Dataset Information

Network-driven reputation in online scientific communities.

ABSTRACT: The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.

SUBMITTER: Liao H

PROVIDER: S-EPMC4251832 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Network-driven reputation in online scientific communities.

Liao Hao H Xiao Rui R Cimini Giulio G Medo Matúš M

PloS one 20141202 12

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generate ...[more]

PMID: 25463148

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Project description:BACKGROUND:Online reputation management (ORM) is an emerging practice strategy that emphasizes the systematic and proactive monitoring of online reviews relating to one's professional reputation. OBJECTIVE:We developed this survey project to assess whether radiation oncologists are aware of ORM and how it is utilized in their practices. We hypothesized that ORM is largely unknown by most practicing radiation oncologists and that little time is spent actively managing their reputations. METHODS:An online survey was submitted to 1222 radiation oncologists using the Qualtrics research platform. Physician emails were gathered from the American Society for Radiation Oncology member directory. A total of 85 physicians initiated the survey, whereas 76 physicians completed more than or equal to 94% (15/16) of the survey questions and were subsequently used in our analyses. The survey consisted of 15 questions querying practice demographics, patient satisfaction determination, ORM understanding, and activities to address ORM and 1 question for physicians to opt-in to a US $50 Amazon gift card raffle. The survey data were summarized using a frequency table, and data were analyzed using the Chi-square test, Fisher exact test, and Spearman correlation coefficients. RESULTS:We calculated a 7% (85/1222) response rate for our survey, with a completion rate of 89% (76/85). A majority of respondents (97%, 74/76) endorsed being somewhat or strongly concerned about patient satisfaction (P<.001). However, 58% (44/76) of respondents reported spending 0 hours per week reviewing or managing their online reputation and 39% (30/76) reported spending less than 1 hour per week (P<.001). A majority of physicians (58%, 44/76) endorsed no familiarity with ORM (P<.001) and 70% (53/76) did not actively manage their online reputation (P<.001). Although 83% (63/76) of respondents strongly or somewhat believed that patients read online reviews (P<.001), 57% (43/76) of respondents did not check their online reviews (P=.25) and 80% (61/76) endorsed never responding to online reviews (P<.001). Moreover, 58% (44/76) of the respondents strongly or somewhat supported the idea of managing their online reputation going forward (P=.001). In addition, 11 out of the 28 pairs of questions asked in our correlation studies reached statistical significance. Degree of concern for patient satisfaction and the notion of managing one's ORM going forward were the 2 most frequently correlated topics of statistical significance in our analyses. CONCLUSIONS:ORM is presently under-recognized in radiation oncology. Although most practitioners are concerned about patient satisfaction, little effort is directed toward the internet on this matter. ORM offers an area of practice improvement for many practicing radiation oncologists.

Dataset Information

Network-driven reputation in online scientific communities.

Publications

Network-driven reputation in online scientific communities.

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