Propagating online social networks

The rapid development of online social networks (OSN) renders them a popular mechanism for information diffusion. Studying the temporal characteristics is critical in understanding the diffusion process. However, due to the lack of well-defined propagation data, hardly any study addresses the temporal feature of information diffusion in OSN. In this paper, we present a measurement study on information diffusion in the Renren social network. We investigate the latency of information propagation along social links and define the “activation time” for an OSN user, and find that the activation time follows the lognormal distribution. Based on this, we develop two new information diffusion models incorporating asynchronous activation times. Application of the models in the influence maximization problem shows that they capture the temporal diffusion behavior very well. This leads to fundamental ramifications to many related OSN applications.

Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization.
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\nFirstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of “sharing video” behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task.
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\nSecondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well.
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\nFinally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications.

A novel negative feedback information dissemination model based on online social network

We live in a world where information flows very rapidly and people become aware of events on the other side of the world in a matter of seconds; this a consequence of the globalized, fully-connected world we live in. Information spreads via many different channels, but more recently we have witnessed the birth of the information-over-online-social-network phenomena. This means that more and more people get their news from online social networks such Facebook and microblogs such as Twitter. Yet, another source of information are weblogs (or blogs). Bloggers (people who write to blog or own a blog) are capable of influencing a lot of people and they even tend to be sources of information to mainstream news media. This paper delves into an issue relating to the ability of information to spread, but instead of tracking information itself, we look at the infrastructure that is in place linking blogs.We argue that the structure itself is an enabler or disabler of information spread depending on a categorization. This paper categorizes blogs and studies the level of overlap between these categories.

Social networks have become a central feature of everyday life. Most young people are members of at least one online social network, and they naturally provide a great deal of personal information as a condition for participation in the rich online social lives these networks afford. Increasingly, this information is being used as evidence in criminal and even civil legal proceedings. These latter uses, by actors involved in the justice system, are typically justified on the grounds that social network information is essentially public in nature, and thus does not generate a subjective expectation of privacy necessary to support a civil rights-based privacy protection. This justification, however, is based on the perceptions of individuals who are outside the online social network community, rather than reflecting the norms and privacy practices of participants in online social networks. This project takes a user-centric approach to the question of whether online social spaces are public venues, examining of the information-related practices of social network participants, focusing on how they treat their own information and that of others posted in online social spaces. Our results reveal that online social spaces are indeed loci of public display rather than private revelation: online profiles are structured with the view that ‘everyone’ can see them, even if the explicitly intended audience is more limited. These social norms are inconsistent with the claim that social media are private spaces; instead, it appears that participants view and treat online social networks as public venues.

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow us to study social interactions on a scale and at a level of detail that has never before been possible. We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on realworld data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.

Prior research suggests that social connections, including acquaintances, friends, and family, are valuable in a job search process. In these studies, the size of an average job seeker’s network was much smaller and limited by the available modes of communication and the costs associated with maintaining social connections. However, the recent growth of online social networks has enabled job seekers to stay connected with many connections, weak or strong. Thus, the number of online connections—especially weak—has increased significantly. In this paper, we first examine whether an individual’s social network plays a role in driving job search behavior, taking into account online social networking sites (e.g., LinkedIn) and other job search modes. Second, we examine how ties in online social networks (both weak and strong) affect job search outcomes (modeled sequentially as job leads, interviews, and offers), and we compare the findings to job outcomes from traditional job search modes (e.g., career fairs, newspaper, Internet postings, and friends and family). To do so, we first construct an economic model of search behavior incorporating cost and benefit functions; we then estimate the model to recover structural parameters using the survey data of 424 users. Our findings show that users are spending more time searching for jobs on social networking sites. In addition, users’ strong ties play a significant role in job search and are especially helpful in generating job leads, interviews, and offers; the weak ties, on average, are ineffective in generating positive outcomes and marginally negative in some cases. This paper was accepted by Lorin Hitt, information systems.

Traditional marketing models are swiftly being upended by the advent of online social networks. Yet, practicing firms that are engaging with online social networks neither have a reliable theory nor sufficient practical experience to make sense of the phenomenon. Extant theory in particular is based on observations of the real world, and may thus not apply to online social networks. Practicing firms may consequently be misallocating a large amount of resources, simply because they do not know how the online social networks with which they interact are organized. The purpose of this dissertation is to investigate how online social networks that are in stark contrast to real-world social networks behave and how they get organized. In particular, I explore how network structure and information flow within the network impact each other, and how they affect the phenomenon of influence in online social networks. I have collected retrospective data from Twitter conversations about six YouTube product categories (Music, Entertainment, Comedy, Science, Howto and Sports) in continuous time for a period of three months. Measures of network structure (Scale Free Metric, Assortativity and Small World Metric), network flows (Total Paths, Total Shortest Paths, Graph Diameter, Average Path Length, and Average Geodesic Length) and influence (Eigenvector Centrality/Centralization) were computed from the data. Experimental measures such as power law distributions of paths, shortest paths and nodal eigenvector centrality were introduced to account for node-level structure. Factor analysis and regression analysis were used to analyze the data and generate results. The research conducted in this dissertation has yielded three significant findings. 1. Network structure impacts network information flow, and conversely; network flow and network structure impact the network phenomenon of influence. However, the impact of network structure and network flow on influence could not be identified in all instances, suggesting that it cannot be taken for granted. 2. The nature of influence within a social network cannot be understood just by analyzing undirected or directed networks. The behavioral traits of individuals within the network can be deduced by analyzing how information is propagated throughout the network and how it is consumed. 3. An increase or decrease in the scale of a network leads to the observation of different organizational processes, which are most likely driven by very different social phenomena. Social theories that were developed from observing real-world networks of a relatively small scale (hundreds or thousands of people) consequently do not necessarily apply to

The adoption of social analytics heralds the arrival of a new paradigm in political campaign practice, driven by both behavioural science and social networks. Yet these domains have historically been in tension with opposite perspectives on political behaviour. The increasing focus on online social networks offers an opportunity to synthesise complementary research traditions on political behaviour to better understand the relationship between individual personality and information behaviour. We propose a model based on core dispositional personality traits, political participation as information consumption and information diffusion theory to explain online information behaviour during election events. Information diffusion requires users to perform several actions in combination - engage, rate content, comment, share and interpret information received. The research model enables us to observe in experimental settings how personality type guides interaction with and diffusion of political information across online social networks. The learned behaviours and direct responses of participants may help political campaigns in the design of future behavioural targeting strategies that activate social network effects to achieve campaign goals.

This paper aims to identify user-centric features to calculate the strength of social ties between Online Social Network (OSN) users, and models the same using Latent Space Model (LSM). The modeling approach processes a socio-centric user-set as the users are directly (friend) or indirectly (friend-of-friend) related to a seed (target) user, which makes it easier to identify social ties between users as compared to random sampling from a set of diverse OSN users. For a given user, interaction data up to two levels is modeled and analyzed to generate a user-centric social network. Eleven different features related to Facebook have been identified to calculate the strength of social ties between users. LSM is used to visualize relationships in user-centric historical data and to estimate the probability of social ties between OSN users. The users are plotted using LSM in a three-dimensional (3D) social space around a seed user, and a link probability function is devised to calculate the probability of link between any two users with respect to the persona of the seed user. A sphere of influence around each user demarcating its active influence area is also identified and discussed in this paper.

A lot of malicious information such as rumours are hidden in the massive data flow in social online networks. Once this type of malicious information spreads, it could affect social stability in severe cases. This paper introduces the concept of trusted regulator to select key nodes, and we propose a method to hinder the rapid spread of malicious information by blocking accounts and publishing clarification, so that the external disturbances from the chosen nodes and the malicious information could fight against each other. Local control strategy is applied to the propagation of malicious information. Specifically, we first introduce the SIMRT model and an importance indicator based on edge weight, then we propose the reverse intervention algorithm based on the importance indicator. Experiment results on different data sets show that the proposed algorithm can effectively suppress the spread of malicious information.

The purpose of this work is to develop an algorithm and a method for identifying key nodes involved in the dissemination of criminal information within social networks. This study focuses on social network analysis (SNA) metrics that facilitate the detection of influential actors in organized groups, particularly activists who serve as primary disseminators of criminal content. The research objects include both the textual content and metadata of users on social media platforms such as "Vkontakte" and "YouTube." To achieve this goal, an algorithm for detecting nodes that distribute criminal information has been developed. A conceptual model has been constructed, integrating network analysis principles with computational techniques to assess influence. This model introduces a novel framework for evaluating social network nodes based on a combination of structural, semantic, and emotional factors. Specifically, it incorporates influence assessment metrics that consider the heterogeneous nature of content, including its linguistic features, sentiment, and patterns of engagement. Additionally, the model accounts for the emission dynamics of criminal content, allowing for a more precise determination of high-risk nodes within the network. A method for quantifying the influence of social network nodes engaged in criminal content dissemination has been formulated. This method utilizes centrality measures along with content analysis techniques to improve accuracy in detecting key actors. Experimental validation conducted on multiple real-world datasets (including VKontakte groups and known extremist networks) demonstrated that the proposed method achieves an accuracy of up to 80% in identifying the most influential criminal nodes. Compared to baseline centrality-based methods, our approach provides more reliable detection due to the integration of semantic-emotional metrics and emission indicators. The results confirm the practical value of the method in operational scenarios such as the early detection of criminal activity and the prioritization of threat actors for monitoring. These findings have strong implications for real-world applications in law enforcement and cybersecurity. By leveraging advanced algorithmic techniques for social network monitoring, authorities can proactively detect and mitigate the spread of criminal information.

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users' private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs.

Social networks are ubiquitous, which has been one of the important topics of many scholars. With the rapid development of information technology in recent years, people's social platforms are not limited to offline, and online social networks have gradually become an indispensable part of our lives. However, there is still a lack of research on the differences between online and offline social networks. Therefore, this paper probes into the differences between online and offline social network information dissemination by collecting data from online and offline double-layer coupled networks, and comparing and analyzing the data. Research shows that there are some differences between online and offline social networks in the law of information dissemination, which will have a certain impact on real life. Therefore, three suggestions are put forward: 1. At the initial stage of promotion, there are few audience groups, so offline methods can be adopted. With the expansion of information dissemination scope, gradually turning to online promotion, the best promotion effect will be achieved at the fastest speed. 2. The speed and effect of information dissemination can't be ignored for people who have no value in social network information dissemination. 3. Based on the complexity of the public opinion information dissemination network, users need to constantly cultivate their ability to identify information, the management of major social platforms earnestly perform their regulatory responsibilities, and the relevant functional departments of the government gradually establish a public opinion guidance mechanism. If joint efforts can be formed, the public opinion prevention and control effect will be more significant.