AIMC Topic: Mass Media
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The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media.
PloS one
Jan 29, 2021
We examine gender bias in media by tallying the number of men and women quoted in news text, using the Gender Gap Tracker, a software system we developed specifically for this purpose. The Gender Gap Tracker downloads and analyzes the online daily pu...
Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables.
Anxiety, stress, and coping
Jan 26, 2021
OBJECTIVES: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.
Automatic Identification of Information Quality Metrics in Health News Stories.
Frontiers in public health
Dec 18, 2020
Many online and printed media publish health news of questionable trustworthiness and it may be difficult for laypersons to determine the information quality of such articles. The purpose of this work was to propose a methodology for the automatic a...
Reporting of screening and diagnostic AI rarely acknowledges ethical, legal, and social implications: a mass media frame analysis.
BMC medical informatics and decision making
Dec 10, 2020
BACKGROUND: Healthcare is a rapidly expanding area of application for Artificial Intelligence (AI). Although there is considerable excitement about its potential, there are also substantial concerns about the negative impacts of these technologies. S...
Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation.
Journal of medical Internet research
Sep 9, 2020
BACKGROUND: The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend.
Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.
Journal of medical Internet research
Aug 17, 2020
BACKGROUND: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus d...
How much are we exposed to alcohol in electronic media? Development of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA).
Drug and alcohol dependence
Jan 9, 2020
BACKGROUND: Evidence demonstrates that seeing alcoholic beverages in electronic media increases alcohol initiation and frequent and excessive drinking, particularly among young people. To efficiently assess this exposure, the aim was to develop the A...
Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.
Methods (San Diego, Calif.)
Aug 13, 2017
From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single-stranded RNA virus belonging to the family Coronaviridae. Despite expert opi...
The digital generation and nursing robotics: A netnographic study about nursing care robots posted on social media.
Nursing inquiry
Oct 11, 2016
The aim of this study was to present the functionality and design of nursing care robots as depicted in pictures posted on social media. A netnographic study was conducted using social media postings over a period of 3 years. One hundred and Seventy-...
Automatically quantifying the scientific quality and sensationalism of news records mentioning pandemics: validating a maximum entropy machine-learning model.
Journal of clinical epidemiology
Mar 7, 2016
OBJECTIVE: To develop and validate a method for automatically quantifying the scientific quality and sensationalism of individual news records.
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