Xuning Tang

Ranking User Influence in Healthcare Social Media

Due to the revolutionary development of Web 2.0 technology, individual users have become major contributors of Web content in online social media. In light of the growing activities, how to measure a user’s influence to other users in online social media becomes increasingly important. This research need is urgent especially in the online healthcare community since positive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence within the online healthcare community. We proposed a new approach to incorporate users’ reply relationship, conversation content and response immediacy which capture both explicit and implicit interaction between users to identify influential users of online healthcare community. A weighted social network is developed to represent the influence between users. We tested our proposed techniques thoroughly on two medical support forums. Two algorithms UserRank and Weighted in-degree are benchmarked with PageRank and in-degree. Experiment results demonstrated the validity and effectiveness of our proposed approaches.

An analysis of user influence ranking algorithms on Dark Web forums

Social media is actively utilized by extremists to spread out their ideologies. While the Internet provides a platform for any users around the world to share their opinions, some opinions in social media can be related to the national security and threatening to others. Given the large volume and exponential growing rate of messages on the social media platforms, it is impossible to analyze the messages by manual effort. An effective way to identify the threat through social media is detecting the influential users automatically. Bu identifying the influential users, we can determine the impact and the neighborhood of these users. In this work, we develop weights to incorporate message content similarity and response immediacy to measure the degree of influence between any two users on a social networking site and integrate the weights with the typical link analysis techniques. In our experiment, we investigate the impact of weights and the basic algorithms (iterative or prestige) on the user influence ranking. The experiment is conducted on the Dark Web forum provided in the ISI-KDD Challenge. The result shows that the weights make substantial impact on the ranking results, especially on the in-degree algorithm.

Identifing influential users in an online healthcare social network

As an important information portal, online healthcare forum are playing an increasingly crucial role in disseminating information and offering support to people. It connects people with the leading medical experts and others who have similar experiences. During an epidemic outbreak, such as H1N1, it is critical for the health department to understand how the public is responding to the ongoing pandemic, which has a great impact on the social stability. In this case, identifying influential users in the online healthcare forum and tracking the information spreading in such online community can be an effective way to understand the public reaction toward the disease. In this paper, we propose a framework to monitor and identify influential users from online healthcare forum. We first develop a mechanism to identify and construct social networks from the discussion board of an online healthcare forum. We propose the UserRank algorithm which combines link analysis and content analysis techniques to identify influential users. We have also conducted an experiment to evaluate our approach on the Swine Flu forum which is a sub-community of a popular online healthcare community, MedHelp (www.medhelp.org). Experimental results show that our technique outperforms PageRank, in-degree and out-degree centrality in identifying influential user from an online healthcare forum.

Identifying Implicit and Explicit Relationships Through User Activities in Social Media

Social commerce has emerged as a new paradigm of commerce due to the advancement and application of Web 2.0 technologies including social media sites. Social media sites provide a valuable opportunity for social interactions between electronic commerce consumers as well as between consumers and businesses. Although the number of users and interactions is large in social media, the social networks extracted from explicit user interactions are usually sparse. Hence, the result obtained through the analysis of the extracted network is not always useful because many potential ties in the social network are not captured by the explicit interactions between users. In this work, we propose a temporal analysis technique to identify implicit relationships that supplement the explicit relationships identified through the social media interaction functions. Our method is based on the homophily theory developed by McPherson, Smith-Lovin, and Cook [31]. We have conducted experiments to evaluate the effectiveness of the identified implicit relationships and the integration of implicit and explicit relationships. The results indicate that our proposed techniques are effective and achieve a higher accuracy. Our results prove the importance of implicit relationships in deriving complete online social networks that are the foundation for understanding online user communities and social network analysis. Our techniques can be applied to improve effectiveness of product and friend recommendation in social commerce.

Identifying implicit relationships between social media users to support social commerce

The Internet is an ideal platform for business-to-consumer (B2C) and business-to-business (B2B) electronic commerce where businesses and consumers conduct commerce activities such as searching for consumer products, promoting business, managing supply chain and making electronic transactions. With the advance of Web 2.0 technologies and the popularity of social media sites, social commerce offers new opportunities of social interaction between electronic commerce consumers as well as social interaction between consumers and e-retailers. The user contributed content provides a tremendous amount of information that may assist in electronic commerce services. Social network analysis and mining has been a powerful tool for electronic commerce vendors and marketing companies to understand the user behavior which is useful for identifying potential customers of their products. However, the capability of social network analysis and mining diminishes when the social network data is incomplete, especially when there are only limited ties available. The social networks extracted from explicit relationships in social media are usually sparse. Many social media users who have similar interest may not have direct interactions with one another or purchase the same products. Therefore, the explicit relationships between electronic commerce users are not sufficient to construct social networks for effective social network analysis and mining. In this work, we propose the temporal analysis techniques to identify implicit relationships for enriching the social network structure. We have conducted an experiment on Digg.com, which is a social media site for users to discover and share content from anywhere of the Web. The experiment shows that the temporal analysis techniques outperform the baseline techniques that only rely on explicit relationships.

Social networks integration and privacy preservation using subgraph generalization

Intelligence and law enforcement force make use of terrorist and criminal social networks to support their investigations such as identifying suspects, terrorist or criminal subgroups, and their communication patterns. Social networks are valuable resources but it is not easy to obtain information to create a complete terrorist or criminal social network. Missing information in a terrorist or criminal social network always diminish the effectiveness of investigation. An individual agency only has a partial terrorist or criminal social network due to their limited information sources. Sharing and integration of social networks between different agencies increase the effectiveness of social network analysis. Unfortunately, information sharing is usually forbidden due to the concern of privacy preservation. In this paper, we introduce the KNN algorithm for subgraph generation and a mechanism to integrate the generalized information to conduct social network analysis. Generalized information such as lengths of the shortest paths, number of nodes on the boundary, and the total number of nodes is constructed for each generalized subgraphs. By utilizing the generalized information shared from other sources, an estimation of distance between nodes is developed to compute closeness centrality. Two experiments have been conducted with random graphs and the Global Salafi Jihad terrorist social network. The result shows that the proposed technique improves the accuracy of closeness centrality measures substantially while protecting the sensitive data.

Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process

Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many community network detection techniques employ a two-stage approach to identify and detect evolutionary relationships between communities of two adjacent time epochs. These techniques often identify communities with high temporal variation, since the two-stage approach detects communities of each epoch independently without considering the continuity of communities across two time epochs. Other techniques require identification of a predefined number of hidden communities which is not realistic in many applications. To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which enables the detection of hidden communities and tracks their evolution simultaneously from a network stream. The number of hidden communities is automatically determined by a temporal Dirichlet process without human intervention. We tested our proposed technique on three different testbeds with results identifying a high performance level when compared to the baseline algorithm.

Dynamic Community Detection with Temporal Dirichlet Process

Research of detecting dynamic communities from network stream has attracted increasingly attention recently. Some of the previous techniques employed a two-stage approach to detect communities. However, since the two-stage approaches detect communities within each epoch independently, the identified communities usually have high temporal variation. Another restriction of the previous techniques is the requirement of predefining the number of hidden communities by a fixed value or within a very narrow range. To overcome these limitations, we propose the Dynamic Stochastic Block model with Temporal Dirichlet Process, which is able to detect communities and track their evolution simultaneously from a network stream. The number of communities is automatically decided by a Recurrent Chinese Restaurant Process without human intervention. In addition, the identified communities exhibit a rich-gets-richer effect and other appealing properties. The experiment results on both simulated dataset and Flickr dataset showed the effectiveness of our proposed technique.

Identifying Dark Web clusters with temporal coherence analysis

Extremists are actively utilizing social media as propaganda to promote their ideologies. Online forums are ideal platforms to draw attention from worldwide Internet users to the timely issues and some opinions in these discussions can be threatening the public safety. It is of great interest for the intelligence to identify clusters on these forums and capture the topics of discussions and their development. Previous work in cluster identification focused on social networks constructed by the direct interactions between users utilizing link analysis techniques. However, the direct interactions between users may only capture one potential relationship between forum users. Users who share common interests may not necessarily interact with each other directly. On the other hand, they may be active in similar events simultaneously. In this paper, we propose a temporal coherence analysis approach to identify clusters of users from the Dark Web data. Users are represented as vectors of activeness and clusters are extracted with the support of temporal coherence analysis. We tested our proposed methods on both synthetic dataset and real world dataset. Using the real-world Dark Web dataset, three clusters were identified and each cluster was also associated with a specific theme. It shows that a cluster of users participating in a theme of discussion can be discovered without using any content analysis but only using temporal analysis.

Social network integration and analysis using a generalization and probabilistic approach for privacy preservation

Social Network Analysis and Mining (SNAM) techniques have drawn significant attention in the recent years due to the popularity of online social media. With the advance of Web 2.0 and SNAM techniques, tools for aggregating, sharing, investigating, and visualizing social network data have been widely explored and developed. SNAM is effective in supporting intelligence and law enforcement force to identify suspects and extract communication patterns of terrorists or criminals. In our previous work, we have shown how social network analysis and visualization techniques are useful in discovering patterns of terrorist social networks. Attribute to the advance of SNAM techniques, relationships among social actors can be visualized through network structures explicitly and implicit patterns can be discovered automatically. Despite the advance of SNAM, the utility of a social network is highly affected by its d completeness. Missing edges or nodes in a social network will reduce the utility of the network. For example, SNAM techniques may not be able to detect groups of social actors if some of the relationships among these social actors are not available. Similarly, SNAM techniques may overestimate the distance between two social actors if some intermediate nodes or edges are missing. Unfortunately, it is common that an organization only have a partial social network due to its limited information sources. In public safety domain, each law enforcement unit has its own criminal social network constructed by the data available from the criminal intelligence and crime database but this network is only a part of the global criminal social network, which can be obtained by integrating criminal social networks from all law enforcement units. However, due to the privacy policy, law enforcement units are not allowed to share the sensitive information of their social network data. A naive and yet practical approach is anonymizing the social network data before publishing or sharing it. However, a modest privacy gains may reduce a substantial SNAM utility. It is a challenge to make a balance between privacy and utility in social network data sharing and integration. In order to share useful information among different organizations without violating the privacy policies and preserving sensitive information, we propose a generalization and probabilistic approach of social network integration in this paper. Particularly, we propose generalizing social networks to preserve privacy and integrating the probabilistic models of the shared information for SNAM. To preserve the identity of sensitive nodes in social network, a simple approach in the literature is removing all node identities. However, it only allows us to investigate of the structural properties of such anonymized social network, but the integration of multiple anonymized social networks will be impossible. To make a balance between privacy and utility, we introduce a social network integration framework which consists of three major steps: (i) constructing generalized sub-graph, (ii) creating generalized information for sharing, and (iii) social networks integration and analysis. We also propose two sub-graph generalization methods namely, edge betweenness based (EBB) and K-nearest neighbor (KNN). We evaluated the effectiveness of these algorithms on the Global Salafi Jihad terrorist social network.