Objectives The role of social media as a source of timely and massive information has
become more apparent since the era of Web 2.0. Multiple studies illustrated the use of …

Recent work in machine learning and natural language processing has studied the health
content of tweets and demonstrated the potential for extracting useful public health …

We use a statistical classifier described in (Paul and Dredze 2011a) to identify tweets about
health from HEALTH. The classifier has an estimated F-1 score of. 70, and evenly balances …

Social media has evolved into a crucial resource for obtaining large volumes of real-time
information. The promise of social media has been realized by the public health domain …

Objective The aim of the Social Media Mining for Health Applications (# SMM4H) shared
tasks is to take a community-driven approach to address the natural language processing …

Abstract Objective We executed the Social Media Mining for Health (SMM4H) 2017 shared
tasks to enable the community-driven development and large-scale evaluation of automatic …

Background: Biomedical research has traditionally been conducted via surveys and the
analysis of medical records. However, these resources are limited in their content, such that …

In recent years, advances in technologies, such as machine learning, natural language
processing, and automated data processing, have offered potential biomedical and public …

Introduction The authors of this work propose an unsupervised machine learning model that
has the ability to identify real-world latent infectious diseases by mining social media data. In …

Background Digital traces left on the Internet by web users, if properly aggregated and
analyzed, can represent a huge information dataset able to inform syndromic surveillance …