Does Twitter trigger negative tones in politicians' digital communication? On social media direct feedback mechanisms such as retweets or likes signal to politicians which message and tone are popular. Current research suggests that negative language increases the number of retweets a single tweet receives, indicating preferences for negativity in the audience on Twitter. However, it remains unclear whether politicians adapt to the logic of Twitter or simply follow the rules determined by the broader political context, namely the state of their electoral race. We use sentiment analysis to measure the tone used by 342 candidates in 97,909 tweets in their Twitter campaign in the 2018 midterm elections for the US House of Representatives and map the ideological composition of each politician's Twitter network. We show that the feedback candidates receive creates an incentive to use negativity. The size and direction of the tonal incentive is connected to the ideological composition of the candidate's follower network. Unexpectedly, the platform-specific incentive does not affect the tone used by candidates in their Twitter campaigns. Instead we find that the tone is mainly related to characteristics of the electoral race. We show that our findings are not dependent on our sentiment measurement by validating our results using hand coding and machine learning.
