MersV1, PubMed, Curation, bibRecord, 000B97

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.

Identifieur interne : 000B97 ( PubMed/Curation ); précédent : 000B96; suivant : 000B98

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.

Auteurs : Sungwoon Choi [Corée du Sud] ; Jangho Lee [Corée du Sud] ; Min-Gyu Kang [Corée du Sud] ; Hyeyoung Min [Corée du Sud] ; Yoon-Seok Chang [Corée du Sud] ; Sungroh Yoon [États-Unis]

Source :

Methods (San Diego, Calif.) [ 1095-9130 ] ; 2017.

RBID : pubmed:28813689

Descripteurs français

KwdFr :
- Apprentissage machine, Coronavirus du syndrome respiratoire du Moyen-Orient (pathogénicité), Flambées de maladies, Humains, Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Mass-médias, République de Corée.
MESH :
- pathogénicité : Coronavirus du syndrome respiratoire du Moyen-Orient.
- virologie : Infections à coronavirus.
- épidémiologie : Infections à coronavirus.
- Apprentissage machine, Flambées de maladies, Humains, Mass-médias, République de Corée.

English descriptors

KwdEn :
- Coronavirus Infections (epidemiology), Coronavirus Infections (virology), Disease Outbreaks, Humans, Machine Learning, Mass Media, Middle East Respiratory Syndrome Coronavirus (pathogenicity), Republic of Korea.
MESH :
- epidemiology : Coronavirus Infections.
- pathogenicity : Middle East Respiratory Syndrome Coronavirus.
- virology : Coronavirus Infections.
- Disease Outbreaks, Humans, Machine Learning, Mass Media, Republic of Korea.

Abstract

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 opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions.

DOI: 10.1016/j.ymeth.2017.07.027
PubMed: 28813689

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<front><div type="abstract" xml:lang="en">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 opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions.</div>
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<Abstract><AbstractText>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 opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions.</AbstractText>
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Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks.

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