在打击网络错误信息中,超越算法和事实核查的印象:一篇文献综述

Abdurrahman Onifade
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引用次数: 0

摘要

错误信息是一种全球性的流行病,由人类智力的复杂性、算法系统和其他因素推动。例如,在针对社交媒体的参与和反应进行优化的算法激增的推动下,错误信息点燃或阻碍了社会政治参与和运动,并劝阻公民接种疫苗。观察表明,遏制错误信息的努力在很大程度上是以技术为基础的,人们普遍认为,错误信息可以通过编码消除,或者用自动化工具进行事实核查。因此,本文有助于讨论在打击错误信息的斗争中,除了算法和事实检查之外,还应该探索其他机制。本文采用综合文献综述方法,从b谷歌Scholar、JStor等来源有目的地选择22篇全文,如表1所示。使用PRISMA流程图来显示搜索过程。文献综述的结果表明,算法和事实核查在识别、核实和纠正错误信息方面产生了重大影响。尽管如此,它们也有缺点,应该与信息素养计划/服务和信息伦理相辅相成。该研究建议将信息素养和信息伦理作为教育模块的组成部分,并提高对解决错误信息问题的非算法方法的认识,以便主动建立一个更知情的公众。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Looking beyond the impressions of algorithms and fact-checking in fighting online misinformation: A literature review
Misinformation is a global pandemic, fueled by the sophistication of the human intellect, algorithmic systems among other factors. Enhanced by the proliferation of algorithms optimized for engagement and reactions on social media, misinformation has ignited or hampered sociopolitical participation and movements and dissuaded citizens from being vaccinated, for example. Observations have shown that efforts to contain misinformation have largely been tech-based, with ubiquitous impressions that it can be coded into extinction and/or fact-checked with automated tools. This paper, therefore, contributes to the debate that there are mechanisms that should be explored beyond algorithms and fact-checking in the fight against misinformation. The paper adopted an integrative literature review approach, using purposive selection of 22 full texts from Google Scholar, JStor and other sources as captured in Table 1. The PRISMA flow diagram was used to show the search process. Findings from the literature reviewed showed that algorithms and fact-checking have made significant impacts in identifying, verifying and correcting misinformation. Nonetheless, they have drawbacks that should be complemented with information literacy programs/services and information ethics. The study suggests that information literacy and information ethics be made integral parts of educational modules and awareness should be increased about non-algorithmic approaches to solving misinformation problems in order to proactively build a more informed public.
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