Dokument: Information Metrics. Empirical Methods of Information Science
| Titel: | Information Metrics. Empirical Methods of Information Science | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=55273 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20210127-090115-7 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch Deutsch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Dorsch, Isabelle [Autor] | |||||||
| Dateien: |
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| Beitragende: | Prof. Dr. Stock, Wolfgang G. [Gutachter] Prof Dr. Veszelszki, Ágnes [Gutachter] | |||||||
| Stichwörter: | Informationswissenschaft, Informetrie, Social Media, Szientometrie | |||||||
| Dewey Dezimal-Klassifikation: | 000 Informatik, Informationswissenschaft, allgemeine Werke | |||||||
| Beschreibung: | This Thesis examines information content, users, and systems in empirical informetrics, more precisely in its sub-fields social media as well as scientometrics. “Informetrics is the study of the quantitative aspects of information in any form, not just records or bibliographies, and in any social group, not just scientists” (Tague-Sutcliffe, 1992, p. 1). Informetrics can be further divided in various sub-fields, one of them scientometrics. Whereas scientometric research focuses on the analysis of research literature and research evaluation in a more established manner, social media also allows for informetric analyses as a rather young discipline. Informetric analysis generates new insights and brings them into new relations (Stock & Stock, 2013). Social media as well as scientometrics can be analyzed in terms of their published content (e.g., social media postings, research publications) and in terms of their impact (e.g., topics of interest, citations). Likewise, the same or at least similar informetric methodologies can be applied in both research areas.
The Thesis is divided into Part 1 Social Media in Informetrics and Part 2 Scientometrics. How does informetrics in social media research work? How can we measure information content of social media documents? How do social media users describe the content of their documents? (Research Question 1) Social media platforms allow their users not only the creation of textual-content but rather multi-visual media. Photos, (live-streamed) videos, and ephemeral moments became an indispensable part of social media applications like Instagram or YouNow. For textual-content, besides pure text, hashtags for the representation of knowledge exist. Such media are a rich resource for social media (content) analysis. Studies in Part 1 analyze how Instagram users tag their pictures regarding different kinds of pictures and hashtag categories (Chapter 2) and, additionally, in terms of gender-depended differences (Chapter 4). Furthermore, the Instagram users’ hashtagging creation behavior and selection process is investigated (Chapter 3) as well as the motivation (financial gain, fame) to stream content on the social live streaming services (SLSSs) Periscope, Ustream, and YouNow (Chapter 5). How does scientometrics work? Is there a reliable data basis for scientometric studies? How is it possible to analyze research topics? How reliable are the data used for analysis in informetrics? (Research Question 2) Scientometric research evaluation of publication productivity and impact can take place on different units of assessment (Rousseau, Egghe, & Guns, 2018), like on the output of individual researchers (micro level) or institutions (meso level). Digital scientific information services, for example, databases as Web of Science (WoS) or social media platforms like ResearchGate are part of many studies. Chapter 6 introduces a re-interpreted scholarly indicator “visibility.” It is the share of the number of an author’s publications on a certain information service relative to the author’s entire oeuvre based upon his/her personal publication list. Likewise, the “boundedness” for an author’s scientific publication list is introduced (Chapter 7). Chapter 8 focuses on a publication title term topic analysis on the meso level. We analyzed researchers’ opinions on publication productivity, citation impact, and the h-index as well as their knowledge on the h-index (Chapter 9). In the context of scientific publication content, Part 2 Scientometrics, therefore, addresses content, reliability of data, and opinion of the research “producers.” References Rousseau, R., Egghe, L., & Guns, R. (2018). Becoming Metric-wise: A Bibliometric Guide for Researchers. Cambridge, MA: Chandos Publishing, 2018. Stock, W. G., & Stock, M. (2013). Handbook of Information Science. Berlin: De Gruyter Saur. Tague-Sutcliffe, J. (1992). An introduction to Informetrics. Information Processing and Management, 28(1), 1–3. | |||||||
| Lizenz: |  Urheberrechtsschutz | |||||||
| Fachbereich / Einrichtung: | Philosophische Fakultät » Institut für Sprache und Information » Informationswissenschaft | |||||||
| Dokument erstellt am: | 27.01.2021 | |||||||
| Dateien geändert am: | 27.01.2021 | |||||||
| Promotionsantrag am: | 24.11.2020 | |||||||
| Datum der Promotion: | 15.12.2020 |
