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Towards a Multimodal Analysis to Predict Mental Illness in Twitter Platform

Nurul Nadhrah Kamaruzaman, Universiti Putra Malaysia
2019 International Journal of Advanced Trends in Computer Science and Engineering  
By mining all the data, it could help to determine the current mental state of social media users and detect those who are suffering from mental illness.  ...  This multimodal fusion model is expected to produce an accurate result for the prediction of mental illness among social media users.  ...  The difference in actions and patterns of user interaction expressed in social media can be detected through learning information of social media data based on text mining, social interaction analysis,  ... 
doi:10.30534/ijatcse/2019/1981.42019 fatcat:w4mj5kj66jcx7a22ova56nkrve

Screening for Depressed Individuals by Using Multimodal Social Media Data

Paulo Mann, Aline Paes, Elton H. Matsushima
2021 AAAI Conference on Artificial Intelligence  
An alternative to early detection at clinical attendance is to use ML models trained on annotated social media content to predict whether the person shows depression symptoms or not.  ...  Furthermore, these models can suggest which behavior, on social media, might lead to depression that differs from previously established psychiatric criteria -typically used on clinical consultations (  ...  Potentially, the new methods developed here can also be employed to solve other problems with similar characteristics, namely: (1) multimodal data gathered from social media; (2) set of instances to compose  ... 
dblp:conf/aaai/MannPM21 fatcat:adlf2f2axfg6xgwix7houdhrsq

Multimodal Deep Learning based Framework for Detecting Depression and Suicidal Behaviour by Affective Analysis of Social Media Posts

Anshu Malhotra, Rajni Jindal
2018 EAI Endorsed Transactions on Pervasive Health and Technology  
In this paper, we propose a real time, deep learning based system for affective analysis of a user's online social media posts of multimodal nature, with the objective of detecting onset of depression  ...  METHODS: Joint representations are obtained by fusing the individual vector representations of multiple modalities from user's social media feed: text, image and videos.  ...  multimodal user generated content from social media. .  ... 
doi:10.4108/eai.13-7-2018.164259 fatcat:gapdfp5etbfolpdjqau232dn5q

Social Network Mental Disorders Detection via Online Social Media Mining

Prof. Narinder Kaur and Lakshay Monga
2021 International journal of modern trends in science and technology  
Social Network Mental Disorder Detection" or "SNMD" is an approach to analyse data and retrieve sentiment that it embodies.  ...  Twitter SNMD analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user.  ...  KEYWORDS: Social Media Mining, Sentiment Analysis, Machine Learning. I. INTRODUCTION Social Network Mental Disorders Detection Analysis is a technique widely used in text mining.  ... 
doi:10.46501/ijmtst0701006 fatcat:aatd3f3htzatzjjql6huerzs7q

Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic [article]

Sahraoui Dhelim, Liming Luke Chen, Huansheng Ning, Sajal K Das, Chris Nugent, Devin Burns, Gerard Leavey, Dirk Pesch, Eleanor Bantry-White
2021 arXiv   pre-print
In this paper, we survey the literature of social media analysis for mental disorders detection, with a special focus on the studies conducted in the context of COVID-19 during 2020-2021.  ...  Finally, we discuss the challenges of mental disorder detection using social media data, including the privacy and ethical concerns, as well as the technical challenges of scaling and deploying such systems  ...  However, they covered only a few works of suicidal detection from social media data.  ... 
arXiv:2105.08165v1 fatcat:s6kfnft73zbkvoqbtfqmg7xtlu

See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data [article]

Paulo Mann, Aline Paes, Elton H. Matsushima
2020 arXiv   pre-print
Previous works have already relied on social media data to detect depression on the general population, usually focusing on either posted images or texts or relying on metadata.  ...  However, nowadays, the data shared at social media is a ubiquitous source that can be used to detect the depression symptoms even when the student is not able to afford or search for professional care.  ...  Previous works have also focused on multimodal social media data sources to detect disorders, for example, the relationship between eating disorders and the removal of posts from Instagram (Chancellor,  ... 
arXiv:1912.01131v2 fatcat:dybu2kdgojesvl3f5djamwphta

Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions

Shumaila Aleem, Noor ul Huda, Rashid Amin, Samina Khalid, Sultan S. Alshamrani, Abdullah Alshehri
2022 Electronics  
ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes.  ...  Moreover, it presents an overview to identify the objectives and limitations of different research studies presented in the domain of depression detection.  ...  [59] presented interpretive Multimodal Depression Detection with Hierarchical Attention Network (MDHAN) to detect depressed people on social media.  ... 
doi:10.3390/electronics11071111 fatcat:bx5z4vbqgrd67htkaz6rmt65ou

Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

Guangyao Shen, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, Wenwu Zhu
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Inspired by these, our work aims to make timely depression detection via harvesting social media data.  ...  Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental  ...  Fund of China (2016IM010200), National Natural, and Science Foundation of China (61370023,61521002).  ... 
doi:10.24963/ijcai.2017/536 dblp:conf/ijcai/ShenJNFZHC017 fatcat:lgnqiar5nrfzzfxwf2aurumqpy

Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

Jianlong Zhou, Hamad Zogan, Shuiqiao Yang, Shoaib Jameel, Guandong Xu, Fang Chen
2021 IEEE Transactions on Computational Social Systems  
Multimodal features capture depression cues from emotion, topic, and domain-specific perspectives.  ...  We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia.  ...  Since social media is social by its nature, social patterns can be consequently found in Twitter feeds, for instance, thereby revealing key aspects of mental and emotional disorders.  ... 
doi:10.1109/tcss.2020.3047604 fatcat:n467kkpzujakhfsvldipbdlws4

An Initial Study of Depression Detection on Mandarin Textual through BERT Model

Yung Teck Kiong
2022 14th ACM Web Science Conference 2022  
In the era of advancement of Internet technology, number of social media user is growing exponentially and became part of human lifestyle.  ...  Previous studies demonstrated that the possibility and capability of artificial intelligence technology on analyzing texts on social media for detecting depression tendency.  ...  [22] proposed a multimodal dictionary learning solution for depression detection by continuously harvesting social media content.  ... 
doi:10.1145/3501247.3539015 fatcat:k2kgy7gmdvc7zofvapi6sofn4a

Inferring Social Media Users' Mental Health Status from Multimodal Information

Zhentao Xu, Verónica Pérez-Rosas, Rada Mihalcea
2020 International Conference on Language Resources and Evaluation  
In this paper, we explore the use of multimodal cues present in social media posts to predict users' mental health status.  ...  Worldwide, an increasing number of people are suffering from mental health disorders such as depression and anxiety.  ...  Authors in (Wang and Li, 2015) addressed the identification of sentiment in social media images using both, visual information and contextual network information, including friends' comments and users  ... 
dblp:conf/lrec/XuPM20 fatcat:mqyn3otszvd4fgzonf5hfh2cl4

Affecting reality

Carmen Ng
2019 A Peer-Reviewed Journal About  
This essay emerged from an ongoing project on affective semiotics and social impact game design, in connection with a transnational research project on human-robot interactionsupported by the European  ...  capturing of feelings.  ...  Notes [6] For an overview of conducting multimodal research and the identification of multimodal slices, see Bateman, Wildfeuer, and Hippala, Ch. 7, § 7.1.1 "Media and their canvases" and §7.1.2 "From  ... 
doi:10.7146/aprja.v8i1.115418 fatcat:r6fnqh2xofdhvpg2jong7ccpjq

Deep learning in mental health outcome research: a scoping review

Chang Su, Zhenxing Xu, Jyotishman Pathak, Fei Wang
2020 Translational Psychiatry  
mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data.  ...  ., medical records, behavioral data, social media usage, etc.).  ...  Stress detection ACC = 0.916 Users stress state is closely related to that of his/her friends in social media.  ... 
doi:10.1038/s41398-020-0780-3 pmid:32532967 pmcid:PMC7293215 fatcat:gbdjszebnndt3j4todyw5k2scq

Advances in Emotion Recognition: Link to Depressive Disorder [chapter]

Xiaotong Cheng, Xiaoxia Wang, Tante Ouyang, Zhengzhi Feng
2020 Mental Disorders [Working Title]  
Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder.  ...  Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social  ...  With the advent of social media, social media platforms are becoming a rich source of multimodal affective information, including text, videos, images, and audios. One of them is textual analysis.  ... 
doi:10.5772/intechopen.92019 fatcat:jmss4llbpnfrxcue6bzebsgmby

Modern Views of Machine Learning for Precision Psychiatry [article]

Zhe Sage Chen, Prathamesh Kulkarni, Isaac R. Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu Zhang
2022 arXiv   pre-print
diagnosis of mental disorders.  ...  We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion.  ...  Acknowledgments The research was partially supported from the US National Science Foundation (CBET-1835000 to Z.S.C.), the National Institutes of Health (R01-NS121776 and R01-MH118928 to Z.S.C.).  ... 
arXiv:2204.01607v2 fatcat:coo557v2jzh6debycy3mhccfze
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