@inproceedings{nanomi-arachchige-etal-2021-dataset-research,
title = "A Dataset for Research on Modelling Depression Severity in Online Forum Data",
author = "Nanomi Arachchige, Isuri Anuradha and
Jayasuriya, Vihangi Himaya and
Weerasinghe, Ruvan",
editor = "Djabri, Souhila and
Gimadi, Dinara and
Mihaylova, Tsvetomila and
Nikolova-Koleva, Ivelina",
booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2021",
month = sep,
year = "2021",
address = "Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-srw.20",
pages = "144--153",
abstract = "People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.",
}
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<abstract>People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.</abstract>
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%0 Conference Proceedings
%T A Dataset for Research on Modelling Depression Severity in Online Forum Data
%A Nanomi Arachchige, Isuri Anuradha
%A Jayasuriya, Vihangi Himaya
%A Weerasinghe, Ruvan
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F nanomi-arachchige-etal-2021-dataset-research
%X People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.
%U https://aclanthology.org/2021.ranlp-srw.20
%P 144-153
Markdown (Informal)
[A Dataset for Research on Modelling Depression Severity in Online Forum Data](https://aclanthology.org/2021.ranlp-srw.20) (Nanomi Arachchige et al., RANLP 2021)
ACL