Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, 30172, Venice, Italy.
LUISS Data Lab, Viale Pola 12, 00198, Rome, Italy.
Sci Rep. 2024 Oct 1;14(1):22804. doi: 10.1038/s41598-024-69354-y.
The share of social media attention to political candidates was shown to be a good predictor of election outcomes in several studies. This attention to individual candidates fluctuates due to incoming daily news and sometimes reflects long-term trends. By analyzing Twitter data in the 2013 and 2022 election campaign we observe that, on short timescales, the dynamics can be effectively characterized by a mean-reverting diffusion process on a logarithmic scale. This implies that the response to news and the exchange of opinions on Twitter lead to attention fluctuations spanning orders of magnitudes. However, these fluctuations remain centered around certain average levels of popularity, which change slowly in contrast to the rapid daily and hourly variations driven by Twitter trends and news. In particular, on our 2013 data we are able to estimate the dominant timescale of fluctuations at around three hours. Finally, by considering the extreme data points in the tail of the attention variation distribution, we could identify critical events in the electoral campaign period and extract useful information from the flow of data.
社交媒体对政治候选人的关注度在几项研究中被证明是选举结果的良好预测指标。由于每天都会有新的新闻，因此对个别候选人的关注度会波动，有时还会反映出长期趋势。通过分析 2013 年和 2022 年选举活动中的 Twitter 数据，我们观察到，在短时间尺度上，动态可以通过对数尺度上的均值回复扩散过程有效地描述。这意味着对新闻的反应和在 Twitter 上交换意见会导致关注度波动跨越多个数量级。然而，这些波动仍然集中在某些受欢迎程度的平均水平周围，与由 Twitter 趋势和新闻驱动的快速日常和小时变化相比，这些平均水平变化缓慢。特别是在我们的 2013 年数据中，我们能够估计波动的主导时间尺度约为三个小时。最后，通过考虑关注度变化分布尾部的极端数据点，我们可以识别选举期间的关键事件，并从数据流中提取有用信息。
