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A Research Approach to Detect Unreliable Information in Online Professional Social Networks: Using LinkedIn Mobile as an Example

A Research Approach to Detect Unreliable Information in Online Professional Social Networks: Using LinkedIn Mobile as an Example

Nan Jing, Mengdi Li, Su Zhang
Copyright: © 2015 |Volume: 6 |Issue: 4 |Pages: 18
ISSN: 1947-9158|EISSN: 1947-9166|EISBN13: 9781466678187|DOI: 10.4018/IJHCR.2015100103
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MLA

Jing, Nan, et al. "A Research Approach to Detect Unreliable Information in Online Professional Social Networks: Using LinkedIn Mobile as an Example." IJHCR vol.6, no.4 2015: pp.39-56. http://doi.org/10.4018/IJHCR.2015100103

APA

Jing, N., Li, M., & Zhang, S. (2015). A Research Approach to Detect Unreliable Information in Online Professional Social Networks: Using LinkedIn Mobile as an Example. International Journal of Handheld Computing Research (IJHCR), 6(4), 39-56. http://doi.org/10.4018/IJHCR.2015100103

Chicago

Jing, Nan, Mengdi Li, and Su Zhang. "A Research Approach to Detect Unreliable Information in Online Professional Social Networks: Using LinkedIn Mobile as an Example," International Journal of Handheld Computing Research (IJHCR) 6, no.4: 39-56. http://doi.org/10.4018/IJHCR.2015100103

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Abstract

Professional social network gives companies a platform to post hiring information and locate professional talents. However, the professional network has a great number of users who generate huge amount of information every day, which makes it difficult for the hiring company to distinguish reliability of users' information and evaluate their professional abilities. In this context, this article bases on LinkedIn Mobile as the online professional social network and proposes a research approach to effectively identify unreliable information and evaluate users' abilities. First, the authors look for relevant social network profiles for a cross-site check. Second, on a single professional social networking they site, the authors check the similarity between the user's background and his connections' backgrounds, to detect any possible unreliable information. Third, they propose an algorithm to rank the trustfulness of users' recommendations based on a PageRank algorithm that was traditionally to evaluate the importance of web pages.

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