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 Ofrit Lesser

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Average citations per article3.33
Citation Count10
Publication count3
Publication years2013-2017
Available for download1
Average downloads per article969.00
Downloads (cumulative)969
Downloads (12 Months)130
Downloads (6 Weeks)12
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1
December 2017 Computers in Human Behavior: Volume 77 Issue C, December 2017
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 0

The study examines the gendered discourse patterns on a popular online social network, TheMarker Caf, using social network analysis. Overall, the findings strengthen previous analyses that report evidence of men's assertive and dominant discourse style and social role versus women's more cooperative and supportive discourse style. Men wrote more posts, ...
Keywords: Social capital, Popularity, Social network analysis, Social networking site, Gender

2 published by ACM
January 2014 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining: Volume 5 Issue 1, December 2013
Publisher: ACM
Bibliometrics:
Citation Count: 9
Downloads (6 Weeks): 12,   Downloads (12 Months): 130,   Downloads (Overall): 969

Full text available: PDFPDF
Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect ...
Keywords: Academia.edu, Google+, Twitter, Flickr, hidden links, social networks, DBLP, Flixster, YouTube, imbalanced dataset, supervised learning, Facebook, Link prediction, TheMarker Cafe, training set size

3
April 2013 SBP'13: Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 1

Inferring Online Social Networks (OSN) group members may help to evaluate the authenticity of an applicant asking to join a certain group, and secure vulnerable populations online, such as children. We propose machine learning based methods, which associate OSN members' affiliation with virtual groups based on personal, topological, and group ...
Keywords: machine learning, group prediction, social networks



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