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When primary elections are closed, in the months leading up to primary elections, candidates for political office may use ideologically more extreme rhetoric to compete for the votes of their co-partisans. Previous studies of the relationship between primary election rule and politicians’ rhetorical extremism have focused on one congressional election, thus not controlling for the political environment, or focused on two presidential elections only. This research improves the literature by studying 14 years of data and using a causal inference framework. Using a deep learning method, this paper estimates ideological extremism of tweets produced by Members of Congress (MCs) between 2008 and 2022 to study whether among politicians of the same ideology, those in states with closed primary elections tweeted using more ideologically extreme language than those in states with open primary elections. In addition, using a differences-in-differences approach, this research exploit changes in some states’ primary election laws to study if these changes lead to changes in the extremism of MCs’ tweet content. Finally, this research identifies the association between tweets’ extremism and their propagation. In summary, this research contributes to the literature by using new data and a causal inference method to study how electoral institutions may condition political communication.