Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

• URL: http://arxiv.org/abs/2202.03291v1
• Date: Mon, 7 Feb 2022 15:29:01 GMT
• Title: Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation
• Authors: Esteban A. R\'issola, Mohammad Aliannejadi, Fabio Crestani
• Abstract summary: 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.
• Score: 7.133136338850781
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions' co-occurrence patterns, and online behavioural traits, including social engagement and posting trends. Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit.

Related papers

• The Evolution of Language in Social Media Comments [37.69303106863453]
  This study investigates the linguistic characteristics of user comments over 34 years, focusing on their complexity and temporal shifts. We utilize a dataset of approximately 300 million English comments from eight diverse platforms and topics. Our findings reveal consistent patterns of complexity across social media platforms and topics, characterized by a nearly universal reduction in text length, diminished lexical richness, but decreased repetitiveness.
  arXiv  Detail & Related papers  (2024-06-17T12:03:30Z)
• Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination [6.353109707205691]
  This paper examines the differences between Twitter (X) in the United States and Sina Weibo posts in China on two primary dimensions of affect. We observe that for Twitter users, the variation in emotional intensity is less distinct between negative and positive emotions compared to Weibo users. From language features, we discover that affective expressions are associated with personal life and feelings on Twitter, while on Weibo such discussions are about socio-political topics in the society.
  arXiv  Detail & Related papers  (2024-01-10T16:32:25Z)
• Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers [30.656554495536618]
  We study the relationship between tweet emotion dynamics and mental health disorders. We find that each of the UED metrics studied varied by the user's self-disclosed diagnosis. This work provides important early evidence for how linguistic cues pertaining to emotion dynamics can play a crucial role as biosocial markers for mental illnesses.
  arXiv  Detail & Related papers  (2023-10-26T13:00:26Z)
• Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks [3.570593982494095]
  We look at speech emotion understanding as a perception task which is a more realistic setting. We leverage ComParE rich dataset of multilingual speakers and multi-label regression target of 'emotion share' or perception of that emotion. Our results show that HuBERT-Large with a self-attention-based light-weight sequence model provides 4.6% improvement over the reported baseline.
  arXiv  Detail & Related papers  (2023-08-28T07:11:27Z)
• Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition [48.29355616574199]
  We analyze the transferability of emotion recognition across three different languages--English, Mandarin Chinese, and Cantonese. This study concludes that different language and age groups require specific speech features, thus making cross-lingual inference an unsuitable method.
  arXiv  Detail & Related papers  (2023-06-26T08:48:08Z)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [57.70351255180495]
  We apply a deep-learning-based computer-vision algorithm to a sample of 220 YouTube videos depicting political leaders from 15 different countries. We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
  Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
  arXiv  Detail & Related papers  (2022-12-27T16:08:49Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks [58.24134321728942]
  We compare and identify cognitive aspects on deep neural-based visual lip-reading models. We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
  arXiv  Detail & Related papers  (2021-10-13T05:30:50Z)
• Validating daily social media macroscopes of emotions [0.12656629989060433]
  We run a large-scale survey at an online newspaper to gather daily self-reports of affective states from its users. We compare these with aggregated results of sentiment analysis of user discussions on the same online platform. For both platforms, we find strong correlations between text analysis results and levels of self-reported emotions.
  arXiv  Detail & Related papers  (2021-08-17T14:28:56Z)
• A Framework for the Computational Linguistic Analysis of Dehumanization [52.735780962665814]
  We analyze discussions of LGBTQ people in the New York Times from 1986 to 2015. We find increasingly humanizing descriptions of LGBTQ people over time. The ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
  arXiv  Detail & Related papers  (2020-03-06T03:02:12Z)

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.