%0期刊文章%@ 2561-326X %I JMIR出版物%V 5% 卡塔尔世界杯8强波胆分析N 12% P e32427 %T平民算法:审查与节制的民主方法A Fedoruk,Benjamin A Nelson,Harrison %A Frost,Russell %A Fucile Ladouceur，凯%+理学院，安大略省理工大学，2000 Simcoe St N，奥沙瓦，ON, L1G 0C5，加拿大，1 905 721 8668，benjamin.fedoruk@ontariotechu.net %K信息病学%K错误信息%K算法%K社交媒体%K平民%K自然语言处理%K情感分析%K情感%K信任%K决策%K COVID-19 %D 2021 %7 21.12.2021 %9原始论文%J JMIR表单Res %G英语%X背景:COVID-19大流行造成的信息大流行引发了若干社会问题，包括公众与卫生专家之间的不信任加剧，甚至一些人拒绝接受疫苗接种;有消息称，四分之一的美国人会拒绝接种疫苗。这种社会关注可以追溯到今天的数字化水平，尤其是社交媒体的形式。目的:本研究的目标是确定一种最优的社交媒体算法，该算法能够减少错误信息的数量，同时也能确保某些个人自由(如言论自由)得到维护。在完成本文所述的分析之后，抽象出一种算法。发现一组最优社交媒体算法的抽象方面是本研究的目的。方法:由于社交媒体是错误信息传播的最重要因素，该团队决定在各种基于文本的平台(Twitter、4chan、Reddit、Parler、Facebook和YouTube)上研究信息流行病学。 This was done by using sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the data sets, both application programming interfaces (installed using Python’s pip) and pre-existing data compiled by standard scientific third parties were used. Results: The sentiment can be described using bimodal distributions for each platform, with a positive and negative peak, as well as a skewness. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. Conclusions: From this, the novel Plebeian Algorithm is proposed, which uses sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed as misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public’s evidence-informed decision-making. %M 34854812 %R 10.2196/32427 %U https://formative.www.mybigtv.com/2021/12/e32427 %U https://doi.org/10.2196/32427 %U http://www.ncbi.nlm.nih.gov/pubmed/34854812