Articles

Ranking User Influence in Healthcare Social Media

Abstract:

Attribute 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 online healthcare community since positive influence can be beneficial while negative influence may cause negative impacts to other users of the same community. In this paper, a research framework was proposed to study user influence within 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.

Authors:

Xuning Tang
Christopher C. Yang

Publication Venue:

ACM Transactions on Intelligent Systems and Technology, vol. 3, no. 4, Sept. 2012

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TUT: A Statistical Model for Detecting Trends, Topics and User Interests in Social Media

Abstract:

The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users’ interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users’ interests to different trends.

Authors:

Xuning Tang
Christopher C. Yang

Publication Venue:

Proceedings of 21st ACM Conference on Information and Knowledge Management, 2012

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Identifying Implicit and Explicit Relationships Through User Activities in Social Media

Abstract:

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.

Authors:

Christopher C. Yang
Xuning Tang
Qizhi Dai
Ling Jiang

Publication Venue:

International Journal of Electronic Commerce, vol. 18, no.2, Winter, 2013-2014, pp. 73-96

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

Abstract:

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.

Authors:

Xuning Tang
Christopher C. Yang

Publication Venue:

ACM Transactions on Intelligent Systems and Technology, vol.5, no.2, April, 2014

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Understanding Online Consumer Review Opinions with Sentiment Analysis using Machine Learning

Abstract:

With the advent of Web 2.0 technologies, the Web has evolved to become a popular channel of communication and interaction between Web users and online consumers. Social media, unlike traditional media, have rich but unorganized content contributed by users, often in fragmented and sparse fashion. Users usually spend a lot of their time filtering useless information and yet are not able to capture the essence. In this study, we focus on user-contributed reviews of products, which many online consumers use to support their purchase decisions by identifying products that best fit their preferences. In the recent years, sentiment classification and analysis of online consumer reviews has drawn significant research attention. Most existing techniques rely on natural language processing tools to parse and analyze sentences in a review, yet they offer poor accuracy, because the writing in online reviews tends to be less formal than writing in news or journal articles. Many opinion sentences contain grammatical errors and unknown terms that do not exist in dictionaries. Therefore, this study proposes two supervised learning techniques (class association rules and naïve Bayes classifier) to classify opinion sentences into appropriate product feature classes and produce a summary of consumer reviews. An em- pirical evaluation that compares the performance of the class association rules technique and the naïve Bayes classifier for sentiment analysis shows that our proposed techniques achieve more than 70% of the macro and micro F-measures.

Authors:

Christopher C. Yang
Xuning Tang
Y. C. Wong
Chih-Ping Wei

Publication Venue:

Pacific Asia Journal of the Association for Information Systems, vol. 2, no. 3, 2010

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