Detection and analysis of medical misbehavior ...
Document type :
Communication dans un congrès avec actes
Title :
Detection and analysis of medical misbehavior in online forums
Author(s) :
Bigeard, Elise [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Grabar, Natalia [Auteur]

Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Conference title :
SNAMS 2019
City :
Granada
Country :
Espagne
Start date of the conference :
2019-10-22
Book title :
2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)
English keyword(s) :
Natural Language Processing
Text Mining
Social Media
Healthcare domain
Drug Non-Compliance
Text Mining
Social Media
Healthcare domain
Drug Non-Compliance
HAL domain(s) :
Sciences du Vivant [q-bio]
Informatique [cs]
Informatique [cs]
English abstract : [en]
Social media is an important source of information on behaviour and habits of users. It has been used as such in public health research to monitor adverse drug effects and drug misuse among others. We propose to study drug ...
Show more >Social media is an important source of information on behaviour and habits of users. It has been used as such in public health research to monitor adverse drug effects and drug misuse among others. We propose to study drug non-compliance in health online forums. First, we use supervised classification to detect non-compliance messages and obtain 0.436 of F-measure. Then, we manually analyse the content of the messages to learn what kinds of behaviour can be detected, and to study the effect the social media can have on patient's compliance behaviour.Show less >
Show more >Social media is an important source of information on behaviour and habits of users. It has been used as such in public health research to monitor adverse drug effects and drug misuse among others. We propose to study drug non-compliance in health online forums. First, we use supervised classification to detect non-compliance messages and obtain 0.436 of F-measure. Then, we manually analyse the content of the messages to learn what kinds of behaviour can be detected, and to study the effect the social media can have on patient's compliance behaviour.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Collections :
Source :
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