TY - GEN
T1 - A Study on Mental Health Discussion through Reddit
AU - Kamarudin, Nur Shazwani
AU - Beigi, Ghazaleh
AU - Liu, Huan
N1 - Funding Information:
This work is partially supported by UMP-IIUM-UiTM Sustainable Research Collaboration 2020 (Grant No. RDU
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - The massive growth of social media for the past few decades has given a new era to the open web. People are using social media to openly share opinions and also discussed sensitive subjects such as mental health. This paper study the online community on the Reddit discussion forum. We focused on studying linguistic behavior in the online mental health community. We report linguistic analysis in order to understand the similarity and differences of each mental health community. We first study the sentiment per each Reddit community and followed by the topic modeling. By using the well-known Latent Dirichlet Allocation (LDA) method, we extract the most discussed topic for each community. By utilizing all related posts from each subreddits in each community, we establish the following exciting insights: (a) We observe that the headline of the post does not indicate the whole content of the discussion post, (b) we found that sentiment across the mental health community was high on positive compared to negative, and (c) topic distribution for each community varies but there exist similarities among them.
AB - The massive growth of social media for the past few decades has given a new era to the open web. People are using social media to openly share opinions and also discussed sensitive subjects such as mental health. This paper study the online community on the Reddit discussion forum. We focused on studying linguistic behavior in the online mental health community. We report linguistic analysis in order to understand the similarity and differences of each mental health community. We first study the sentiment per each Reddit community and followed by the topic modeling. By using the well-known Latent Dirichlet Allocation (LDA) method, we extract the most discussed topic for each community. By utilizing all related posts from each subreddits in each community, we establish the following exciting insights: (a) We observe that the headline of the post does not indicate the whole content of the discussion post, (b) we found that sentiment across the mental health community was high on positive compared to negative, and (c) topic distribution for each community varies but there exist similarities among them.
KW - mental health
KW - online forum
KW - sentiment analysis
KW - social network
KW - topic modeling
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U2 - 10.1109/ICSECS52883.2021.00122
DO - 10.1109/ICSECS52883.2021.00122
M3 - Conference contribution
AN - SCOPUS:85116119585
T3 - Proceedings - 2021 International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021
SP - 637
EP - 643
BT - Proceedings - 2021 International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021
Y2 - 24 August 2021 through 26 August 2021
ER -