Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms

• URL: http://arxiv.org/abs/2407.02662v1
• Date: Tue, 2 Jul 2024 20:51:06 GMT
• Title: Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms
• Authors: Viet Cuong Nguyen, Mini Jain, Abhijat Chauhan, Heather Jaime Soled, Santiago Alvarez Lesmes, Zihang Li, Michael L. Birnbaum, Sunny X. Tang, Srijan Kumar, Munmun De Choudhury,
• Abstract summary: One in five adults in the US lives with a mental illness. Short-form video content has grown to serve as a crucial conduit for disseminating mental health help and resources.
• Score: 19.510446994785667
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Over one in five adults in the US lives with a mental illness. In the face of a shortage of mental health professionals and offline resources, online short-form video content has grown to serve as a crucial conduit for disseminating mental health help and resources. However, the ease of content creation and access also contributes to the spread of misinformation, posing risks to accurate diagnosis and treatment. Detecting and understanding engagement with such content is crucial to mitigating their harmful effects on public health. We perform the first quantitative study of the phenomenon using YouTube Shorts and Bitchute as the sites of study. We contribute MentalMisinfo, a novel labeled mental health misinformation (MHMisinfo) dataset of 739 videos (639 from Youtube and 100 from Bitchute) and 135372 comments in total, using an expert-driven annotation schema. We first found that few-shot in-context learning with large language models (LLMs) are effective in detecting MHMisinfo videos. Next, we discover distinct and potentially alarming linguistic patterns in how audiences engage with MHMisinfo videos through commentary on both video-sharing platforms. Across the two platforms, comments could exacerbate prevailing stigma with some groups showing heightened susceptibility to and alignment with MHMisinfo. We discuss technical and public health-driven adaptive solutions to tackling the "epidemic" of mental health misinformation online.

Related papers

• MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media [31.752563319585196]
  Black box models are inflexible when switching between tasks, and their results typically lack explanations. With the rise of large language models (LLMs), their flexibility has introduced new approaches to the field. In this paper, we introduce the first multi-task Chinese Social Media Interpretable Mental Health Instructions dataset, consisting of 9K samples. We also propose MentalGLM series models, the first open-source LLMs designed for explainable mental health analysis targeting Chinese social media.
  arXiv  Detail & Related papers  (2024-10-14T09:29:27Z)
• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model [7.327234765760251]
  We develop a Knowledge-Guided NTM to predict a short-form video's suicidal thought impact on viewers. Our method also discovers medically relevant topics from videos that are linked to suicidal thought impact. Our method can help platforms understand videos' suicidal thought impacts, thus moderating videos that violate their community guidelines.
  arXiv  Detail & Related papers  (2024-01-11T03:36:47Z)
• 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)
• Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes [79.79659145328856]
  We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation. We make the case that polarization and misinformation adherence are closely tied.
  arXiv  Detail & Related papers  (2022-06-30T12:34:24Z)
• MentSum: A Resource for Exploring Summarization of Mental Health Online Posts [19.247804638955785]
  Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health conditions, express their feelings, and seek help from the community and counselors. Some of these platforms, such as Reachout, are dedicated forums where the users register to seek help. Others such as Reddit provide subreddits where the users publicly but anonymously post their mental health distress. Although posts are of varying length, it is beneficial to provide a short, but informative summary for fast processing by the counselors.
  arXiv  Detail & Related papers  (2022-06-02T03:08:34Z)
• Know it to Defeat it: Exploring Health Rumor Characteristics and Debunking Efforts on Chinese Social Media during COVID-19 Crisis [65.74516068984232]
  We conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site. Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type. We show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies across rumor types and debunkers.
  arXiv  Detail & Related papers  (2021-09-25T14:02:29Z)
• Understanding Health Misinformation Transmission: An Interpretable Deep Learning Approach to Manage Infodemics [6.08461198240039]
  This study proposes a novel interpretable deep learning approach, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD) to predict health misinformation transmission in social media. We select features according to social exchange theory and evaluate GAN-PiWAD on 4,445 misinformation videos. Our findings provide direct implications for social media platforms and policymakers to design proactive interventions to identify misinformation, control transmissions, and manage infodemics.
  arXiv  Detail & Related papers  (2020-12-21T15:49:19Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)
• Assessing Viewer's Mental Health by Detecting Depression in YouTube Videos [6.846274669929093]
  Depression is one of the leading causes of suicide and placing large economic burdens on families and society. In this paper, we develop and test the efficacy of machine learning techniques applied to the content of YouTube videos. Our model can detect depressive videos with an accuracy of 83%.
  arXiv  Detail & Related papers  (2020-07-29T16:17:35Z)

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.