Cross-Cultural Differences in Mental Health Expressions on Social Media
- URL: http://arxiv.org/abs/2402.11477v4
- Date: Sun, 09 Feb 2025 02:22:37 GMT
- Title: Cross-Cultural Differences in Mental Health Expressions on Social Media
- Authors: Sunny Rai, Khushi Shelat, Devansh R Jain, Kishen Sivabalan, Young Min Cho, Maitreyi Redkar, Samindara Sawant, Lyle H. Ungar, Sharath Chandra Guntuku,
- Abstract summary: Current understandings of mental health expressions on social media are derived from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts.
We examine mental health posts on Reddit made by individuals geolocated in India to identify variations in social media language specific to the Indian context.
- Score: 11.446351184195647
- License:
- Abstract: Culture moderates the way individuals perceive and express mental distress. Current understandings of mental health expressions on social media, however, are predominantly derived from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. To address this gap, we examine mental health posts on Reddit made by individuals geolocated in India, to identify variations in social media language specific to the Indian context compared to users from Western nations. Our experiments reveal significant psychosocial variations in emotions and temporal orientation. This study demonstrates the potential of social media platforms for identifying cross-cultural differences in mental health expressions (e.g. seeking advice in India vs seeking support by Western users). Significant linguistic variations in online mental health-related language emphasize the importance of developing precision-targeted interventions that are culturally appropriate.
Related papers
- The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.
We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - Emotion fusion for mental illness detection from social media: A survey [16.410940528107115]
Mental illnesses are one of the most prevalent public health problems worldwide.
There has been a growing research interest in the early detection of mental illness by analysing user-generated posts on social media.
According to the correlation between emotions and mental illness, leveraging and fusing emotion information has developed into a valuable research topic.
arXiv Detail & Related papers (2023-04-19T08:28:34Z) - NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental
Health on Social Media [10.342474142256842]
We argue that more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare.
Within the scope of Natural Language Processing (NLP), we explore critical areas of inquiry associated with Causal analysis and Perception mining.
We advocate for a more explainable approach toward modeling computational psychology problems through the lens of language.
arXiv Detail & Related papers (2023-01-26T09:26:01Z) - Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings.
We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels.
We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
arXiv Detail & Related papers (2022-11-14T18:47:26Z) - Affective Idiosyncratic Responses to Music [63.969810774018775]
We develop methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform.
We test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses.
arXiv Detail & Related papers (2022-10-17T19:57:46Z) - 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) - Mental Disorders on Online Social Media Through the Lens of Language and
Behaviour: Analysis and Visualisation [7.133136338850781]
We study the factors that characterise and differentiate social media users affected by mental disorders.
Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary.
We find evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder.
arXiv Detail & Related papers (2022-02-07T15:29:01Z) - Social media emotion macroscopes reflect emotional experiences in
society at large [0.12656629989060433]
Social media generate data on human behaviour at large scales and over long periods of time.
Recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level.
No research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society.
arXiv Detail & Related papers (2021-07-28T09:40:42Z) - Exploring Self-Identified Counseling Expertise in Online Support Forums [26.086207762353336]
We study the differences between interactions with peers and with self-identified mental health professionals.
Our work contributes toward the developing efforts of understanding how health experts engage with health information- and support-seekers in social networks.
arXiv Detail & Related papers (2021-06-24T12:53:07Z) - Learning Triadic Belief Dynamics in Nonverbal Communication from Videos [81.42305032083716]
Nonverbal communication can convey rich social information among agents.
In this paper, we incorporate different nonverbal communication cues to represent, model, learn, and infer agents' mental states.
arXiv Detail & Related papers (2021-04-07T00:52:04Z) - 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)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.