Online self-help groups are increasingly active in social media, which enable a simple and low-threshold exchange, especially for the chronically ill. […]
Experiences are shared with regard to certain therapies; however, those affected often also give each other emotional support, as the suffering pressure is particularly well understood within a self-help group. In addition, patients exchange ideas about solutions to problems that arise in the context of their illness. Therefore, these social media data offer insights into a wide variety of diseases and needs.
This information has the potential to promote patient-centered medical innovations because it reflects the everyday real needs of those affected. However, there is a problem here: the manual processing, evaluation and analysis of these large data sets is practically impossible. However, this can be solved with the help of social media mining, an automated analysis of social media data, often supported by artificial intelligence. Scientists from the University of Witten / Herdecke (UW / H) and from two pharmaceutical companies show methods and use cases of social media mining for the innovation management of the pharmaceutical industry in a now published article in the renowned journal “Drug Discovery Today”.
Social media mining in online support groups can initially be used primarily to identify the description of the needs of patients in their own words in data volumes and to prioritize them with regard to their importance. In a next step, patient groups with similar needs can also be formed from these data and examined further. The results of these analyses can be used in research on patient-centered drugs. At later stages of the development process, you can support recruitment via social media for participation in studies.