@inproceedings{thukral-etal-2020-identifying,
title = "Identifying pandemic-related stress factors from social-media posts {--} {E}ffects on students and young-adults",
author = "Thukral, Sachin and
Sangwan, Suyash and
Chatterjee, Arnab and
Dey, Lipika",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.23",
doi = "10.18653/v1/2020.nlpcovid19-2.23",
abstract = "The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly. There are currently more than 200 million college students in 186 countries worldwide, affected due to the pandemic. The mode of education has changed suddenly, with the rapid adaptation of e-learning, whereby teaching is undertaken remotely and on digital platforms. This study presents insights gathered from social media posts that were posted by students and young adults during the COVID times. Using statistical and NLP techniques, we analyzed the behavioural issues reported by users themselves in their posts in depression related communities on Reddit. We present methodologies to systematically analyze content using linguistic techniques to find out the stress-inducing factors. Online education, losing jobs, isolation from friends and abusive families emerge as key stress factors",
}
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%0 Conference Proceedings
%T Identifying pandemic-related stress factors from social-media posts – Effects on students and young-adults
%A Thukral, Sachin
%A Sangwan, Suyash
%A Chatterjee, Arnab
%A Dey, Lipika
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F thukral-etal-2020-identifying
%X The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly. There are currently more than 200 million college students in 186 countries worldwide, affected due to the pandemic. The mode of education has changed suddenly, with the rapid adaptation of e-learning, whereby teaching is undertaken remotely and on digital platforms. This study presents insights gathered from social media posts that were posted by students and young adults during the COVID times. Using statistical and NLP techniques, we analyzed the behavioural issues reported by users themselves in their posts in depression related communities on Reddit. We present methodologies to systematically analyze content using linguistic techniques to find out the stress-inducing factors. Online education, losing jobs, isolation from friends and abusive families emerge as key stress factors
%R 10.18653/v1/2020.nlpcovid19-2.23
%U https://aclanthology.org/2020.nlpcovid19-2.23
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.23
Markdown (Informal)
[Identifying pandemic-related stress factors from social-media posts – Effects on students and young-adults](https://aclanthology.org/2020.nlpcovid19-2.23) (Thukral et al., NLP-COVID19 2020)
ACL