This thesis focuses on the development of novel data mining techniques that convert user interactions in social media networks into readable data that would benefit users, companies, and governments. The readable data can either warn of dangerous side effects of pharmaceutical drugs or improve intervention strategies. A weighted model enabled us to represent user activity in the network, that allowed us to reflect user sentiment of a pharmaceutical drug and/or service. The result is an accurate representation of user sentiment. This approach, when modified for specific diseases, drugs, and services, can enable rapid user feedback that can be converted into rapid responses from consumers to industry and government to withdraw possibly dangerous drugs and services from the market or improve said drugs and services. Our approach monitors social media networks in real-time, enabling government and industry to rapidly respond to consumer sentiment of pharmaceutical drugs and services. / <p>QC 20170314</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-203119 |
| Date | January 2017 |
| Creators | Akay, Altug |
| Publisher | KTH, Systemsäkerhet och organisation, Stockholm |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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