Narrative detection in online patient communities

Although narratives on patient forums are a valuable source of medical information, their systematic detection and analysis has so far been limited to a single study. In this study, we examine whether psycho-linguistic features or document embeddings can aid identification of narratives. We also investigate which features distinguish narratives from other social media posts. This study is the rst to automatically identify the topics discussed in narratives on a patient forum. Our results show that for classifying narratives, character 3-grams outperform psycho-linguistic features and document embeddings. We found that narratives are characterized by the use of past tense, health-related words and rst-person pronouns, whereas non-narrative text is associ-ated with the future tense, emotional support words and second-person pronouns. Topic analysis of the patient narratives uncovered fourteen dierent medical topics, ranging from tumor surgery to side eects. Future work will use these methods to extract experiential patient knowledge from social media.