Abstract
This research aimed at identifying the trends in the topics of interest of the tweets published by 43 expert professors in the field of ICT and education and the network of their followers and followed in Tweeter, as well as their relationship with the characteristics of that network. With this purpose, NodeXL was employed to import, directly and automatically, 185,517 tweets which gave origin to a network of connections of 49,229 nodes. Data analysis involved social network analysis, text extraction and text mining using NodeXL, Excel and T-Lab. The research hypothesis was that there is a direct correlation between the trends identified in the topics of interest and the characteristics of the network of connections that emerge from the imported tweets. Among the conclusions of the study we can highlight the following: (1) most of the trends identified from the analyzed tweets were related to education and educational technologies that could enhance teaching and learning processes; (2) the text mining procedure applied to the tweets revealed an interesting association between education and technologies; (3) and finally that the analysis of lemmas seems to be more promising than that of hashtags for detecting trends in the tweets.
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Notes
- 1.
This paper was developed within the framework of research project 4-14-5, Dynamics of social networks of teachers in Twitter, founded by the Univ. Tecnológica de Pereira (Colombia).
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Gil Ramírez, H., Guilleumas García, R.M. (2017). Social Networks of Teachers in Twitter. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_11
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