國立虎尾科技大學 |

Scholarly Communication in the Age of Big Data and Social Media.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Scholarly Communication in the Age of Big Data and Social Media./
作者:	Ke, Qing.
面頁冊數:	1 online resource (168 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
Contained By:	Dissertation Abstracts International79-09A(E).
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355929027

Scholarly Communication in the Age of Big Data and Social Media.
Ke, Qing.

Scholarly Communication in the Age of Big Data and Social Media. - 1 online resource (168 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.

Thesis (Ph.D.)--Indiana University, 2018.

Includes bibliographical references

The increasing availability of big scholarly data and the widespread use of social media have created opportunities for the study of scholarly communication. This dissertation concerns about scholarly communication across three spheres, namely the scientific community, the patented technologies domain, and online social media. The first work examines how scholarly papers are cited over time within the scientific community by focusing on the delayed recognition phenomenon in science. The analysis points out that such phenomenon is not exceptional, as previous literature has repeatedly suggested. I then study factors linked to citation counts of papers, showing their limited ability to explain the variation in citations. An evolution model is also proposed to reproduce the tie strength distribution in scientific collaboration networks. As the domains where paper can be cited are not restricted to the scientific community, next, I look at citations of papers from the patented technologies domain. I present a comparative study on how papers are cited by U.S. patents and by other papers over time, using a newly-created dataset that links non-patent references made by U.S. patents to biomedical papers. My analysis shows that the two types of citations exhibit distinct temporal variations. Finally, while metrics derived from social medial have been argued to provide broader impact and advocated for research evaluation, actors who participate in conversations about science on social media are largely unknown. Motivated by this issue, an approach is proposed to identify scientists on Twitter. The analysis shows that Twitter has been employed by scholars across the disciplinary spectrum and scholarly communication on social media rarely happens across disciplinary boundaries.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355929027Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Scholarly Communication in the Age of Big Data and Social Media.
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502 $a Thesis (Ph.D.)--Indiana University, 2018.
504 $a Includes bibliographical references
520 $a The increasing availability of big scholarly data and the widespread use of social media have created opportunities for the study of scholarly communication. This dissertation concerns about scholarly communication across three spheres, namely the scientific community, the patented technologies domain, and online social media. The first work examines how scholarly papers are cited over time within the scientific community by focusing on the delayed recognition phenomenon in science. The analysis points out that such phenomenon is not exceptional, as previous literature has repeatedly suggested. I then study factors linked to citation counts of papers, showing their limited ability to explain the variation in citations. An evolution model is also proposed to reproduce the tie strength distribution in scientific collaboration networks. As the domains where paper can be cited are not restricted to the scientific community, next, I look at citations of papers from the patented technologies domain. I present a comparative study on how papers are cited by U.S. patents and by other papers over time, using a newly-created dataset that links non-patent references made by U.S. patents to biomedical papers. My analysis shows that the two types of citations exhibit distinct temporal variations. Finally, while metrics derived from social medial have been argued to provide broader impact and advocated for research evaluation, actors who participate in conversations about science on social media are largely unknown. Motivated by this issue, an approach is proposed to identify scientists on Twitter. The analysis shows that Twitter has been employed by scholars across the disciplinary spectrum and scholarly communication on social media rarely happens across disciplinary boundaries.
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