Machine learning models to detect anxiety and depression through social media: A scoping review
Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990022000179 |
| _version_ | 1811198938738327552 |
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| author | Arfan Ahmed Sarah Aziz Carla T. Toro Mahmood Alzubaidi Sara Irshaidat Hashem Abu Serhan Alaa A. Abd-alrazaq Mowafa Househ |
| author_facet | Arfan Ahmed Sarah Aziz Carla T. Toro Mahmood Alzubaidi Sara Irshaidat Hashem Abu Serhan Alaa A. Abd-alrazaq Mowafa Househ |
| author_sort | Arfan Ahmed |
| collection | DOAJ |
| description | Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019–2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic. |
| first_indexed | 2024-04-12T01:39:40Z |
| format | Article |
| id | doaj.art-82014e86c2504c46ab52930b73505b79 |
| institution | Directory Open Access Journal |
| issn | 2666-9900 |
| language | English |
| last_indexed | 2024-04-12T01:39:40Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj.art-82014e86c2504c46ab52930b73505b792022-12-22T03:53:14ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002022-01-012100066Machine learning models to detect anxiety and depression through social media: A scoping reviewArfan Ahmed0Sarah Aziz1Carla T. Toro2Mahmood Alzubaidi3Sara Irshaidat4Hashem Abu Serhan5Alaa A. Abd-alrazaq6Mowafa Househ7AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar; Corresponding authors.AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, QatarInstitute of Digital Healthcare, WMG University of Warwick, Warwick, UKCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarJordan University Hospital, Amman, JordanJordan University Hospital, Amman, JordanAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, QatarCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar; Corresponding authors.Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019–2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.http://www.sciencedirect.com/science/article/pii/S2666990022000179AnxietyDepressionSocial mediaSocial networkingArtificial intelligenceMachine learning |
| spellingShingle | Arfan Ahmed Sarah Aziz Carla T. Toro Mahmood Alzubaidi Sara Irshaidat Hashem Abu Serhan Alaa A. Abd-alrazaq Mowafa Househ Machine learning models to detect anxiety and depression through social media: A scoping review Computer Methods and Programs in Biomedicine Update Anxiety Depression Social media Social networking Artificial intelligence Machine learning |
| title | Machine learning models to detect anxiety and depression through social media: A scoping review |
| title_full | Machine learning models to detect anxiety and depression through social media: A scoping review |
| title_fullStr | Machine learning models to detect anxiety and depression through social media: A scoping review |
| title_full_unstemmed | Machine learning models to detect anxiety and depression through social media: A scoping review |
| title_short | Machine learning models to detect anxiety and depression through social media: A scoping review |
| title_sort | machine learning models to detect anxiety and depression through social media a scoping review |
| topic | Anxiety Depression Social media Social networking Artificial intelligence Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666990022000179 |
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