We present an analysis of the dynamics of discussions in Twitter (before it became X) among supporters of various candidates in the 2022 French presidential election, and followers of different types of media. Our study demonstrates that we can automatically detect the synchronization of interest among different groups around specific topics at particular times. We introduce two complementary methods for constructing dynamic semantic networks, each with its own advantages. The growing aggregated network helps identify the reactivation of past topics, while the rolling window network is more sensitive to emerging discussions that, despite their significance, may appear suddenly and have a short lifespan. These two approaches offer distinct perspectives on the discussion landscape. Rather than choosing between them, we advocate for using both, as their comparison provides valuable insights at a relatively low computational and storage cost. Our findings confirm and quantify, on a larger scale and in an automatic, agnostic manner, observations previously made using more qualitative methods. We believed this work represents a step forward in developing methodologies to assess equity in information treatment, an obligation imposed by law on broadcasters that use broadcast spectrum frequencies in certain countries.

翻译：本研究分析了2022年法国总统选举期间，各候选人支持者与不同类型媒体关注者在Twitter（更名为X之前）平台上的讨论动态。研究表明，我们能够自动检测特定时间点不同群体围绕特定话题产生的兴趣同步现象。我们提出了两种构建动态语义网络的互补方法，每种方法各具优势：增长聚合网络有助于识别历史话题的再激活，而滚动窗口网络对新兴讨论更为敏感——这类讨论尽管重要，却可能突然出现且持续时间短暂。这两种方法为讨论态势提供了不同的观察视角。我们主张同时采用两种方法而非择一使用，因为二者的比较能以较低的计算和存储成本提供有价值的洞见。我们的发现以更大规模、自动且无需预设的方式，证实并量化了先前通过定性方法获得的观测结果。我们认为这项工作在开发评估信息处理公平性的方法论方面取得了进展——某些国家法律对使用广播频谱频率的传播机构提出了此项义务要求。