Scientific literature on Twitter as subject research : preliminary findings based on bibliometric analysis
Notice bibliographique
Résumé
Background and purpose[br/] [br/] Since its launch in 2006, Internet platform has rapidly expanded. Despite being behind services like Facebook (1.3 billion active users) and WhatsApp (500 million), this platform currently has about 284 million active users worldwide, with 500 million Tweets being sent out every day, in more than 35 different languages (Twitter, 2015). In 2014 the eMarketer Consultancy predicted that should reach 300 million monthly active users by 2016, and pass the 400 million mark at the end of 2018. As a phenomenon of the digital era, generates a new type of research data that has received a good deal of attention in the academic literature. It has turned into a popular subject research that has been widely investigated in the academic world in different fields ranging from the Social Sciences to Health Sciences, addressing various questions, methods approaches, and covering multiple data sets. Bruns & Weller (2014) pointed out that scientific publications with Twitter explicitly in the title are as follows: 1,400 on Scopus, 470 on Web of Science (WoS) and another 10,000 available via Google Scholar. In turn, Zimmer & Proferes (2014) conducted a content analysis study with a set of 382 academic publications that used from 2006 to 2012. This study provides the first findings of a bibliometric study which was conducted to describe the scientific literature available on Twitter.[br/][br/] Methods[br/][br/] The bibliometric research was carried out for the indexed literature in the period from 2006 (when was released) until 2014, and only included articles with the word Twitter in the title, abstract and/or keywords. Analysis was conducted on the basis of the results from Scopus database, which has a wider coverage than WoS. The 2,338 documents retrieved were analyzed according to the average growth in number of publications per year, journal titles, authorship, affiliations, countries and subject area.[br/][br/] Findings[br/][br/] The academic literature with as research subject has been an average growth rate of 52.23% per year. Journal titles with more than 10 publications in this subject were as follows: PLOS One (n=46); Computers in Human Behavior (n=37); Journal of Medical Internet Research (n=37); First Monday (n=36); Information Communication and Society (n=35); Public Relations Review (n=34); Estudios Sobre El Mensaje Periodistico (n=19); Social Science Computer Review (n=18); E-content (n=17); Cutting Edge Technologies in Higher Education (n=16); New Media and Society (n=15); Expert Systems with Applications (n=14); ACM Transactions on Intelligent Systems and Technology, Journal of Communication, Government Information Quarterly, and PC World San Francisco CA, 13 articles each; Journal of the American Society for Information Science and Technology, and Online Wilton Connecticut with 12 articles each; American Behavioral Scientist (n=11); Cyberpsychology Behavior and Social Networking, Proceedings of the ASIST Annual Meeting, and Studies in Computational Intelligence, with 10 articles each.[br/][br/][br/] The most prolific authors were H. W. Park with 13 articles and A. Bruns, with 12 ones. The institutions with more than 15 contributions were Yeungnam University (n=30); Queensland University of Technology (n=22); Pennsylvania State University, University of Oxford and University of Toronto (n=18 each); University of Texas at Austin (n=17); University of Maryland and Indiana University (n=16 each); Seoul National University, University College London, Carnegie Mellon University, University at Buffalo State at University of New York, with 15 articles each. The following countries had more than 50 articles: the United States with 912 publications (39% of the total sample); the United Kingdom, 216 (9.23%); Spain, 148 (6.33%); Australia, 119 (5.1%); South Korea, 116 (4.9%); China, 99 (4.23%); Canada, 98 (4.2%); Germany, 65 (2.8%); Japan, 63 (2.7%); and The Netherlands, 51 (2.2%). The subject areas with most contributions are the Social Sciences, with 1,014 articles (43.27% of the total), followed by Computer Science with 826 (35.3%) and Medicine with 327 (13.98%). [br/][br/] Discussion and concluding remarks[br/][br/] The first findings indicated that there was a steady growth in the academic literature which include as subject research in the period from its foundation in 2006 to 2014. There is a long tail of journal titles that have published articles on this subject, with 22 titles leading to more than 10 publications, all of them in English, apart from a single title in Spanish. Two authors stand out in terms of number of articles, and various institutions have made contributions, most of them being universities from North America (United States and Canada) and Europe (United Kingdom, Spain, Germany and The Netherlands), although Asia (China, Japan and South Korea) and Oceania (Australia) also stood out. The findings are continuing to be analyzed for more details such as what subject-areas are being researched, the analytical methodologies that are employed and the tools used, as well as the citation impact and the network from the cited references.[br/][br/] References[br/] Bruns, A. & Weller, K. (2014). Data Analytics – Or: the Pleasures and Perils of Studying Twitter. Aslib Journal of Information Management, 66:3. DOI: http://dx.doi.org/10.1108/AJIM-02-2014-0027. eMarketer Consultancy (2014). Emerging Markets Drive User Growth Worldwide. Accessed 9 Jan. 2015 from: http://www.emarketer.com/Article/Emerging-Markets-Drive-Twitter-User-Growth-Worldwide/1010874#sthash.pxPo8DI3.dpuf. Twitter Inc. (2015). About. Accessed 9 Jan. 2015 from: https://about.twitter.com/company. Zimmer, M. & Proferes, N. J. (2014). A topology of research: disciplines, methods, and ethics. Aslib Journal of Information Management, 66:3, pp. 250-261.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,097 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,076 | 0,131 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,011 | 0,012 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».