Social Media Mining Toolkit (SMMT)
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2020.
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Citing papers per year: 2022: 4.
4 papers.
- 2022A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wavecited by 8
- 2022An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detectioncited by 7
- 2022TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervisioncited by 2
- 2022Identifying epidemic related Tweets using noisy learningcited by 1
- 2022Identifying epidemic related Tweets using noisy learning10 Sep 2022
- 2022An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection20 Jul 2022
- 2022A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave20 Jul 2022
- 2022TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision11 Jul 2022