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Social Media Mining Toolkit (SMMT)
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vas...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korea Genome Organization
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362951/ https://www.ncbi.nlm.nih.gov/pubmed/32634870 http://dx.doi.org/10.5808/GI.2020.18.2.e16 |
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author | Tekumalla, Ramya Banda, Juan M. |
author_facet | Tekumalla, Ramya Banda, Juan M. |
author_sort | Tekumalla, Ramya |
collection | PubMed |
description | There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain. |
format | Online Article Text |
id | pubmed-7362951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Korea Genome Organization |
record_format | MEDLINE/PubMed |
spelling | pubmed-73629512020-07-23 Social Media Mining Toolkit (SMMT) Tekumalla, Ramya Banda, Juan M. Genomics Inform Application Note There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain. Korea Genome Organization 2020-06-15 /pmc/articles/PMC7362951/ /pubmed/32634870 http://dx.doi.org/10.5808/GI.2020.18.2.e16 Text en (c) 2020, Korea Genome Organization (CC) This is an open-access article distributed under the terms of the Creative Commons Attribution license(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Application Note Tekumalla, Ramya Banda, Juan M. Social Media Mining Toolkit (SMMT) |
title | Social Media Mining Toolkit (SMMT) |
title_full | Social Media Mining Toolkit (SMMT) |
title_fullStr | Social Media Mining Toolkit (SMMT) |
title_full_unstemmed | Social Media Mining Toolkit (SMMT) |
title_short | Social Media Mining Toolkit (SMMT) |
title_sort | social media mining toolkit (smmt) |
topic | Application Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362951/ https://www.ncbi.nlm.nih.gov/pubmed/32634870 http://dx.doi.org/10.5808/GI.2020.18.2.e16 |
work_keys_str_mv | AT tekumallaramya socialmediaminingtoolkitsmmt AT bandajuanm socialmediaminingtoolkitsmmt |