Social media feeds typically favor posts according to user engagement. The most ubiquitous type of engagement (and the type we study) is *likes*. Users customarily take engagement metrics such as likes as a neutral proxy for quality and authority. This incentivizes like manipulation to influence public opinion through *coordinated inauthentic behavior* (CIB). CIB targeted at likes is largely unstudied as collecting suitable data about users' liking behavior is non-trivial. This paper contributes a scripted algorithm to collect suitable liking data from Twitter and a collected 30 day dataset of liking data from the Danish political Twittersphere #dkpol, over which we analyze the script's performance. Using only the binary matrix of users and the tweets they liked, we identify large clusters of perfectly correlated users, and discuss our findings in relation to CIB.

翻译：社交媒体信息流通常根据用户参与度来推荐帖子。我们研究的最普遍参与类型是*点赞*。用户习惯将点赞等参与度指标视为质量和权威的中性代理，这种倾向激励了通过*非真实协调行为*（CIB）操纵点赞来影响公众舆论。针对点赞的CIB尚未得到充分研究，因为收集用户点赞行为的合适数据具有挑战性。本文贡献了一个脚本化算法，用于从Twitter收集合适的点赞数据，并收集了丹麦政治推特圈#dkpol中30天的点赞数据集，在此数据集上我们分析了脚本的性能。仅使用用户及其点赞推文的二元矩阵，我们就识别出大规模完全相关的用户聚类，并讨论了与CIB相关的发现。