Charline Bour

Clinatec

Epidemiological cohort studies play a crucial role in identifying risk factors for various outcomes among participants. These studies are often time-consuming and costly due to recruitment and long-term follow-up. Social media (SM) data has emerged as a valuable complementary source for digital epidemiology and health research, as online communitie...

Objective This work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit. Methods We collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms...

Introduction: The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes. Research design and methods: We developed a worldwide online diabetes observatory based on 34 mill...

Background Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient’s perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress. Objective Leveraging machine learning methods, we aim to...

With an unprecedented volume of data generated online by individuals, it becomes more and more relevant to analyze them for health-related purposes. However, the field of digital epidemiology lacks standardization which prevents it from moving away from a reproducibility crisis. In this article, we introduce the concept of a virtual digital cohort...

BACKGROUND Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient’s perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress. OBJECTIVE Leveraging machine learning methods, we aim to...

Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations shared within the diabetes online community from a causality perspective. Materials and Methods: More than 30 m...

Introduction More than one-third of the world population uses at least one form of social media. Since their advent in 2005, health-oriented research based on social media data has largely increased as discussions about health issues are broadly shared online and generate a large amount of health-related data. The objective of this scoping review i...

BACKGROUND As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. But so far, social media features, methodologies and ethical issues are unclear with no overview of this relatively young field of research. OBJECTIVE This scoping review aimed to provide an evidence map of th...

Background As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research. Objective This scoping rev...