Abstract
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.
Highlights
In the last six years, there has been a great influx of research works that describe different types of research works using Twitter and Reddit data, nearly 2,500 papers are found in PubMed [1]
In an attempt to shift the biomedical community into better practices for research transparency and reproducibility, 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
Parallel to tools like SMMT, there are other research groups that are outlining frameworks to streamline the mining of social media like Sarker et al [12], which are complementary to the use and need of this tool
Summary
In the last six years, there has been a great influx of research works that describe different types of research works using Twitter and Reddit data, nearly 2,500 papers are found in PubMed [1]. The data acquisition methodology is different on each study and seldomly reported, a crucial step towards reproducibility of any of their analyses. When it comes to using Twitter data for drug identification and pharmacovigilance tasks, authors of works like [7,8,9] have been consistently releasing publicly available datasets, software tools, and complete Natural Language Processing (NLP) systems with their works. Parallel to tools like SMMT, there are other research groups that are outlining frameworks to streamline the mining of social media like Sarker et al [12], which are complementary to the use and need of this tool
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