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There is no denying that Donald Trump's speaking style is entertaining but what about his linguistic style? There is a plethora of studies about gender differences in language used by politicians but only few have used tweets for analysis. Unlike traditional methods, big data offers new possibilities of mining large-scale material for research. In this contribution, we make use of web scraping to collect the tweet (N=3,239) by Donald Trump prior to his election as president. To determine his linguistic style, we devised our own dictionary that contained both feminine and linguistic markers. The code for sentiment analysis was tweaked to yield a femininity score. Our analysis of some of his campaign speeches suggest otherwise.

Radical right populist parties have often been treated as "pariahs," being excluded from coalition politics in parliamentary democracies. We argue that negative rhetoric targeted at radical right populist parties in legislative debates is used by the established parties to distance themselves from such parties and that the incentives to do so depend on the political context. Using sentiment analysis of speeches in the Swedish Riksdag from 2010 to 2022, we find that rhetoric targeted toward the radical right Sweden Democrats is more negative than speech concerning other parties on average. We also find that this negative rhetoric declined over time, particularly from the center-right parties, as the formerly marginal Sweden Democrats gained more seats and became a potential partner for cooperation. Our analysis demonstrates how tracking parliamentary discourse provides insights into changing party dynamics. Our findings suggest that, as the prospects for populists' pariah status change, rhetoric from established parties reflects this shifting role in party politics, with enduring negativity accompanied by reduced hostility among the center-right parties with the greatest potential for cooperation.