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Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities

As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now...

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Autores principales: Gruzd, Anatoliy, Kumar, Priya, Abul-Fottouh, Deena, Haythornthwaite, Caroline
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731652/
https://www.ncbi.nlm.nih.gov/pubmed/33343085
http://dx.doi.org/10.1007/s10606-020-09376-y
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author Gruzd, Anatoliy
Kumar, Priya
Abul-Fottouh, Deena
Haythornthwaite, Caroline
author_facet Gruzd, Anatoliy
Kumar, Priya
Abul-Fottouh, Deena
Haythornthwaite, Caroline
author_sort Gruzd, Anatoliy
collection PubMed
description As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now used for learning. This paper addresses the challenges of identifying (1) what information, communication, and discursive practices support successful online communities, (2) whether such practices are similar on Twitter and Reddit, and (3) whether machine learning classifiers can be successfully used to analyze larger datasets of learning exchanges. This paper builds on earlier work that used manual coding of learning and exchange in Reddit ‘Ask’ communities to derive a coding schema we refer to as ‘learning in the wild’. This schema of eight categories: explanation with disagreement, agreement, or neutral presentation; socializing with negative, or positive intent; information seeking; providing resources; and comments about forum rules and norms. To compare across media, results from coding Reddit’s AskHistorians are compared to results from coding a sample of #Twitterstorians tweets (n = 594). High agreement between coders affirmed the applicability of the coding schema to this different medium. LIWC lexicon-based text analysis was used to build machine learning classifiers and apply these to code a larger dataset of tweets (n = 69,101). This research shows that the ‘learning in the wild’ coding schema holds across at least two different platforms, and is partially scalable to study larger online learning communities.
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spelling pubmed-77316522020-12-17 Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities Gruzd, Anatoliy Kumar, Priya Abul-Fottouh, Deena Haythornthwaite, Caroline Comput Support Coop Work Article As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now used for learning. This paper addresses the challenges of identifying (1) what information, communication, and discursive practices support successful online communities, (2) whether such practices are similar on Twitter and Reddit, and (3) whether machine learning classifiers can be successfully used to analyze larger datasets of learning exchanges. This paper builds on earlier work that used manual coding of learning and exchange in Reddit ‘Ask’ communities to derive a coding schema we refer to as ‘learning in the wild’. This schema of eight categories: explanation with disagreement, agreement, or neutral presentation; socializing with negative, or positive intent; information seeking; providing resources; and comments about forum rules and norms. To compare across media, results from coding Reddit’s AskHistorians are compared to results from coding a sample of #Twitterstorians tweets (n = 594). High agreement between coders affirmed the applicability of the coding schema to this different medium. LIWC lexicon-based text analysis was used to build machine learning classifiers and apply these to code a larger dataset of tweets (n = 69,101). This research shows that the ‘learning in the wild’ coding schema holds across at least two different platforms, and is partially scalable to study larger online learning communities. Springer Netherlands 2020-06-29 2020 /pmc/articles/PMC7731652/ /pubmed/33343085 http://dx.doi.org/10.1007/s10606-020-09376-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Gruzd, Anatoliy
Kumar, Priya
Abul-Fottouh, Deena
Haythornthwaite, Caroline
Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities
title Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities
title_full Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities
title_fullStr Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities
title_full_unstemmed Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities
title_short Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities
title_sort coding and classifying knowledge exchange on social media: a comparative analysis of the #twitterstorians and askhistorians communities
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731652/
https://www.ncbi.nlm.nih.gov/pubmed/33343085
http://dx.doi.org/10.1007/s10606-020-09376-y
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