Polarized platforms? How partisanship shapes perceptions of algorithmic news bias

The use of artificial intelligence-based algorithms for the curation of news content by social media platforms like Facebook and Twitter has upended the gatekeeping role long held by traditional news outlets. This has caused some US policymakers to argue that platforms are skewing news diets against them, and such claims are beginning to take hold among some voters. In a nationally representative survey experiment, we explore whether traditional models of media bias perceptions extend to beliefs about algorithmic news bias. We find that partisan cues effectively shape individuals' attitudes about algorithmic news bias but have asymmetrical effects. Specifically, whereas in-group directional partisan cues stimulate bias perceptions for members of both parties, Democrats, but not Republicans, also respond to out-group cues. We conclude with a discussion about the implications for the formation of attitudes about new technologies and the potential for polarization.

Automated summary

The use of AI algorithms by social media platforms for curating news content has disrupted the traditional gatekeeping role of traditional news outlets. This has led to debates among US policymakers regarding platform bias and has gained traction among some voters. A survey experiment reveals that partisan cues effectively shape attitudes towards algorithmic news bias, but their effects are asymmetrical.