We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on data obtained from the micro-blogging platform Twitter. We build a semantic network based on the hashtags used by all the users following at least one of the main candidates. With this network we can detect the topics that are discussed in the society. At a difference with most studies of opinion on social media, we do not choose the topics a priori, they emerge from the community structure of the semantic network instead. We assign to each user a dynamical topic vector which measures the evolution of her/his opinion in this space and allows us to monitor the similarities and differences among groups of supporters of different candidates. Our results show that the method is able to detect the dynamics of formation of opinion on different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of 2015 election.

我们根据从微博平台 Twitter 获得的数据，对阿根廷 2015 年和 2019 年总统选举期间政治格局的演变进行了研究。我们基于所有用户使用的主题标签构建了一个语义网络，这些标签至少跟随一个主要候选者。通过这个网络，我们可以检测到社会上讨论的话题。与大多数社交媒体观点研究不同的是，我们没有先验地选择主题，而是从语义网络的社区结构中出现。我们为每个用户分配一个动态主题向量，用于衡量她/他在该空间中的意见演变，并允许我们监控不同候选人的支持者群体之间的异同。