Paper Info
Title
A Large-Scale Social Media Corpus for the Detection of Youth Depression (Project Note).
Abstract
Social media is frequently used by youth to share their health and mental issues. Therefore, social media has become a major online resource to study the language used to express issues such as depression and self-harm which can help to identify individuals at risk of harm. Furthermore, depression and suicide are generally closely related especially that depression is the most common symptom associated with self-harm acts such as suicide. In this project, we propose to build a linguistically annotated corpus with the sentiment analysis in order to study the youth behavior through their social media discourse across the MENA region. We plan to create a large-scale dataset of users with self-reported depression messages. Several correlational analyses will be performed to understand the psycho-social-behaviors. We plan to annotate the collected corpus using a team of dedicated annotators from various Arabic countries. Moreover, we will use various natural language processing (NLP) tools and techniques to reveal the linguistic patterns and the sentiments expressed by these tweets. Finally, we will apply machine learning (ML) methods to build behavior prediction tools using the annotated corpus. We believe that the annotated corpus to will be a valuable resource to be used by linguists, sociologists, computer scientists, psychologists, policy makers, etc.
Year
DOI
Venue
2018
10.1016/j.procs.2018.10.483
Procedia Computer Science
Keywords
Field
DocType
Social Media Analysis,Corpus Annotation,Arabic Dialects,Depression
Data science,Social media,Arabic,Computer science,Sentiment analysis,Harm,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
142
1877-0509
0
PageRank 
References 
Authors
0.34
1
1
Name
Order
Citations
PageRank
Wajdi Zaghouani119721.27