Understanding Topic Duration in Twitter Learning Communities Using Data Mining

Autor/inn/en	Arslan, Okan; Xing, Wanli; Inan, Fethi A.; Du, Hanxiang
Titel	Understanding Topic Duration in Twitter Learning Communities Using Data Mining
Quelle	In: Journal of Computer Assisted Learning, 38 (2022) 2, S.513-525 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Arslan, Okan) ORCID (Xing, Wanli) ORCID (Inan, Fethi A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12633
Schlagwörter	Electronic Learning; Social Media; Networks; Communities of Practice; Information Retrieval; Data Analysis; Professional Development + Suchen Sie Ihr Suchwort? Soziale Medien; Community; Auswertung
Abstract	Background: There has been increasing interest in online professional learning networks in a variety of social media platforms, especially in Twitter. Twitter offers immediacy, personalization, and support of networks to increase professional knowledge and the sense of membership. Knowing the topics discussed in Twitter and the factors that affect the duration of a topic would help to sustain and reconstruct Twitter-based professional learning activities. Objectives: The purpose of this study is to analyse the topics discussed and what factors affect the duration of a specific topic in 6 years within a virtual professional learning network (VPLN) using #Edchat in Twitter, based on media richness features. Methods: Internet-mediated research and digital methods are used for data collection and analysis. Various text, natural language processing, and machine learning algorithms were used along with the quantitative multilevel models. This study examined 504,998 tweets posted by 72,342 unique users by using #Edchat. Results: There were 150 topics discussed over the 6 years and multilevel random intercept regression model revealed that a specific topic discussed in the #Edchat VPLN is discussed longer when it has more tweets, rather than retweets, posted by a high number of different users along with moderate text, high or moderate mentions with more hashtags. Takeaways: The study developed an automated social media richness feature extraction framework that can be adapted for other theoretical applications in educational context. Emergent topics discussed in Twitter among #Edchat VPLN members for professional development were identified. It extends the social media richness theory for educational context and explore factors that affect an online professional learning activity in Twitter. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01