulgation of beliefs about health threats and the efficacy of health interventions. To explain the critical points of divergence between our results and Reddi's conclusions, we must critically evaluate the scientific evidence. First, ours is a randomized experiment, whereas the studies to which Reddi refers rely on observational data to make claims about information diffusion (the authors of those studies are careful to note that their evidence is predictive rather than causal). Our approach, which randomly manipulates the ratings of online content, holds author and content effects constant, isolating the causal effect of upvoting or downvoting on opinion and behavior change. For example, the studies Reddi refers to analyze whether negative or positive content about vaccinations is more likely to be retweeted. They do not exclude several likely alternative explanations of retweeting behavior that may have nothing to do with whether negative tweets are actually changing people's opinions about vaccinations. The majority of Twitter users may already have a negative opinion of vaccinations, inspiring more retweets of such content. The evidence Reddi cites shows that negative information spreads farther and faster. Our experiment shows that negative information is less likely to change people's opinions than positive information. These two claims are not contradictory. In fact, they are complementary pieces of evidence in a broader story of information diffusion and behavior change. Observational data about and simulations of information diffusion are useful for understanding how information and awareness spread ([ 1 ][1], [ 2 ][2]), but causal inference is necessary to understand how information diffusion subsequently changes opinions and behavior ([ 3 ][3]–[ 5 ][4]). Combining causal analysis of behavior change with predictive analysis of information diffusion creates a powerful lens through which to understand the ebbs and flows of behaviors on our planet. Second, analogies between ratings systems and Twitter will always remain loose at best. Collective opinion rating aggregates and presents the opinion of the community about specific content. Voters are likely motivated by the hope that their vote may influence the opinion of other readers. (Re)tweeting information on Twitter is also likely motivated by the desire to maximize publicity for certain information, the desire to become known as a curator of certain information, and the desire for personal exposure. Ratings are anonymous and do not push information to one's “followers,” making these motivations less likely in ratings systems than on Twitter. The influence mechanisms in these scenarios differ as well. Twitter users are not privy to collective opinion on a topic when they tweet about it, whereas they are precisely aware of collective opinion when they vote on or rate an item. Thus, the motivations for diffusing information and rating it likely differ. Perhaps more important, differences in the designs of these systems could drive differences between our results and results from studies of Twitter. Whereas the system we studied enables users to either upvote, downvote, or abstain, there is no feature analogous to the downvote on Twitter. The user can only choose to either retweet or not. Facebook operates similarly: One can like content, but not dislike it. Our results generalize to collective opinion aggregation and ratings systems; results from studies of Twitter likely generalize to microblogging and networked information dissemination systems. 1. [↵][5] 1. E. Adar, 2. L. A. Adamic , in The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (Compiegne, France, 2005), pp. 207–214. 2. [↵][6] 1. S. Aral, 2. M. Van Alstyne , Am. J. Soc. 117, 90 (2011). [OpenUrl][7][CrossRef][8] 3. [↵][9] 1. S. Aral, 2. L. Muchnik, 3. A. Sundararajan , Proc. Natl. Acad. Sci. 106, 21544 (2009). [OpenUrl][10][Abstract/FREE Full Text][11] 4. 1. C. R. Shalizi, 2. A. C. Thomas , Soc. Meth. Res. 40, 211 (2011). [OpenUrl][12][CrossRef][13] 5. [↵][14] 1. S. Aral, 2. L. Muchnik, 3. A. Sundararajan , Network Sci. 1, (2013). 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Graphical Evolutionary Game for Information Diffusion Over Social Networks

Current social networks are of extremely large-scale generating tremendous information flows at every moment. How information diffuse over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the dynamics of information diffusion, which are heavily influenced by network users' decisions, actions and their socio-economic interactions, is generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree and non-uniform degree networks, with the highlight of two special networks, Erd\H{o}s-R\'enyi random network and the Barab\'asi-Albert scale-free network. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and the same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we also conduct experiment on Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks.