  Social media has become an indispensable channel for political communication.
However, the political discourse is increasingly characterized by hate speech,
which affects not only the reputation of individual politicians but also the
functioning of society at large. In this work, we empirically analyze how the
amount of hate speech in replies to posts from politicians on Twitter depends
on personal characteristics, such as their party affiliation, gender, and
ethnicity. For this purpose, we employ Twitter's Historical API to collect
every tweet posted by members of the 117th U.S. Congress for an observation
period of more than six months. Additionally, we gather replies for each tweet
and use machine learning to predict the amount of hate speech they embed.
Subsequently, we implement hierarchical regression models to analyze whether
politicians with certain characteristics receive more hate speech. We find that
tweets are particularly likely to receive hate speech in replies if they are
authored by (i) persons of color from the Democratic party, (ii) white
Republicans, and (iii) women. Furthermore, our analysis reveals that more
negative sentiment (in the source tweet) is associated with more hate speech
(in replies). However, the association varies across parties: negative
sentiment attracts more hate speech for Democrats (vs. Republicans).
Altogether, our empirical findings imply significant differences in how
politicians are treated on social media depending on their party affiliation,
gender, and ethnicity.
