Beschreibung:
This paper assesses the results of an experiment designed to identify discrimination in users' following behavior on Twitter. Specifically, we created fictitious bot accounts that resembled humans and claimed to be PhD students in economics. The accounts differed in three characteristics: gender (male or female), race (Black or White), and university affiliation (top- or lower-ranked). The bot accounts randomly followed Twitter users who form part of the #EconTwitter academic community. We measured how many follow-backs each account obtained after a given period. Twitter users from this community were 12% more likely to follow accounts of White students compared to those of Black students; 21% more likely to follow accounts of students from top-ranked, prestigious universities compared to accounts of lower-ranked institutions; and 25% more likely to follow female compared to male students. The racial gap persisted even among students from top-ranked institutions, suggesting that Twitter users racially discriminate even in the presence of a signal that could be interpreted as indicative of high academic potential. Notably, we find that Black male students from top-ranked universities receive no more follow-backs than White male students from relatively lower-ranked institutions.