| Autor: |
Davis, Brent D., Sedig, Kamran, Lizotte, Daniel J. |
| Předmět: |
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| Zdroj: |
Big Data & Cognitive Computing; Sep2019, Vol. 3 Issue 3, p1-16, 16p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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