@inproceedings{f50c69f41b6e4c8c95dee43a352e6394,
title = "Identification of cancer survivors living with PTSD on social media",
abstract = "The trauma of cancer often leaves survivors with PTSD. Tweets posted on Twitter usually reflect the users' psychological state, which is convenient for data collection. However, Twitter also contains a mix of noisy and genuine tweets. The process of manually identifying genuine tweets is expensive and time-consuming. Thus, we propose a knowledge transfer technique to filter out unrelated tweets. Our experiments show that our model outperforms the baselines.",
keywords = "Cancer survivors, Post-traumatic, Social media, Stress disorders",
author = "Ismail, {Nur Hafieza} and Ninghao Liu and Mengnan Du and Zhe He and Xia Hu",
note = "Publisher Copyright: {\textcopyright} 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",
year = "2019",
month = aug,
day = "21",
doi = "10.3233/SHTI190488",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1468--1469",
editor = "Brigitte Seroussi and Lucila Ohno-Machado and Lucila Ohno-Machado and Brigitte Seroussi",
booktitle = "MEDINFO 2019",
address = "Netherlands",
}