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Increasing numbers of patients are sharing their health-related experiences online in forums, or on social media websites, such as Twitter and Facebook. This largely untapped source of data about patients’ experience of living with disease and its treatment may be useful in deriving drug safety information such as the occurrence, nature and impact of side effects.
Text mining techniques can transform free text into structured data amenable for analysis by automatically recognising mentions of various health conditions and their relationship to a particular medication. These techniques have been used to identify the occurrence of commonly discussed drug adverse events (AEs) from posts on Facebook and Twitter.1 2 They have also been used to identify discussions about benefits of drugs and how these benefits compared with the AEs, other treatment options, costs and complaints about the product.1
A recently published analysis of Twitter posts mentioning prednisolone or prednisone found insomnia and weight gain to be the most frequently discussed side effects.3 However, with the 140 (or more recently 280) character limit per tweet, any side effect information is limited to what can be included and discussed within this space.
HealthUnlocked (HU), Europe’s largest social media network for health that supports patients and healthcare providers, hosts over 700 communities (including the UK’s …
Handling editor Josef S Smolen
Twitter @Arani_Viv, @WGDixon
Contributors WGD conceived the study. AV performed the analyses and drafted the manuscript. MB contributed to the data analysis. GN provided computer science technical support and expertise regarding automated language processing. All authors reviewed and approved the final version of this text.
Funding The work was supported by the Centre for Epidemiology Versus Arthritis (20380). AV is supported by a National Institute for Health and Research (NIHR) funded Academic Clinical Fellowship. MB is funded by an Engineering and Physical Sciences Research Council (EPSRC) PhD fellowship.
Competing interests WGD has received consultancy fees from Google and Bayer, unrelated to this work.
Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.
Patient consent for publication Not required.
Ethics approval University of Manchester Research Ethics Committee, review by Computer Science ethics (ID CS 257).
Provenance and peer review Not commissioned; externally peer reviewed.
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