AIMC Topic: Social Media

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Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.

Proceedings of the National Academy of Sciences of the United States of America
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...

Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning.

Addictive behaviors
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...

Gaining insights from social media language: Methodologies and challenges.

Psychological methods
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...

Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza.

PloS one
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...

An ensemble method for extracting adverse drug events from social media.

Artificial intelligence in medicine
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...

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

PLoS computational biology
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...

Exploiting Language Models to Classify Events from Twitter.

Computational intelligence and neuroscience
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...

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

Journal of medical systems
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...

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

Journal of medical Internet research
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...

Exploring Spanish health social media for detecting drug effects.

BMC medical informatics and decision making
BACKGROUND: Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have devoted a great interest in the early detection of ADRs due to their high incidence and increasing hea...