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Unpacking Altmetric Donuts: Content Analysis of Tweets to Scholarly Journal Articles

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The 3rd Altmetric Conference, 28-29 September 2016

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Unpacking Altmetric Donuts: Content Analysis of Tweets to Scholarly Journal Articles

  1. 1. Unpacking Altmetric Donuts: Shinji Mine (Mie University,Japan) Content Analysis of Tweets to Scholarly Journal Articles The 3rd Altmetric Conference, 28-29 September 2016
  2. 2. Introduction • The widespread popularity of altmetrics in scholarly communication • the meaning of altmetric score remains unclear. • The necessity of contextual and content analysis for altmetrics (Priem 2014, Bornmann 2016) • Although previous qualitative studies focused on some aspects of twitter (Sugimoto et. al, submitted), no attempt has been made to deal with both tweet’s content and tweeter’s profile in a study. • This study analyzes both content of tweets to articles and tweeter’s profile to investigate the meaning of altmetrics (Twitter).
  3. 3. Research Questions 1.Who tweets about scholarly journal articles? 2.What exactly are they tweeting about? 3.In terms of content and profile, does the number of altmetrics score make a difference, especially between Altmetric Top 100 articles and random sample articles?
  4. 4. Method: Process of this study Altmetric
 2014 Top 100 Article, Review, Letter 
 in WoS 2014 Altmetric API Article, Review, Letter 
 with DOI Twitter API Article, Review, Letter 
 with twitter post Random Sampled
 Article, Review, Letter Content Analysis 105,087 10,000 1,082 1,082 358,373 1,656,832 560,663 1,279,503 100 No. of docs 100 Stratified sampling
  5. 5. Content Coding Facet 1: Bibliographic Information 1.Read descriptions in each tweet 2.Add 0/1 value Author Name,Article Title,Journal Title, Other Source Title, URL Facet 2: Excerption Abstract, Body Text (BT), Figure, Other Source BT Facet 3: Sharing/Introduction/Presentation Pointer, Summary, Related Information Facet 4: Comment Positive, Negative, Neutral, Critiques, Question,As-a-Authority Facet 5: Others Keywords, Others Profile Coding Facet 6: Tweeter’s Profile 1.Read descriptions in each tweet 2.Google Search 3.Add 0/1 value Academics, Non Academics Tweet Type Coding Original Tweet, Retweet Add 0/1 value * Twitter API data
  6. 6. Tweeter’s Profile & Tweet Type • In both groups, most of tweet were by non academics and retweet. • In random articles, there were more academic tweeters & original tweets than top 100 articles. Top 100 Random 100 0% 25% 50% 75% 100% 69.9% 84.7% 30.1% 15.3% Academics (n=10,000) (n=1,082) Non Academics Top 100 Random 100 0% 25% 50% 75% 100% 52.1% 64.5% 47.9% 35.6% Original Retweet(n=10,000) (n=1,082)
  7. 7. Academics Non Academics 0% 25% 50% 75% 100% 65.3% 58.2% 34.7% 41.8% Academics Non Academics 0% 25% 50% 75% 100% 51.7% 52.4% 48.3% 47.6% Tweet Type by Profile • In top 100 articles, tweet by academics included more original tweets. • Random 100 articles showed similar trends between academics and no academics. Original Tweet Retweet Random 100 Top 100 (n=1,605) (n=8,395) (n=323) (n=758)
  8. 8. 0% 10% 20% 30% 40% 50% 60% Author Article Title Journal Title Other Title URL Abstract Body Text(BT) Figure Other BT Other Title Pointer Summary Related Information Positive Negative Neutral Critiques Question As-a-Authority Keywords Others Author Article Title Journal Title Other Title URL Abstract Body Text(BT) Figure Other BT Other Title Top 100 Random 100 (n=10,000) (n=1,082) Random 100 ’s original tweet Top 100 ’s original tweet Retweet •Original tweets in top 100 articles tended to include more comments and “critiques” tweets. Possible reasons are that these articles attracted much wider attentions in-and-outside academia and are more controversial. •Random 100 articles’ original tweets tended to be “article’s title”, “short expression of article”, or “keywords”. These articles may be less controversial than top 100 articles. Proportion of contents between Top and Random 100
  9. 9. 0%10%20%30%40%50%60% 0% 10% 20% 30% 40% 50% 60% Author Article Title Journal Title Other Title URL Abstract Body Text(BT) Figure Other BT Other Title Pointer Summary Related Information Positive Negative Neutral Critiques Question As-a-Authority Keywords Others Random 100Top 100 Academics Non Academics (n=1,605) (n=8,395) (n=323) (n=758) Non academic’s original tweet Academics ’s original tweet Author Article Title Journal Title Other Title URL Abstract Body Text(BT) Figure Other BT Other Title Pointer Summary RandoTop 100 Academics Non Academics (n=1,605) (n=8,395) (n=323) (n=758) A Article Journa Othe Retweet •Academics: original tweets on “pointer”, “summary”, “positive“, “critiques” than non academics. •Non academics: more “article title”, “journal title”, “abstracts” and “keywords” tweet. •Most of all “summary” by non academics were original tweet. These articles may be more “esoteric” and failed to get social attentions, the publishers of the articles seemed to tweet on articles in their own journals. •Academics: more “bib info” and “critiques”. More excerpted from “body text” but most of all were retweet. •Non academics: Less original tweets in most of categories. But the differences were not so large. Relationship between Tweeter’s profile and Tweet’s contents
  10. 10. Conclusion •Content of tweets became more diverse than that of previous study (Thelwall et. al, 2013). •Two groups with different altmetrics profile were not identical in terms of 1) type and content of tweet and 2) tweeter’s profile. However, the effect of removing retweets is not negligible. •In most of categories, retweets accounted for all tweets regardless of tweeter’s profile. If engagement and impact matters, position of retweets in calculating the score of altmetrics should be carefully considered.
  11. 11. Acknowledgements •This work was supported by JSPS KAKENHI Grant Numbers 26330364(PI:Shinji Mine, Mie University), 26280121(PI:Keiko Kurata, Keio University). • I would like to thank 
 Mamiko Matsubayashi at University of Tsukuba,
 Catherine Williams & Amy Rees at Altmetric.com 
 for their kind help and data provision.

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