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Monitoring Tweets for Depression to Detect At-risk Users

Zunaira Jamil, Diana Inkpen, Prasadith Buddhitha, Kenton White
2017 Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality  
We propose an automated system that can identify at-risk users from their public social media activity, more specifically, from Twitter.  ...  Therefore, we only use this classifier to compute the estimated percentage of depressed tweets and to add this value as a feature for the userlevel classifier.  ...  We thank the annotators (Bryan Paget and Sameen Salim) for their time and expertise. We thank the organizers of CLPsych 2015 for providing us access to their datasets.  ... 
doi:10.18653/v1/w17-3104 dblp:conf/acl-clpsych/JamilIBW17 fatcat:glrqxw2n2zfipn6pccu6fc5dwu

Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study

Yulin Hswen, John A Naslund, John S Brownstein, Jared B Hawkins
2018 JMIR Mental Health  
Digital technology could support efforts to detect suicide risk and inform suicide prevention efforts.  ...  Among all users, tweets about suicide were associated with tweets about depression (r=0.62, P<.001) and anxiety (r=0.45, P<.001).  ...  The funders played no role in the study design; collection, analysis, or interpretation of data; writing of the manuscript; or decision to submit the manuscript for publication.  ... 
doi:10.2196/11483 pmid:30545811 pmcid:PMC6315229 fatcat:lsiac5uucvhgdaokym5fwodiji

Detection of suicide-related posts in Twitter data streams

M. J. Vioules, B. Moulahi, J. Aze, S. Bringay
2018 IBM Journal of Research and Development  
Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.  ...  In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide-warning signs for individuals and to detect posts containing suicide-related content.  ...  For each set of users, we collected at most the last 50 tweets to create a database of 5,446 tweets of which 2,381 are from distressed users and 3,065 are from everyday users.  ... 
doi:10.1147/jrd.2017.2768678 fatcat:x727xkafofbjdh2t4r4kuy43he

User Centric Social Opinion and Clinical Behavioural Model for Depression Detection

Ayodeji Olusegun Ibitoye, Rantiola Fidelix Famutimi, Dauda Odunayo Olanloye, Ehisuoria Akioyamen
2021 International Journal of Intelligent Information Systems  
tweets with trackable changes in clinical body vitals using wearable device for effective therapy in depressed patient management.  ...  No doubt, each data source can independently predict human depression status; however, the exclusive mutual relationship between both data sources has not been studied for depression detection.  ...  Hence, to properly manage its severity, the need for the development of an automated system with capacity to monitor, detect and recommend appropriate therapy for depressed individuals became essential  ... 
doi:10.11648/j.ijiis.20211004.15 fatcat:pvlfoz25k5eildnsoiyjtme6wi

Social media, big data, and mental health: current advances and ethical implications

Mike Conway, Daniel O'Connor
2016 Current Opinion in Psychology  
Recent work uses sophisticated NLP and ML methods to, for instance, assess suicide risk in pediatric populations based on writing samples [29] , predict depression severity and optimal treatment based  ...  Twitter in particular, due to its public Application Programming Interface 4 and status as a "broadcast" social network 5 , has been used for population-level influenza surveillance [16] [17] [18] , monitoring  ...  Acknowledgments We would like to thank Nicholas Perry (Department of Psychology, University of Utah) and Danielle Mowery (Department of Biomedical Informatics, University of Utah) for their comments on  ... 
doi:10.1016/j.copsyc.2016.01.004 pmid:27042689 pmcid:PMC4815031 fatcat:ylqd2kxjfzaj5kkc7tqp4ejlui

Evaluating behavioral and linguistic changes during drug treatment for depression: a pairwise study using tweets in Spanish (Preprint)

Angela Leis, Francesco Ronzano, Miguel Angel Mayer, Laura I. Furlong, Ferran Sanz
2020 Journal of Medical Internet Research  
Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication.  ...  These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence.  ...  This information can be used as a complementary tool to detect signals of depression and for monitoring and supporting patients using Twitter.  ... 
doi:10.2196/20920 pmid:33337338 fatcat:inxnp4ocnjbwddsz25z4qfxa44

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  
This article studies community depression dynamics due to the COVID-19 pandemic through user-generated content on Twitter.  ...  A new approach based on multimodal features from tweets and term frequency-inverse document frequency (TF-IDF) is proposed to build depression classification models.  ...  Data Set for Depression Model Training In order to detect depression at the tweet level, we created two labeled data sets for both depressed and nondepressed tweets. 1) Positive Tweets (Depressed): We  ... 
doi:10.1109/tcss.2020.3047604 fatcat:n467kkpzujakhfsvldipbdlws4

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

Jianlong Zhou, Hamad Zogan, Shuiqiao Yang, Shoaib Jameel, Guandong Xu, Fang Chen
2020 arXiv   pre-print
allows users to perceive the dynamics of depression over the time.  ...  For instance, depression is one of the most common mental health issues according to the findings made by the World Health Organisation (WHO).  ...  Dataset for depression model training In order to detect depression at the tweet level, we created two labelled datasets for both depressed and non-depressed tweets: • Positive tweets (depressed): we used  ... 
arXiv:2007.02325v1 fatcat:l5z3x7xr5jgexfi6wkftenslom

Depressive Emotion Detection and Behavior Analysis of Men Who Have Sex With Men via Social Media

Yong Li, Mengsi Cai, Shuo Qin, Xin Lu
2020 Frontiers in Psychiatry  
The online MSM depressive emotion detection using machine learning can provide a proper and easy-to-use way in real-world applications, which help identify high-risk individuals at the early stage of depression  ...  With the development of social media and machine learning techniques, MSM depression can be well monitored in an online and easy-to-use manner.  ...  An effective and easy-touse method is provided here for monitoring depressive emotions, which can help identify at-risk individuals in the early stage of depression for further clinical diagnosis.  ... 
doi:10.3389/fpsyt.2020.00830 pmid:32922323 pmcid:PMC7456911 fatcat:tcz2d2x2jfg6vjhvvfpbhtkfmm

Detecting the magnitude of depression in Twitter users using sentiment analysis

Jini Jojo Stephen, Prabu P.
2019 International Journal of Electrical and Computer Engineering (IJECE)  
The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users.  ...  Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user  ...  ACKNOWLEDGEMENTS The author would like to thank mentor and guide, Prof. Dr. Prabu P, for his constant support throughout the lifecycle of this research.  ... 
doi:10.11591/ijece.v9i4.pp3247-3255 fatcat:kerkcg4enndrxnoo7tarm6i7kq

Detecting Stress Based on Social Networking Interactions

hold tweet content and data on social interaction to detect stress.  ...  information for stress detection.  ...  the risk of depression for an individual.  ... 
doi:10.35940/ijitee.k1734.0981119 fatcat:azi6cwvd45ht5afjby3jrqqgmy

Anxious Depression Prediction in Real-time Social Data [article]

Akshi Kumar, Aditi Sharma, Anshika Arora
2019 arXiv   pre-print
In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed.  ...  Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector . An anxiety-related lexicon is built to detect the presence of anxiety indicators.  ...  Tweets can at most contain 280 characters, so users tend to write in short forms.  ... 
arXiv:1903.10222v1 fatcat:qmqj3r4p4nfkvniq4bmprkmiiq

Using Arabic Tweets to Understand Drug Selling Behaviors [article]

Wesam Alruwaili, Bradley Protano, Tejasvi Sirigiriraju, Hamed Alhoori
2019 arXiv   pre-print
Twitter provides a crucial resource for monitoring legal and illegal drug sales in order to support the larger goal of finding ways to protect patient safety.  ...  For predicting tweets selling drugs, Support Vector Machine, yielded the highest accuracy rate (96%), whereas for predicting the legality of the advertised drugs, the Naive Bayes, classifier yielded the  ...  Byrd, Mansurov, and Baysal [14] demonstrated how to use tweets to detect and surveil influenza in a given area at a given time.  ... 
arXiv:1911.01275v1 fatcat:cjaldiqqe5eshnn6w3dtxl3i3m

Monitoring People's Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic

Ali Al-Laith, Mamdouh Alenezi
2021 Information  
The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms.  ...  The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion.  ...  Acknowledgments: Authors are thankful to Prince Sultan University, Saudi Arabia for providing the fund to carry out the work.  ... 
doi:10.3390/info12020086 fatcat:p74ajrqszzczhoricba37ztdxa

Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features

Hatoon S. ALSAGRI, Mourad YKHLEF
2020 IEICE transactions on information and systems  
The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets.  ...  For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets.  ...  Afnan Alwabili from King Faisal Takhassusi Hospital for their greatfull assistance in certifying that Twitter users in the dataset are practicing some depression symptoms.  ... 
doi:10.1587/transinf.2020edp7023 fatcat:znrncd2yrvb2bkd57rj3gpvc4e
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