意大利议会选举的公众关注的波动和极端事件
Andrea Auconi1, Lorenzo Federico2,3, Gianni Riotta4
1Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, 30172, Venice, Italy.
Scientific reports
|October 1, 2024
概括
社交媒体对政治候选人的关注预测了选举结果. 对Twitter数据的分析显示,注意力动态遵循一个平均值逆转的扩散过程,波动以缓慢变化的受欢迎程度为中心.
科学领域:
- 计算社会科学 计算社会科学
- 政治科学 政治科学是指政治学.
- 网络科学 网络科学
背景情况:
- 社交媒体对政治候选人的关注是众所周知的选举结果的预测因素.
- 由于新闻周期,候选人的注意力每天波动,反映了长期趋势.
- 了解这些动态对于分析选举活动至关重要.
研究的目的:
- 分析来自2013年和2022年选举活动的Twitter数据.
- 描述社交媒体关注政治候选人的短期动态.
- 识别关键事件并从注意力波动中提取信息.
主要方法:
- 分析两次选举 (2013年和2022年) 的Twitter数据.
- 应用一个平均值逆转的扩散过程模型在对数尺度上.
- 估计注意力波动的主导时间尺度.
- 识别极端数据点以检测关键活动事件.
主要成果:
- 社交媒体注意力动态可以在对数尺度上建模为平均值逆转的扩散过程.
- 注意力波动表现出快速的每日/每小时变化,但以缓慢变化的受欢迎程度为中心.
- 据估计,注意力波动的主要时间尺度约为三小时 (2013年数据).
- 极端的注意力变化确定了关键的选举活动事件.
结论:
- 社交媒体的关注为选举动态提供了有价值的见解.
- 拟议的扩散模型有效地捕捉了注意力波动.
- 对注意力尾巴的分析可以揭示重要的竞选事件和趋势.
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