Abstract
Existing keyword-based search techniques suffer from limitations owing to unknown, mismatched, and obscure vocabulary. These challenges are particularly prevalent in social media, where slang, jargon, and memetics are abundant. We develop a new technique, Archetype-Based Modeling and Search, that can mitigate these challenges as they are encountered in social media. This technique learns to identify new relevant documents based on a specified set of archetypes from which both vocabulary and relevance information are extracted. We present a case study from the social media data from Reddit, by using authors from /r/Opiates to characterize discourse around opioid use and to find additional relevant authors on this topic.
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Davis, B. D., Sedig, K., & Lizotte, D. J. (2019). Archetype-based modeling and search of social media. Big Data and Cognitive Computing, 3(3), 1–16. https://doi.org/10.3390/bdcc3030044
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