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Twitter Analysis for Depression on Social Networks based on Sentiment and Stress

Xiaohui Tao, Ravi Dharmalingam, Ji Zhang, Xujuan Zhou, Lin Li, Raj Gururajan
<span title="">2019</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r4xy5vufljfg7iigmhddrhkfxy" style="color: black;">2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC)</a> </i> &nbsp;
Detecting words that express negativity in a social media message is one step towards detecting depressive moods.  ...  We also obtained the stress scores which correlated well with negative sentiment expressed in the content.  ...  Paltoglou et al for allowing the use of their SentStrength and TensiStrength programs for sentiment scoring, and to Dat Quoc Nguyen for use of his jLDADMM program for LDA analysis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/besc48373.2019.8963550">doi:10.1109/besc48373.2019.8963550</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/besc/TaoD0ZLG19.html">dblp:conf/besc/TaoD0ZLG19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xi3piiz7l5csfmrjdhscsbpoou">fatcat:xi3piiz7l5csfmrjdhscsbpoou</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507092637/https://eprints.usq.edu.au/38102/1/besc2019_conference_17.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/78/f8/78f8b77ea93a333a8fc030525fd7ce13b262f459.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/besc48373.2019.8963550"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Detecting Anxiety through Reddit

Judy Hanwen Shen, Frank Rudzicz
<span title="">2017</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ed7pcromqvhzlfpgcbntefttoq" style="color: black;">Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality</a> </i> &nbsp;
Previous investigations into detecting mental illnesses through social media have predominately focused on detecting depression through Twitter corpora (De Choudhury et al., 2013; Resnik et al., 2015;  ...  classify posts related to binary levels of anxiety.  ...  The LDA topic features also perform better than previous results using LDA to detect depression on Twitter (Resnik et al., 2015) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/w17-3107">doi:10.18653/v1/w17-3107</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl-clpsych/ShenR17.html">dblp:conf/acl-clpsych/ShenR17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/espyvkat7vgf7lzotimx3rzfoy">fatcat:espyvkat7vgf7lzotimx3rzfoy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190226235853/http://www.aclweb.org:80/anthology/W17-3107" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f0/40/f0402ec135d97c9f16f59b7b6b43ed3e580561d3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/w17-3107"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

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
<span title="2021-05-17">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Secondly, we explore detection methods used for mental disorders detection including machine learning and deep learning detection methods.  ...  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  ...  [58] applied LDA on non-stressed related tweets from Twitter to detect stress among the users from their tweets and categorized the tweets into two classes stressed and non-stressed user groups.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.08165v1">arXiv:2105.08165v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s6kfnft73zbkvoqbtfqmg7xtlu">fatcat:s6kfnft73zbkvoqbtfqmg7xtlu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210520150716/https://arxiv.org/ftp/arxiv/papers/2105/2105.08165.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/b0/8cb0d70149aeb1a7a367820f5b0933662b92e950.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.08165v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Stress detection using natural language processing and machine learning over social interactions

Tanya Nijhawan, Girija Attigeri, T. Ananthakrishna
<span title="2022-03-20">2022</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pkhnkszyprhb3orbf6g7tqmgiu" style="color: black;">Journal of Big Data</a> </i> &nbsp;
In this paper, we aim to extend sentiment and emotion analysis for detecting the stress of an individual based on the posts and comments shared by him/her on social networking platforms.  ...  Further, these emotions can be used to analyze stress or depression. In conclusion, the ML models and a BERT model have a very good detection rate.  ...  They achieved an accuracy of 57% from SVM and 63% from Naïve-Bayesian. They also emphasized stress detection using big data techniques [29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40537-022-00575-6">doi:10.1186/s40537-022-00575-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/okp6t5bhgrhqviyz7gzjf4ukrq">fatcat:okp6t5bhgrhqviyz7gzjf4ukrq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220324100211/https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-022-00575-6.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/41/58/41588a75ea781fc4a3a277840e0336a2c133beb0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40537-022-00575-6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

COVID-19 Pandemic: Analysis of COVID-19 related tweets (Preprint)

Alaa Abd-Alrazaq, Dari Alhuwail, Mowafa Househ, Mounir Hamdi, Zubair Shah
<span title="2020-03-31">2020</span> <i title="JMIR Publications Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/f42mlbuaivhxrblrv2cbukmx4i" style="color: black;">Journal of Medical Internet Research</a> </i> &nbsp;
This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic.  ...  We leveraged Latent Dirichlet Allocation (LDA) for topic modeling to identify topics discussed in the tweets.  ...  We used the latent Dirichlet allocation (LDA) algorithm from the Python sklearn package. LDA requires a fixed set of topics, where each topic is represented by a set of words.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2196/19016">doi:10.2196/19016</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32287039">pmid:32287039</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sdjwfi3ymrbx5hr7cztpnfs4ka">fatcat:sdjwfi3ymrbx5hr7cztpnfs4ka</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210203222900/https://www.jmir.org/2020/4/e19016//PDF" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7d/90/7d901e98a9c80dd898acd2469422aad05ea58ccc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2196/19016"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Characterizing Diseases and disorders in Gay Users' tweets [article]

Frank Webb, Amir Karami, Vanessa Kitzie
<span title="2018-03-24">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To determine the nature of health-related information shared by men who have sex with men on Twitter, we collected thousands of tweets from 177 active users.  ...  We sampled these tweets using a framework that can be applied to other LGBTQ sub-populations in future research.  ...  For example, people post 500 million tweets per day on Twitter covering different topics from health to politics.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.09134v1">arXiv:1803.09134v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nhcdysf6q5hlzmr66ihozdcpo4">fatcat:nhcdysf6q5hlzmr66ihozdcpo4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191016173220/https://arxiv.org/pdf/1803.09134v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/c6/c7c6ac6370d32d6b65c9b322238df633d4da2b7e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.09134v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

Hamed Jafarzadeh, David J. Pauleen, Ehsan Abedin, Kasuni Weerasinghe, Nazim Taskin, Mustafa Coskun, Rashid Mehmood
<span title="2021-12-15">2021</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders–citizens of a country successful in containing the coronavirus–from the moment COVID-19 became evident  ...  In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time.  ...  , and the latter used Twitter posts to detect stress symptoms related to COVID-19 in the US.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0259882">doi:10.1371/journal.pone.0259882</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34910732">pmid:34910732</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8673617/">pmcid:PMC8673617</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/prx6ap67pvgq3psoln5s6hed34">fatcat:prx6ap67pvgq3psoln5s6hed34</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220114151607/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0259882&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e2/8c/e28c71e6c0331ae4b27faf0a7d186cd7c8e3d909.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0259882"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8673617" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task

Daniel Preoţiuc-Pietro, Maarten Sap, H. Andrew Schwartz, Lyle Ungar
<span title="">2015</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d5ex6ucxtrfz3clshlkh3f6w2q" style="color: black;">Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality</a> </i> &nbsp;
The goal of the shared task was to automatically determine Twitter users who self-reported having one of two mental illnesses: post traumatic stress disorder (PTSD) and depression.  ...  Our system employs user metadata and textual features derived from Twitter posts. To reduce the feature space and avoid data sparsity, we consider several word clustering approaches.  ...  We use the pre-trained Twitter model from the author's website 3 built from 2 billion tweets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3115/v1/w15-1205">doi:10.3115/v1/w15-1205</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/naacl/Preotiuc-Pietro15a.html">dblp:conf/naacl/Preotiuc-Pietro15a</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3e6plukdbjcgdlghfpmgt6gatm">fatcat:3e6plukdbjcgdlghfpmgt6gatm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150608101234/http://m-mitchell.com:80/clpsych2015/pdf/CLPsych05.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5e/70/5e70c1e441d8e74f44a5cc53fc9082932b23e519.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3115/v1/w15-1205"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Application of Machine Learning Methods in Mental Health Detection: A Systematic Review

Rohizah Abd Rahman, Khairuddin Omar, Shahrul Azman Mohd Noah, Mohd Shahrul Nizam Mohd Danuri, Mohammed Ali Al-Garadi
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Coppersmith, Harman, and Dredze 2014 [18] Post- Traumatic Stress Disorder (PTSD) 3200 tweets collected from Twitter.  ...  This research proposed a hybrid model for detecting stress by using user content and social interaction in Twitter.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3029154">doi:10.1109/access.2020.3029154</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kcvohxbugvcubm3yomckyqpobu">fatcat:kcvohxbugvcubm3yomckyqpobu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201007223520/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09214815.pdf?tp=&amp;arnumber=9214815&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/fe/fafe943f21b46bf731baa7c3e9c0f7038892bda4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3029154"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Detection of Depression-Related Posts in Reddit Social Media Forum

Michael M. Tadesse, Hongfei Lin, Bo Xu, Liang Yang
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The key objective of our study is to examine Reddit users' posts to detect any factors that may reveal the depression attitudes of relevant online users.  ...  detection reaching 91% accuracy and 0.93 F1 scores.  ...  [9] applied broader textual features such as LIWC, LDA and frequent 1-3 grams on the Twitter data to examine the personality of the users with selfdeclared post-traumatic stress (PTSD) disorders.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2909180">doi:10.1109/access.2019.2909180</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wxb3onyy7zdsvmk2zsmp77dyci">fatcat:wxb3onyy7zdsvmk2zsmp77dyci</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429043408/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08681445.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/88/ea/88ea9230d23c77fdca3379170f553db7d763bb50.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2909180"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth
<span title="">2017</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s2uqofedx5gzlnmioe3yqc522e" style="color: black;">Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 - ASONAM &#39;17</a> </i> &nbsp;
Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate  ...  Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively.  ...  Another related line of research focused on capturing suicide and self-harm signals from Twitter posts [18] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3110025.3123028">doi:10.1145/3110025.3123028</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29707701">pmid:29707701</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5914530/">pmcid:PMC5914530</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/asunam/YazdavarAEBBTPS17.html">dblp:conf/asunam/YazdavarAEBBTPS17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qjbouaoprndtxacevxfcihjoyy">fatcat:qjbouaoprndtxacevxfcihjoyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180410192723/http://knoesis.wright.edu/sites/default/files/IEEE_Conference%20%2813%29.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8b/05/8b053d14ec9eefb75cc12f8ad28b0fa65bf9aec9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3110025.3123028"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5914530" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media [article]

Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth
<span title="2017-10-16">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate  ...  Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively.  ...  Another related line of research focused on capturing suicide and self-harm signals from Twitter posts [18] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.05429v1">arXiv:1710.05429v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xferiqrs4nfrrpljdcr536r2yu">fatcat:xferiqrs4nfrrpljdcr536r2yu</a> </span>
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Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia

Nala Alahmari, Sarah Alswedani, Ahmed Alzahrani, Iyad Katib, Aiiad Albeshri, Rashid Mehmood
<span title="2022-03-11">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oglosmy3gbhuzobyjit4qalakq" style="color: black;">Sustainability</a> </i> &nbsp;
Specifically, we discover 17 services using machine learning from Twitter data using the Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-services, namely, Prevention, Treatment  ...  The case study focuses on cancer disease in Saudi Arabia using Twitter data in the Arabic language.  ...  automatic value and service detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/su14063313">doi:10.3390/su14063313</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mlgdsks4hfcprj4jlh7fwwwasa">fatcat:mlgdsks4hfcprj4jlh7fwwwasa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220315025920/https://mdpi-res.com/d_attachment/sustainability/sustainability-14-03313/article_deploy/sustainability-14-03313.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/29/24/2924099b0b5f31b48269b86783ff3e68ad55e810.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/su14063313"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

What Makes You Stressed? Finding Reasons From Tweets

Reshmi Gopalakrishna Pillai, Mike Thelwall, Constantin Orasan
<span title="">2018</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5w4agftuibe35hbonabjurfqf4" style="color: black;">Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</a> </i> &nbsp;
Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals.  ...  This paper introduces a novel framework for finding reasons for stress from tweets, analyzing multiple categories for the first time.  ...  Dataset and Annotation Two different datasets of public Twitter posts were collected with the Tweepy API.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/w18-6239">doi:10.18653/v1/w18-6239</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wassa/PillaiTO18.html">dblp:conf/wassa/PillaiTO18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tfdic2crcnennpcifybrduyefu">fatcat:tfdic2crcnennpcifybrduyefu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200509013352/https://www.aclweb.org/anthology/W18-6239.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c6/4c/c64c6549a5f06684b5c78b757bdc5e4723dc6cd3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/w18-6239"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning

Ebtesam Alomari, Iyad Katib, Aiiad Albeshri, Rashid Mehmood
<span title="2021-01-01">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vyslcn4ljzdq3jes5w7fln3qyu" style="color: black;">International Journal of Environmental Research and Public Health</a> </i> &nbsp;
Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020.  ...  This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim  ...  [61] detected stress symptoms related to COVID-19 in the United States.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph18010282">doi:10.3390/ijerph18010282</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33401512">pmid:33401512</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7795453/">pmcid:PMC7795453</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kmfitn5yyvalfb7sadmjr5i4xq">fatcat:kmfitn5yyvalfb7sadmjr5i4xq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210106063907/https://res.mdpi.com/d_attachment/ijerph/ijerph-18-00282/article_deploy/ijerph-18-00282.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ae/9f/ae9fd3a78aef7d6812d8860e8916f789791e1651.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph18010282"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795453" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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