The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)

Social media content is one of the most visible sources of big data and is often used in health studies to draw inferences about various behaviors. Though much can be gleaned from social media data and mining, the approaches used to collect and analyze data are generally strengthened when examined t...

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Autores principales:	Danny Valdez, Megan S. Patterson, Tyler Prochnow
Formato:	article
Lenguaje:	EN
Publicado:	New Prairie Press 2021
Materias:	social media informatics social network analysis covid-19 Special aspects of education LC8-6691 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/6f546005925d45ba9df5366463efa7ba
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spelling	oai:doaj.org-article:6f546005925d45ba9df5366463efa7ba2021-11-18T18:19:31ZThe Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)10.4148/2572-1836.10982572-1836https://doaj.org/article/6f546005925d45ba9df5366463efa7ba2021-04-01T00:00:00Zhttps://newprairiepress.org/cgi/viewcontent.cgi?article=1098&context=hbrhttps://doaj.org/toc/2572-1836Social media content is one of the most visible sources of big data and is often used in health studies to draw inferences about various behaviors. Though much can be gleaned from social media data and mining, the approaches used to collect and analyze data are generally strengthened when examined through established theoretical frameworks. Health behavior, a theory driven field, encourages interdisciplinary collaboration across fields and theories to help us draw robust conclusions about phenomena. This pilot study uses a combined computer informatics and SNA approach to analyze information spread about mask-wearing as a personal mitigation effort during the COVID-19 pandemic. We analyzed one week’s worth of Twitter data (n= 10,107 tweets across 4,289 users) by using at least one of four popular mask-support hashtags (e.g., #maskup). We calculated network-measures to assess structures and patterns present within the Twitter network, and used exponential random graph modeling (ERGM) to test factors related to the presence of retweets between users. The pro-mask Twitter network was largely fragmented, with a select few nodes occupying the most influential positions in the network. Verified accounts, accounts with more followers, and those who generated more tweets were more likely to be retweeted. Contrarily, verified accounts and those with more followers were less likely to retweet others. SNA revealed patterns and structures theoretically important to how information spreads across Twitter. We demonstrated the utility of an interdisciplinary collaboration between computer informatics and SNA to draw conclusions from social media data.Danny ValdezMegan S. PattersonTyler ProchnowNew Prairie Pressarticlesocial mediainformaticssocial network analysiscovid-19Special aspects of educationLC8-6691Public aspects of medicineRA1-1270ENHealth Behavior Research, Vol 4, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social media informatics social network analysis covid-19 Special aspects of education LC8-6691 Public aspects of medicine RA1-1270
spellingShingle	social media informatics social network analysis covid-19 Special aspects of education LC8-6691 Public aspects of medicine RA1-1270 Danny Valdez Megan S. Patterson Tyler Prochnow The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)
description	Social media content is one of the most visible sources of big data and is often used in health studies to draw inferences about various behaviors. Though much can be gleaned from social media data and mining, the approaches used to collect and analyze data are generally strengthened when examined through established theoretical frameworks. Health behavior, a theory driven field, encourages interdisciplinary collaboration across fields and theories to help us draw robust conclusions about phenomena. This pilot study uses a combined computer informatics and SNA approach to analyze information spread about mask-wearing as a personal mitigation effort during the COVID-19 pandemic. We analyzed one week’s worth of Twitter data (n= 10,107 tweets across 4,289 users) by using at least one of four popular mask-support hashtags (e.g., #maskup). We calculated network-measures to assess structures and patterns present within the Twitter network, and used exponential random graph modeling (ERGM) to test factors related to the presence of retweets between users. The pro-mask Twitter network was largely fragmented, with a select few nodes occupying the most influential positions in the network. Verified accounts, accounts with more followers, and those who generated more tweets were more likely to be retweeted. Contrarily, verified accounts and those with more followers were less likely to retweet others. SNA revealed patterns and structures theoretically important to how information spreads across Twitter. We demonstrated the utility of an interdisciplinary collaboration between computer informatics and SNA to draw conclusions from social media data.
format	article
author	Danny Valdez Megan S. Patterson Tyler Prochnow
author_facet	Danny Valdez Megan S. Patterson Tyler Prochnow
author_sort	Danny Valdez
title	The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)
title_short	The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)
title_full	The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)
title_fullStr	The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)
title_full_unstemmed	The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)
title_sort	importance of interdisciplinary frameworks in social media mining: an exploratory approach between computational informatics and social network analysis (sna)
publisher	New Prairie Press
publishDate	2021
url	https://doaj.org/article/6f546005925d45ba9df5366463efa7ba
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The Importance of Interdisciplinary Frameworks in Social Media Mining: An Exploratory Approach Between Computational Informatics and Social Network Analysis (SNA)

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