A Study of Social Web Data on Buprenorphine Abuse Using Semantic Web Technology

The Specific Aims of this application are to use a paradigmatic approach that combines Semantic Web technology, Natural Language Processing and Machine Learning techniques to:

1. Describe drug users’ knowledge, attitudes, and behaviors related to the non-medical use of Suboxone and Subutex as discussed on Web-based forums.
2. Identify and describe temporal patterns of non-medical use of Suboxone and Subutex as discussed on Web-based forums.

The research was carried out by an interdisciplinary team of members of the Center for Interventions, Treatment and Addictions Research (CITAR) and the Ohio Center of Excellence in Knowledge- enabled Computing (Kno.e.sis) at Wright State University. The research team made significant progress advancing the application of information processing techniques, and identifying drug user knowledge, attitudes and behaviors to inform drug abuse epidemiology, including illicit use of buprenorphine products.