A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter

Nur Hafieza Ismail, Ninghao Liu, Mengnan Du, Zhe He, Xia Hu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Background: Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma and fear of cancer recurrence. The widespread use of Twitter for socializing has been the alternative medium for data collection compared to traditional studies of mental health, which primarily depend on information taken from medical staff with their consent. These social media data, to a certain extent, reflect the users’ psychological state. However, Twitter also contains a mix of noisy and genuine tweets. The process of manually identifying genuine tweets is expensive and time-consuming. Methods: We stream the data using cancer as a keyword to filter the tweets with cancer-free and use post-traumatic stress disorder (PTSD) related keywords to reduce the time spent on the annotation task. Convolutional Neural Network (CNN) learns the representations of the input to identify cancer survivors with PTSD. Results: The results present that the proposed CNN can effectively identify cancer survivors with PTSD. The experiments on real-world datasets show that our model outperforms the baselines and correctly classifies the new tweets. Conclusions: PTSD is one of the severe anxiety disorders that could affect individuals who are exposed to traumatic events, including cancer. Cancer survivors are at risk of short-term or long-term effects on physical and psycho-social well-being. Therefore, the evaluation and treatment of PTSD are essential parts of cancer survivorship care. It will act as an alarming system by detecting the PTSD presence based on users’ postings on Twitter.

Original languageEnglish (US)
Article number254
JournalBMC Medical Informatics and Decision Making
StatePublished - Dec 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Health Policy
  • Health Informatics
  • Computer Science Applications


  • Cancer survivor
  • Deep learning
  • PTSD
  • Social media


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