Related papers: Identifying pandemic-related stress factors from social-media posts -- effects on students and young-adults

Identifying pandemic-related stress factors from social-media posts -- effects on students and young-adults

URL: http://arxiv.org/abs/2012.00333v1
Date: Tue, 1 Dec 2020 08:42:27 GMT
Title: Identifying pandemic-related stress factors from social-media posts -- effects on students and young-adults
Authors: Sachin Thukral, Suyash Sangwan, Arnab Chatterjee, Lipika Dey
Abstract summary: 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.
Score: 2.198430261120653
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 behavioral 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.

Related papers

On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook [21.978924582262263]
Depression is the most widely studied mental health condition. The COVID-19 global pandemic has had a great impact on mental health worldwide. We present a survey on natural language processing (NLP) approaches to modeling depression in social media.
arXiv Detail & Related papers (2024-10-11T13:20:54Z)
Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
arXiv Detail & Related papers (2023-03-23T17:19:26Z)
Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z)
"COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
arXiv Detail & Related papers (2022-06-12T19:41:01Z)
Exploring COVID-19 Related Stressors Using Topic Modeling [0.0]
This study aims to apply natural language processing (NLP) on social media data to identify psychosocial stressors during COVID-19 pandemic. We obtained dataset of 9266 Reddit posts from subreddit rCOVID19_support, from 14th Feb. 2020 to 19th July 2021. Our result presented a dashboard to visualize the trend of prevalence of topics about covid-19 related stressors being discussed on social media platform.
arXiv Detail & Related papers (2022-01-12T20:22:43Z)
The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study [1.74243547444997]
How individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to pandemic and subsequent lockdowns. This study recruited a cohort of 49 students from a U.S. college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. From individual-level Google Search and YouTube histories, we developed 5 signals that can quantify shifts in online behaviors during the pandemic.
arXiv Detail & Related papers (2020-09-05T00:54:57Z)
Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing [8.150081210763567]
The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. Social media data can reveal public perceptions toward how governments and health agencies are handling the pandemic. This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data.
arXiv Detail & Related papers (2020-07-23T17:19:50Z)
How different age groups responded to the COVID-19 pandemic in terms of mobility behaviors: a case study of the United States [6.42356279638324]
Senior communities had a faster response to the outbreak in comparison to young communities, they also had better performance consistency during the pandemic. Our study indicates that senior communities outperformed younger communities in terms of their behavior change.
arXiv Detail & Related papers (2020-07-20T19:49:33Z)
Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
arXiv Detail & Related papers (2020-06-05T02:04:25Z)
Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions [44.92240076313168]
We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
arXiv Detail & Related papers (2020-05-29T09:24:38Z)
The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic [66.80677233314002]
Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
arXiv Detail & Related papers (2020-04-21T13:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.