On Dynamic Topic Models for Mining Social Media

2018 ◽  
pp. 209-230 ◽  
Author(s):  
Shatha Jaradat ◽  
Mihhail Matskin
Keyword(s):  
Social Media ◽  
Topic Models

scholarly journals Incorporating Social Role Theory into Topic Models for Social Media Content Analysis

2015 ◽  
Vol 27 (4) ◽  
pp. 1032-1044 ◽  
Author(s):  
Wayne Xin Zhao ◽  
Jinpeng Wang ◽  
Yulan He ◽  
Jian-Yun Nie ◽  
Ji-Rong Wen ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Role Theory ◽  
Social Role ◽  
Topic Models ◽  
Media Content ◽  
Media Content Analysis ◽  
Social Role Theory

scholarly journals SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining

2021 ◽  
Author(s):  
Gerhard Hagerer ◽  
◽  
Martin Kirchhoff ◽  
Hannah Danner ◽  
Robert Pesch ◽  
...  
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Topic Models ◽  
Interactive Visualization ◽  
Visualization Toolkit

scholarly journals Discovering Health Topics in Social Media Using Topic Models

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e103408 ◽  
Author(s):  
Michael J. Paul ◽  
Mark Dredze
Keyword(s):  
Social Media ◽  
Topic Models

scholarly journals Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

2017 ◽  
Author(s):  
Redhouane Abdellaoui ◽  
Pierre Foulquié ◽  
Nathalie Texier ◽  
Carole Faviez ◽  
Anita Burgun ◽  
...  
Keyword(s):  
Social Media ◽  
Virtual Communities ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Antidepressant Drug ◽  
Topic Models ◽  
Web Crawler ◽  
Probabilistic Topic Model ◽  
Manual Review ◽  
Model Approach

BACKGROUND Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. OBJECTIVE The aim of this study was to detect messages describing patients’ noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. METHODS We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. RESULTS Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage. The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844). CONCLUSIONS Topic models enabled us to explore patients’ discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

scholarly journals Learning Polylingual Topic Models from Code-Switched Social Media Documents

2014 ◽  
Author(s):  
Nanyun Peng ◽  
Yiming Wang ◽  
Mark Dredze
Keyword(s):  
Social Media ◽  
Topic Models
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