This study investigates the influence of toxic language in hate speech targeting immigrants, particularly through narrative formats like first-person X (Twitter) threads. Hate speech, defined as promotion of hatred based on personal or group characteristics, increasingly escalates
on social media, impacting public attitudes and behaviors. While previous research
has primarily focused on measuring the scope of hate speech through content analysis and computational methods, there has been limited attention to its effects on audiences.
This study presents the results of an online experiment (N = 339) with a 2 × 2 between subjects design that manipulates the presence of toxic language and message popularity.
Results indicate that hate messages lacking toxic language promote greater identity fusion with the author of the message, which in turn increases the intention to share the message,
reinforces negative attitudes toward immigrants, and increases support for harsh policies against irregular immigration. Moreover, non-toxic hate messages significantly enhance narrative transportation exclusively for individuals with conservative political views, thereby further increasing their intention to share the message. These findings highlight that subtler forms of hate speech can create strong audience connections with
hostile perspectives, emphasizing the need for anti-hate campaigns to address both overt and subtle hate content.
