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信息疫情与虚假新闻——2021年新冠疫情期间其全球影响的全面概述:一项范围综述

Infodemic and fake news - A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review.

作者信息

Balakrishnan Vimala, Ng Wei Zhen, Soo Mun Chong, Han Gan Joo, Lee Choon Jiat

机构信息

Faculty of Computer Science & Information Technology, Universiti Malaya, 50603, Lembah Pantai, Kuala Lumpur, Malaysia.

Faculty of Medicine, Universiti Malaya, 50603, Lembah Pantai, Kuala Lumpur, Malaysia.

出版信息

Int J Disaster Risk Reduct. 2022 Aug;78:103144. doi: 10.1016/j.ijdrr.2022.103144. Epub 2022 Jul 1.

Abstract

The spread of fake news increased dramatically during the COVID-19 pandemic worldwide. This study aims to synthesize the extant literature to understand the magnitude of this phenomenon in the wake of the pandemic in 2021, focusing on the motives and sociodemographic profiles, Artificial Intelligence (AI)-based tools developed, and the top trending topics related to fake news. A scoping review was adopted targeting articles published in five academic databases (January 2021-November 2021), resulting in 97 papers. Most of the studies were empirical in nature (N = 69) targeting the general population (N = 26) and social media users (N = 13), followed by AI-based detection tools (N = 27). Top motives for fake news sharing include low awareness, knowledge, and health/media literacy, Entertainment/Pass Time/Socialization, Altruism, and low trust in government/news media, whilst the phenomenon was more prominent among those with low education, males and younger. Machine and deep learning emerged to be the widely explored techniques in detecting fake news, whereas top topics were related to vaccine, virus, cures/remedies, treatment, and prevention. Immediate intervention and prevention efforts are needed to curb this anti-social behavior considering the world is still struggling to contain the spread of the COVID-19 virus.

摘要

在全球新冠疫情期间,假新闻的传播急剧增加。本研究旨在综合现有文献,以了解2021年疫情之后这一现象的严重程度,重点关注动机和社会人口统计学特征、开发的基于人工智能(AI)的工具以及与假新闻相关的热门话题。采用了范围综述法,针对五个学术数据库(2021年1月至2021年11月)发表的文章,最终得到97篇论文。大多数研究本质上是实证性的(N = 69),针对普通人群(N = 26)和社交媒体用户(N = 13),其次是基于人工智能的检测工具(N = 27)。分享假新闻的主要动机包括意识淡薄、知识匮乏、健康/媒体素养低、娱乐/打发时间/社交、利他主义以及对政府/新闻媒体的信任度低,而这一现象在受教育程度低、男性和年轻人中更为突出。机器学习和深度学习成为检测假新闻中广泛探索的技术,而热门话题与疫苗、病毒、治疗方法、治疗和预防有关。鉴于世界仍在努力遏制新冠病毒的传播,需要立即采取干预和预防措施来遏制这种反社会行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1218/9247231/a396515bd3c4/gr1_lrg.jpg

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