Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets

Sean-Kelly Palicki*, Shereen Fouad , Mariam Adedoyin-Olowe, Zahraa S. Abdallah

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU), which was officially implemented in 2020. During this period, UK residents experienced a great deal of uncertainty around the UK's continued relationship with the EU. Many people have used social media platforms to express their emotions about this critical event. Sentiment analysis has been recently considered as an important tool for detecting mental well-being in Twitter contents. However, detecting the psychological distress status in political related tweets is a challenging task due to the lack of explicit sentences describing the depressive or anxiety status. To address this problem, this paper leverages a transfer learning approach for sentiment analysis to measure the non-clinical psychological distress status in Brexit tweets. The framework transfers the knowledge learnt from self-reported psychological distress tweets (source domain) to detect the distress status in Brexit tweets (target domain). The framework applies a domain adaptation technique to decrease the impact of negative transfer between source and target domains. The paper also introduces a Brexit distress index that can be used to detect levels of psychological distress of individuals in Brexit tweets. We design an experiment that includes data from both domains. The proposed model is able to detect the non-clinical psychological distress status in Brexit tweets with an accuracy of 66% and 62% on the source and target domains, respectively.
Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
Number of pages9
ISBN (Electronic)978-1-4503-8104-8
Publication statusPublished - 22 Mar 2021
EventThe 36th ACM/SIGAPP Symposium on Applied Computing - , Korea, Democratic People's Republic of
Duration: 22 Mar 202126 Mar 2021
Conference number: 36th

Publication series

NameProceedings of the ACM Symposium on Applied Computing


ConferenceThe 36th ACM/SIGAPP Symposium on Applied Computing
Country/TerritoryKorea, Democratic People's Republic of
Internet address


  • brexit
  • psychological distress
  • sentiment analysis
  • social media analytics
  • transfer learning


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