Inferring social ties across heterogenous networks

Tang, Jie; Lou, Tiancheng; Kleinberg, Jon

doi:10.1145/2124295.2124382

Cited by 259 publications

(169 citation statements)

References 20 publications

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“…Compared to prior approaches (Wang et al, 2010;Tang et al, 2012) the profile-based method for the identification of the academic status is simple, but it also fits the available Twitter data that has only a sparse underlying co-author graph. Although the method could only identify the academic status for 17% of the researchers in the sample, a qualitative comparison with the year-based approach indicates much better precision.…”

Section: Discussionmentioning

confidence: 99%

“…Their timeconstrained probabilistic factor graph model achieves an accuracy of more than 80%. Similarly, Tang et al (2012) studied the problem of inferring the type of academic relationship (e.g., advisor-advisee) by learning across heterogeneous networks. They propose a transfer-based factor graph model that learns a predictive function on a source network and infers the type of relationship on a target network.…”

Section: Identifying the Status Of Researchers Within The Academic Himentioning

confidence: 99%

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It’s All About Information? The Following Behaviour of Professors and PhD Students in Computer Science on Twitter

Linek

Hadgu

Hoffmann

et al. 2017

JWS

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Article available under the terms of the CC BY-NC-ND 2.0 licence (https://creativecommons.org/licenses/by-nc-nd/2.0/) eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. ABSTRACTIn this paper we investigate the role of the academic status in the following behaviour of computer scientists on Twitter. Based on a uses and gratifications perspective, we focus on the activity of a Twitter account and the reciprocity of following relationships. We propose that the account activity addresses the users' information motive only, whereas the user's academic status relates to both the information motive and community development (as in peer networking or career planning).Variables were extracted from Twitter user data. We applied a biographical approach to correctly identify the academic status (professor versus PhD student). We calculated a 2 × 2 MANOVA on the influence of the activity of the account and the academic status (on different groups of followers) to differentiate the influence of the information motive versus the motive for community development.Results suggest that for computer scientists Twitter is mainly an information network. However, we found significant effects in the sense of career planning, that is, the accounts of professors had even in the case of low activity a relatively high number of researcher followers -both PhD followers as well as professor followers. Additionally, there was also some weak evidence for community development gratifications in the sense of peer-networking of professors.Overall, we conclude that the academic use of Twitter is not only about information, but also about career planning and networking.

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Section: Discussionmentioning

confidence: 99%

Section: Identifying the Status Of Researchers Within The Academic Himentioning

confidence: 99%

It’s All About Information? The Following Behaviour of Professors and PhD Students in Computer Science on Twitter

Linek

Hadgu

Hoffmann

et al. 2017

JWS

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“…There are also works on inferring the types of relationships. Hopcroft et al (2011) explore the problem of reciprocal relationship prediction and Tang et al (2012) have developed a framework for classifying the type of social relationships by learning across heterogeneous networks. Yang et al (2010) study the retweeting behavior.…”

Section: Problem 2 Active Relationship Mining Given a Partially Labementioning

confidence: 99%

“…Moreover, these methods do not explicitly consider the correlation information between different relationships. Hopcroft et al (2011) explore the problem of reciprocal relationship prediction and Tang et al (2012) have developed a framework for classifying the type of social relationships by learning across heterogeneous networks. Tan et al (2011) have investigated how different types of relationships between users influence the change of users' opinion.…”

Section: Related Workmentioning

confidence: 99%

“…Just to mention a few, there is little doubt that most of our friends are online now. Considerable research has been conducted on social network analysis (Albert and Barabasi 2002;Faloutsos 1999;Newman 2003;Strogatz 2003), dynamic evolution analysis (Kleinberg 2005), social influence analysis (Domingos and Richardson 2001;Kempe et al 2003;Tang et al 2009), social behavior analysis (Roth et al 2010;Tan et al 2010), and social tie analysis (Crandall et al 2010;Hopcroft et al 2011;Leskovec et al 2010;Tang et al 2012). However, most of these works ignore one important fact that makes the online social networks different from the physical social networks, i.e., our physical social networks are colorful ("family members", "colleagues", and "classmates") but the online social networks are still black-and-white: the users merely do not take the time to label the relationships.…”

Section: Introductionmentioning

confidence: 99%

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Actively learning to infer social ties

Zhuang

Tang

et al. 2012

Data Min Knowl Disc

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DOI 10.1007/s10618-012-0274-x 1 23Your article is protected by copyright and all rights are held exclusively by The Author(s). This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s version for posting to your own website or your institution’s repository. You may further deposit the accepted author’s version on a funder’s repository at a funder’s request, provided it is not made publicly available until 12 months after publication

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Social Media, Mining and Profiling in

Liu

2015

The International Encyclopedia of Digital Communication and Society

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The phenomenal rise of social media services in recent years presents new opportunities and challenges to both information consumers and service providers. With its growing popularity, social media has the potential to mine actionable patterns from a large amount of data to understand user behavior and to meet users' information needs. Profiling has the potential to better describe users and their relationships in social media. This entry introduces data mining and profiling in social media and discusses the characteristics of social media data in the context of research that is being undertaken in this area.

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Inferring social ties across heterogenous networks

Cited by 259 publications

References 20 publications

It’s All About Information? The Following Behaviour of Professors and PhD Students in Computer Science on Twitter

It’s All About Information? The Following Behaviour of Professors and PhD Students in Computer Science on Twitter

Actively learning to infer social ties

Social Media, Mining and Profiling in

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