Informational and emotional elements in online support groups: A Bayesian approach to large-scale content analysis
Deetjen U., Powell JA.
© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. Objective This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature. Methods Large-scale qualitative data were obtained from Dailystrength.org. Based on a hand-coded training dataset, all posts were classified into informational or emotional using a Bayesian classification algorithm to generalize the findings. Posts that could not be classified with a probability of at least 75% were excluded. Results The overall tendency toward emotional posts differs by condition: mental health (depression, schizophrenia) and Alzheimer's disease consist of more emotional posts, while informational posts relate more to nonterminal physical conditions (irritable bowel syndrome, diabetes, asthma). There is no gender difference across conditions, although prostate cancer forums are oriented toward informational support, whereas breast cancer forums rather feature emotional support. Across diseases, the best predictors for emotional content are lower age and a higher number of overall posts by the support group member. Discussion The results are in line with previous empirical research and unify empirical findings from single/2-condition research. Limitations include the analytical restriction to predefined categories (informational, emotional) through the chosen machine-learning approach. Conclusion Our findings provide an empirical foundation for building theory on informational versus emotional support across conditions, give insights for practitioners to better understand the role of online support groups for different patients, and show the usefulness of machine-learning approaches to analyze large-scale qualitative health data from online settings.