Ranking User Influence in Healthcare Social Media

Abstract

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.

Identifying Authorities in Online Communities

“…The authority degree of users is then estimated by simultaneously using the authority scores of nodes in one graph to inform the other, and vice versa, in a mutually reinforcing fashion. Tang and Yang [2012] studied the problem of ranking user influence in online health care community. The proposed approach incorporates users' reply relationships, conversation content and response immediacy to build a weighted social network that represents influence between users.…”

“…It is worth noting that the estimation of the weights of the developed social network de-pends on two user supplied parameters. Experiments in [Tang and Yang 2012] suggest that the variation of the values of these parameters has a substantial impact on the accuracy of results. Agarwal et al [2012] studied the problem of identifying a ranked list of influential users in online community blogs.…”

“…Such approaches can be broadly divided into two categories: ranking-based approaches and attributes-based approaches. Most ranking-based approaches [Kwak et al 2010], [Weng et al 2010], [Zhu et al 2011], [Tang and Yang 2012] calculate some kind of score per user which serves as a measure of the degree of authority. Scores are used in ranking users such that the top K users are considered as authoritative.…”

“…Yet unlabeled data are relatively easy to collect. (3) A certain number of methods [Tang and Yang 2012], [Agarwal et al 2012], [Kao et al 2010], [Zhou et al 2009] rely on some user-supplied parameters in order to estimate the authority score of the participants. However, in many applications the optimal values of these parameters are difficult to determine.…”

Intelligent systems and technology for integrative and predictive medicine

“…Efforts are being devoted to a comprehensive operational framework that will connect networked biomedical components [Tang and Yang 2012;Lampos and Cristianini 2012]. The research will enable networks to seamlessly communicate and result in preventive and predictive practices with the three Ps: Personal, Proactive, and Precise, for example, rational combinatorial treatments, adaptive therapeutics, and patient-oriented disease management.…”