Day 4 | Technical Session 5 | January 30, 2021: 10:30 am to 1 pm

Theme: Social Media and Research

The enormous impact and the transformative power of Social Media⎯Twitter, Facebook, LinkedIn, blogs, wikis, and other novel forms of web 2.0 communication channels, on personal lives is well known. It has also been discussed and studied widely in the context of social behavior and politics. But we have only a sketchy understanding of how social media is being used in research and by researchers. The numbers of academic studies have increased over the last couple of years as indicated in the exponential increase in the number of hits in PubMed for ‘Social Media’⎯from around 300 to 3000 from 2007 to 2017. Studies of the uses of social media by researchers have found that there has been a sea change from downright skepticism and rubbishing to harmonius adoption and often gushing praise. There are disciplinary differences. Studies have shown that biosciences and health researchers are the largest group while business and management fields are the smallest in the use of social media for research.

Today, social media is being increasingly deployed in almost every stage of the research life cycle⎯identifying research opportunities, finding collaborators, securing support, reviewing the literature, collecting research data, analyzing research data, disseminating findings, and finally managing the research process.
However primary focus of the use of social media in research has been primarily in the following processes:

  • Dissemination and feedback: Social media have expanded the reach of research communication beyond the hallowed ivory towers of academia to amateur/ordinary people. Through online pages of journals, associations, newsgroups, and direct sharing, it is relatively easy for researchers to reach a broad audience compared to the more “conventional” sharing of knowledge through publishing in scientific journals. Additionally, the rapid dissemination of research findings and the spreading of knowledge to society have increased public interest and involvement in research. This has given rise to citizen science (especially in domains such as agriculture, astronomy, oceanography, ornithology and almost all fields of study) where people participate and add value in different ways, as well. The ability for knowledge and ideas to be shared with an unprecedented speed and magnitude has given social media a special edge. This has made it possible for research findings to have a greater impact and to be rapidly implemented in society. This impact is also being measured. The most commonly used tracking tool Altmetric, tracks nearly all professional and social media outlets on the amount of rumor about an article
  • Critical Review of Existing Articles and Raw Data Sets: One other example of the use of social media for research is its deployment in augmenting the process through review and also offering additional datasets. As an extension of recent developments toward more transparent peer reviewing, several social platforms allow open peer review encouraging readers to critique existing publications in-depth. In addition, users are stimulated to upload raw data sets as well, including negative results that might otherwise never have been published, thereby counteracting the effect of publication bias. This trend is still in its nascent stage.
  • Networking: Social media is used to find collaborators, funders, practitioners, and in some domains (health) patients or patient groups. Social media has created an accessible platform for peer-to-peer discussions and forms an increasingly important networking tool. Depending on the platform used, potential target audiences include professionals as well as user/consumer/patient representatives.
  • Big Data Analytics for Prediction Models and Trends Analysis: Social media outlets have the potential to be used as exponentially growing, observational datasets for research in their respective domains. Examples abound. In a study based on data from Twitter posts (tweets) researchers were able to detect increases and decreases in influenza prevalence with a 85% accuracy. Another example is a study that found that a model that analyzed language expressed on Twitter was better at predicting atherosclerotic heart disease mortality than a model that combined 10 common risk factors such as smoking, diabetes, and hypertension.

Of course there are dangers and pitfalls too: one of them being Dissemination of pseudoscience and fake news through Social Media; and the other is misinterpretation of research.

There is also the risk of genuine research findings to be misinterpreted through selective quotes. Conclusions of research findings are often simplified and overly extrapolated in the media.

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