Identifying and analyzing researchers on twitter

Hadgu, Asmelash Teka; Jäschke, Robert

doi:10.1145/2615569.2615676

Cited by 32 publications

(34 citation statements)

References 12 publications

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“…They achieved this by abstracting domain-specific features inferred from social theories (such as structural balance, structural hole, and social status). The sparseness of the coauthor network in the dataset by Hadgu and Jäschke (2014) applied in this study indicates that it contains only few scholars in an advisor-advisee relationship. Therefore, both approaches are of limited use.…”

Section: Identifying the Status Of Researchers Within The Academic Himentioning

confidence: 96%

“…Weller et al (2011) discussed different options: identifying tweets that contain scholarly content (which they considered very difficult) or links to scholarly content (cf. Priem and Hemminger, 2010), identifying academics (as Priem and Costello (2010) and Hadgu and Jäschke (2014) did), or identifying scholarly hashtags (as done by Letierce et al, 2010 andWen et al, 2014). To date, no framework to cover all approaches exists and therefore, the sets of Twitter users and tweets analysed in the existing literature are very diverse and hardly comparable.…”

Section: Twitter Usage By Researchersmentioning

confidence: 99%

“…Eysenbach (2011) also pointed out that further research is necessary addressing how academics use Twitter and whether information about collaboration and information exchange among scholars can be used to better judge the academic merit of tweets. Using a machine learning approach, Hadgu and Jäschke (2014) identified a set of 9 191 computer scientists leveraging the fact that most conferences maintain Twitter accounts, whose followers they classified as researchers. The present study is based on the same dataset.…”

Section: Twitter Usage By Researchersmentioning

confidence: 99%

“…Our analysis is based on data on computer scientists using Twitter (Hadgu and Jäschke, 2014). The dataset was collected based on a seed list of 170 Twitter accounts of computer science conferences.…”

Section: Twitter Accounts Of Researchersmentioning

confidence: 99%

“…We base our study on a large dataset of Twitter accounts of computer scientists (Hadgu and Jäschke, 2014). Our study contributes to the understanding of the impact of new ICT on scholarly communication and cooperation.…”

Section: Introductionmentioning

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: Identifying the Status Of Researchers Within The Academic Himentioning

confidence: 96%

Section: Twitter Usage By Researchersmentioning

confidence: 99%

Section: Twitter Usage By Researchersmentioning

confidence: 99%

Section: Twitter Accounts Of Researchersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Scholarly use of social media and altmetrics: A review of the literature

Sugimoto

Work

Larivière

et al. 2017

Asso for Info Science & Tech

443

322

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Social media has become integrated into the fabric of the scholarly communication system in fundamental ways: principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics-that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of two main parts: the first examines the use of social media in academia, examining the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.

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Scholarly Twitter Metrics

Haustein

2019

Springer Handbooks

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Identifying and analyzing researchers on twitter

Cited by 32 publications

References 12 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

Scholarly use of social media and altmetrics: A review of the literature

Scholarly Twitter Metrics

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