#ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning.

Background: Due to the high burden of chronic pain, and the detrimental public health consequences of its treatment with opioids, there is a high-priority need to identify effective alternative therapies. Social media is a potentially valuable resource for knowledge about self-reported therapies by chronic pain sufferers.

Methods: We attempted to (a) verify the presence of large-scale chronic pain-related chatter on Twitter, (b) develop natural language processing and machine learning methods for automatically detecting self-disclosures, (c) collect longitudinal data posted by them, and (d) semiautomatically analyze the types of chronic pain-related information reported by them.

Beyond the binaries: reshaping pain communication through arts workshops.

Pain is difficult to communicate and translate into language, yet most social research on pain experience uses questionnaires and semi-structured interviews that rely on words. In addition to the mind/body dualism prevalent in pain medicine in these studies pain communication is characterised by further value-laden binaries such as real/unreal, visible/invisible, and psychological/physical. Starting from the position that research methods play a role in constituting their object, this article examines the potential of participatory arts workshops for developing different versions of pain communication.