Skip to main content Skip to main navigation


Improving personal health mention detection on twitter using permutation based word representation learning

Pervaiz Iqbal Khan; Imran Razzak; Andreas Dengel; Sheraz Ahmed
In: ICONIP 2020: Neural Information Processing. International Conference on Neural Information Processing (ICONIP-2020), November 18-22, Pages 776-785, Lecture Notes in Computer Science (LNCS), Vol. 12532, Springer, Cham, 11/2020.


Social media has become a substitute for social interaction, thus the amount of medical and clinical-related information on the web is increasing. Monitoring of Personal Health Mentioning (PHM) on social media is an active area of research that predicts whether a given piece of text contains a health condition or not. To this end, the main idea is to consider the usage of disease or symptom words in the text. However, due to their usage in a figurative sense, disease or symptom words may not always indicate the presence of the health condition. Prior work attempts to address this by considering contextual word representations along with the utilization of the sentiment information. However, these methods are unable to capture the complete context in which symptom word is used. In this work, we incorporate permutation-based contextual word representation for the task of health mention detection which captures the context of disease words efficiently, in the given piece of text, and hence improves the performance of the classifier. To evaluate the integrity of the proposed method, we perform experimentation on the public benchmark dataset that shows an improvement of 5.5% in F-score in comparison to the state of the art health mention detection classifier. (Code is available at