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Disciplinary Differences in Selected Scholars' Twitter Transmissions

AEW 5/6/13. Disciplinary Differences in Selected Scholars' Twitter Transmissions. Kim Holmberg 1 and Mike Thelwall 2 1 k.holmberg@wlv.ac.uk , http://kimholmberg.fi | 2 m.thelwall@wlv.ac.uk School of Technology, University of Wolverhampton, UK. Cascades, Islands, or Streams?

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Disciplinary Differences in Selected Scholars' Twitter Transmissions

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  1. AEW 5/6/13 Disciplinary Differences in Selected Scholars' Twitter Transmissions Kim Holmberg1 and Mike Thelwall2 1k.holmberg@wlv.ac.uk, http://kimholmberg.fi| 2 m.thelwall@wlv.ac.uk School of Technology, University of Wolverhampton, UK

  2. Cascades, Islands, or Streams? Time, Topic, and Scholarly Activities in Humanities and Social Science Research Indiana University, Bloomington, USA University of Wolverhampton, UK Université de Montréal, Canada

  3. Cascades, Islands, or Streams? Integrate several datasets representing a broad range of scholarly activities Use methodological and data triangulation to explore the lifecycle of topics within and across a range of scholarly activities Develop transparent tools and techniques to enable future predictive analyses

  4. #Altmetricsis the study and use of non-traditional scholarly impact measures that are based on activity in web-based environments. http://www.ploscollections.org/article/browse/issue/info%3Adoi%2F10.1371%2Fissue.pcol.v02.i19;jsessionid=70DF7B9AD8D7CE819F666E7791D4084E

  5. RQ • This research investigates how researchers in different disciplines use Twitter for scholarly communication with the following research questions: • How are researchers in different disciplines using Twitter for scholarly communication? • What kinds of disciplinary differences are there in the use of Twitter for scholarly communication?

  6. Tweet Retweet or RT @username Message (privat) #Hashtag

  7. DATA Data was collected between 4 March 2012 and 16 October 2012 using Twitter’s API. 1) Twitter restricts the collection of tweets sent by users to approx. 3,200 tweets

  8. METHODS From each discipline a random sample of 200 tweets was selected and these were classified using a multifaceted classification scheme. In facet 1 the communication style was classified and in facet 2 the scientific content, or lack of it, was classified.

  9. FACET 1 • communication style • Retweets were identified by the acronym RT or by some other way that clearly indicated that the tweet was at least a partial copy of a previous tweet. • Conversational tweetswere identified by @-sign followed by a username and were not retweets. • Tweets in the Linkscategory were tweets that were neither retweets nor conversational tweets but contained one or more URLs. • Other- all remaining tweets.

  10. FACET 2 • content • The scholarly communicationcategory contained tweets that were clearly about research-related communication. • Discipline-relevanttweets were clearly about disciplinary communication not directly research related. • Not clearwas for tweets with no clear topic. The topic of the tweets and the scientific content were unclear. • Not about scienceand not about the discipline. Tweets irrelevant to the discipline and research.

  11. RESULTS Figure 1. Communication styles of the tweets in the five different disciplines

  12. RESULTS Figure 2. Scientific content of the tweets in the five different disciplines

  13. RESULTS Figure 3. Scientific content of the tweets by communication type

  14. Tweets were classified by only one researcher. • While facet 1 is fairly straightforward, facet 2 was classified conservatively so that clear evidence was needed for the more scholarly categories1. • The sample is based upon 24-52 researchers per discipline • The disciplinary differences found may be due to the sample of researchers rather than their disciplines. • It may be easier to classify tweets in some disciplines • Some disciplines have more specialist vocabularies (e.g., chemoinformatics) and others discuss issues that are of general interest to society (e.g., sociology). LIMITATIONS 1) In another sample with other disciplines, intercoder agreement in facet 1 was 99.2% and in facet 2 68.9% with Cohen’s Kappa 0.587.

  15. The results suggests that there may be significant differences between disciplines in the extent to which their active users use Twitter for scholarly communication. It seems to be worrying that some disciplines are avoiding Twitter almost completely for scholarly communication despite other disciplines evidently finding it useful for this purpose. CONCLUSIONS

  16. Comparisons between active and ‘lazy’ Twitter users. Closeranalysis of the scientifictweets and possiblerelationshipsbetween the tweets and citations. Qualitativestudyabout the researchers’ ownthoughtsabouthowtheyuse and whattheythinkaboutTwitter. FUTURE

  17. Thank you for listening Kim Holmberg, PhD Statistical Cybermetrics Research GroupUniversity of Wolverhampton, UKK.Holmberg@wlv.ac.uk http://kimholmberg.fi @kholmber Acknowledgements This manuscript is based upon work supported by the international funding initiative Digging into Data. Specifically, funding comes from the National Science Foundation in the United States (Grant No. 1208804), JISC in the United Kingdom, and the Social Sciences and Humanities Research Council of Canada.

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