A
Angel Xie
Researcher at Emory University
Publications - 5
Citations - 137
Angel Xie is an academic researcher from Emory University. The author has contributed to research in topics: Breast cancer & Medicine. The author has an hindex of 3, co-authored 4 publications receiving 66 citations.
Papers
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Journal ArticleDOI
Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource.
Abeed Sarker,Sahithi Lakamana,Whitney Hogg-Bremer,Angel Xie,Mohammed Ali Al-Garadi,Yuan-Chi Yang +5 more
TL;DR: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings, and create a symptom lexicon for future research.
Posted ContentDOI
Self-reported COVID-19 symptoms on Twitter: An analysis and a research resource
Abeed Sarker,Sahithi Lakamana,Whitney Hogg-Bremer,Angel Xie,Mohammed Ali Al-Garadi,Yuan-Chi Yang +5 more
TL;DR: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings, and create a symptom lexicon for the research community.
Book ChapterDOI
Automatic Breast Cancer Cohort Detection from Social Media for Studying Factors Affecting Patient-Centered Outcomes
Mohammed Ali Al-Garadi,Yuan-Chi Yang,Sahithi Lakamana,Jie Lin,Sabrina Li,Angel Xie,Whitney Hogg-Bremer,Mylin A. Torres,Imon Banerjee,Abeed Sarker +9 more
TL;DR: In this paper, a natural language processing (NLP) pipeline was proposed to automatically detect breast cancer patients from Twitter based on their self-reports, and a machine learning classifier was trained using manually-annotated data (n = 5,019) for distinguishing firsthand self-reported of breast cancer from other tweets.
Journal ArticleDOI
Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.
TL;DR: In this article , the authors developed and evaluated an automatic system on Twitter to identify users who have self-reported chronic stress experiences, which has a high potential for surveillance and large-scale intervention.
Posted ContentDOI
Automatic Breast Cancer Survivor Detection from Social Media for Studying Latent Factors Affecting Treatment Success
Abeed Sarker,Mohammed Ali Al-Garadi,Yuan-Chi Yang,Sahithi Lakamana,Lin J,Sabrina Li,Angel Xie,Whitney Hogg-Bremer,Mylin A. Torres,Imon Banerjee +9 more
TL;DR: Qualitative analyses of posts from automatically-detected users revealed discussions about side effects, non-adherence, and mental health conditions, illustrating the feasibility of the social media-based approach for studying breast cancer-related PCOs from a large population.