大众媒体对偏见的数字环境中意见演变的影响:一个有界的信任模型
Valentina Pansanella1,2, Alina Sîrbu3, Janos Kertesz4
1Faculty of Science, Scuola Normale Superiore, Pisa, Italy. valentina.pansanella@sns.it.
Scientific reports
|September 5, 2023
概括
社交媒体通过用户交互和媒体内容影响人们的意见. 矛盾的是,通过加强偏见,推系统可以保护民众免受极端宣传的影响.
科学领域:
- 计算社会科学 计算社会科学
- 意见的动态 意见的动态
- 信息生态系统信息生态系统
背景情况:
- 社交网络网站将用户的意见与主流媒体混合在一起,创造了适合错误信息的环境.
- 推系统中的算法偏差可以放大两极分化和激进化.
- 了解社交和媒体影响的相互作用对于在线信息环境至关重要.
研究的目的:
- 调查社会影响和大众媒体如何影响在偏见的在线环境中意见演变.
- 分析算法偏差与外部媒体影响的作用.
- 用一个有界的信任模型来建模论动态,将媒体纳入作为固的代理人.
主要方法:
- 利用一个有界的信任意见动态模型.
- 嵌入的算法偏见和与媒体的互动 (固执的代理人).
- 分析了四种不同的媒体景观场景.
主要成果:
- 思想开放的人群更容易受到温和或极端主义的宣传.
- 平衡的信息环境会导致一些人无法做出决定.
- 推系统通过加强用户偏见,可以减轻外部宣传的完全操纵.
结论:
- 社会影响力,媒体曝光和算法偏见的组合显著塑造了意见动态.
- 算法偏见可能作为一种保护机制,防止广泛的宣传,尽管它的负面内涵.
- 未来的研究应该探索细微的干预措施,以促进在线空间的知情公共话语.
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