Computational intelligence and neuroscience
Oct 3, 2016
We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obt...
Proceedings of the National Academy of Sciences of the United States of America
Sep 30, 2016
Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-rangi...
INTRODUCTION: Nonmedical use of prescription medications/drugs (NMUPD) is a serious public health threat, particularly in relation to the prescription opioid analgesics abuse epidemic. While attention to this problem has been growing, there remains a...
Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in...
Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza...
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large dat...
We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly rea...
Computational intelligence and neuroscience
Sep 14, 2015
Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguis...
Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information f...
BACKGROUND: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the pub...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.