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A Hybrid Deep Learn...
A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health from Social Media Big Data
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- Banna, Md. Hasan Al (författare)
- Department of Computer Science and Engineering, Bangladesh University of Professionals, Dhaka 1216, Bangladesh
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- Ghosh, Tapotosh (författare)
- Department of Computer Science and Engineering, United International University, Dhaka 1209, Bangladesh
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- Nahian, Md. Jaber AL (författare)
- Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka 1212, Bangladesh
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- Kaiser, M. Shamim (författare)
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
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- Mahmud, Mufti (författare)
- Department of Computer Science and Medical Technology Innovation Facility, Nottingham Trent University, Clifton, NG11 8NS Nottingham, U.K.
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- Taher, Kazi Abu (författare)
- Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka 1212, Bangladesh
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- Hossain, Mohammad Shahadat (författare)
- Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh
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- Andersson, Karl (författare)
- Luleå tekniska universitet,Datavetenskap
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 77009-77022
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media. Analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on people’s mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long short-term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
Nyckelord
- COVID-19
- mental health
- depression
- big data
- social media
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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