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Machine Learning Methods to Predict Social Media Disaster Rumor Refuters

This research provides a general methodology for distinguishing disaster-related anti-rumor spreaders from a non-ignorant population base, with strong connections in their social circle. Several important influencing factors are examined and illustrated. User information from the most recent posted...

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Detalles Bibliográficos
Autores principales: Wang, Shihang, Li, Zongmin, Wang, Yuhong, Zhang, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518238/
https://www.ncbi.nlm.nih.gov/pubmed/31022894
http://dx.doi.org/10.3390/ijerph16081452
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author Wang, Shihang
Li, Zongmin
Wang, Yuhong
Zhang, Qi
author_facet Wang, Shihang
Li, Zongmin
Wang, Yuhong
Zhang, Qi
author_sort Wang, Shihang
collection PubMed
description This research provides a general methodology for distinguishing disaster-related anti-rumor spreaders from a non-ignorant population base, with strong connections in their social circle. Several important influencing factors are examined and illustrated. User information from the most recent posted microblog content of 3793 Sina Weibo users was collected. Natural language processing (NLP) was used for the sentiment and short text similarity analyses, and four machine learning techniques, i.e., logistic regression (LR), support vector machines (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were compared on different rumor refuting microblogs; after which a valid and robust distinguishing XGBoost model was trained and validated to predict who would retweet disaster-related rumor refuting microblogs. Compared with traditional prediction variables that only access user information, the similarity and sentiment analyses of the most recent user microblog contents were found to significantly improve prediction precision and robustness. The number of user microblogs also proved to be a valuable reference for all samples during the prediction process. This prediction methodology could be possibly more useful for WeChat or Facebook as these have relatively stable closed-loop communication channels, which means that rumors are more likely to be refuted by acquaintances. Therefore, the methodology is going to be further optimized and validated on WeChat-like channels in the future. The novel rumor refuting approach presented in this research harnessed NLP for the user microblog content analysis and then used the analysis results of NLP as additional prediction variables to identify the anti-rumor spreaders. Therefore, compared to previous studies, this study presents a new and effective decision support for rumor countermeasures.
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spelling pubmed-65182382019-05-31 Machine Learning Methods to Predict Social Media Disaster Rumor Refuters Wang, Shihang Li, Zongmin Wang, Yuhong Zhang, Qi Int J Environ Res Public Health Article This research provides a general methodology for distinguishing disaster-related anti-rumor spreaders from a non-ignorant population base, with strong connections in their social circle. Several important influencing factors are examined and illustrated. User information from the most recent posted microblog content of 3793 Sina Weibo users was collected. Natural language processing (NLP) was used for the sentiment and short text similarity analyses, and four machine learning techniques, i.e., logistic regression (LR), support vector machines (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were compared on different rumor refuting microblogs; after which a valid and robust distinguishing XGBoost model was trained and validated to predict who would retweet disaster-related rumor refuting microblogs. Compared with traditional prediction variables that only access user information, the similarity and sentiment analyses of the most recent user microblog contents were found to significantly improve prediction precision and robustness. The number of user microblogs also proved to be a valuable reference for all samples during the prediction process. This prediction methodology could be possibly more useful for WeChat or Facebook as these have relatively stable closed-loop communication channels, which means that rumors are more likely to be refuted by acquaintances. Therefore, the methodology is going to be further optimized and validated on WeChat-like channels in the future. The novel rumor refuting approach presented in this research harnessed NLP for the user microblog content analysis and then used the analysis results of NLP as additional prediction variables to identify the anti-rumor spreaders. Therefore, compared to previous studies, this study presents a new and effective decision support for rumor countermeasures. MDPI 2019-04-24 2019-04 /pmc/articles/PMC6518238/ /pubmed/31022894 http://dx.doi.org/10.3390/ijerph16081452 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Shihang
Li, Zongmin
Wang, Yuhong
Zhang, Qi
Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
title Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
title_full Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
title_fullStr Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
title_full_unstemmed Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
title_short Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
title_sort machine learning methods to predict social media disaster rumor refuters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518238/
https://www.ncbi.nlm.nih.gov/pubmed/31022894
http://dx.doi.org/10.3390/ijerph16081452
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