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自动社交媒体信息混乱的映射。机器人和人工智能在社会传播误导性信息中的作用。

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.

机构信息

Engineering Faculty, Uninettuno International Telematic University, Rome, Italy.

出版信息

PLoS One. 2024 May 31;19(5):e0303183. doi: 10.1371/journal.pone.0303183. eCollection 2024.

DOI:10.1371/journal.pone.0303183
PMID:38820281
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11142451/
Abstract

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

摘要

本文对社交媒体平台中的信息紊乱现象进行了分析。该研究采用自然语言处理、主题建模和知识图谱构建等方法，深入了解假新闻现象及其对批判性思维和知识管理的影响。分析聚焦于四个研究问题：1）不同平台上错误信息、虚假信息和不良信息的分布情况；2）假新闻中的常见主题及其可见性；3）人工智能作为权威和/或传播者的作用；4）应对信息紊乱的策略。人工智能的作用得到了强调，它既是事实核查的工具，也是构建真实性识别机器人的工具，同时也是虚假叙事的潜在放大器。为了应对信息紊乱，本文提出了一些策略，包括提高社交媒体用户的数字素养技能和促进批判性思维。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1604/11142451/4e2fc7dfb937/pone.0303183.g016.jpg
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相似文献

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Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.

PLoS One. 2024

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引用本文的文献

[1]
Political affiliation or need for cognition? It depends on the post: Comparing key factors related to detecting health disinformation in the U.S.

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[2]
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本文引用的文献

[1]
Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis.

JMIR Infodemiology. 2022-12-5

[2]
Social Media and the Influence of Fake News on Global Health Interventions: Implications for a Study on Dengue in Brazil.

Int J Environ Res Public Health. 2023-3-28

[3]
ChatGPT's inconsistent moral advice influences users' judgment.

Sci Rep. 2023-4-6

[4]
Realfood and Cancer: Analysis of the Reliability and Quality of YouTube Content.

Int J Environ Res Public Health. 2023-3-13

[5]
Large language models and the perils of their hallucinations.

Crit Care. 2023-3-21

[6]
Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets.

J Med Internet Res. 2023-3-31

[7]
Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

Front Public Health. 2023

[8]
Monkeypox Vaccine Acceptance among Ghanaians: A Call for Action.

Vaccines (Basel). 2023-1-21

[9]
Impact of public sentiments on the transmission of COVID-19 across a geographical gradient.

PeerJ. 2023

[10]
COVID-19 Information on YouTube: Analysis of Quality and Reliability of Videos in Eleven Widely Spoken Languages across Africa.

Glob Health Epidemiol Genom. 2023

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