People think that social media platforms do (but should not) amplify divisive content

Abstract

Recent studies have documented the type of content that is most likely to spread widely, or go “viral” on social media, yet little is known about people’s beliefs of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a nationally representative sample of US participants and surveyed them about their perceptions of social media virality (n = 511). In line with prior research, people believe that divisive content, moral outrage, negative content, high-arousal content, and misinformation are all likely to go viral online. However, they reported that this type of content should not go viral on social media. Instead, people reported that many forms of positive content – such as accurate content, nuanced content, and educational content – are not likely to go viral, even though they think this content should go viral. Importantly, these beliefs were shared among most participants, and were only weakly related to political orientation, social media usage, and demographic variables. The overwhelming majority supported greater transparency around social media algorithms (92%), and desired greater control over social media algorithms (86%). In sum, there is broad consensus around the type of content social media platforms should and should not amplify, which can help inform solutions for improving social media.

Accuracy and social motivations shape judgements of (mis)information

“…Then, they were told that they would receive a bonus payment of up to $1.00 based on how accurately they identified information that would be liked by members of their political party if they shared it on social media. Bonuses were awarded on the basis of how closely participants' answers matched partisan alignment scores from a pre-test 48 . Before each question about accuracy and sharing, participants were asked 'If you shared this article on social media, how likely is it that it would receive a positive reaction from [your political party] (for example, likes, shares, and positive comments)?'…”

“…We also examine whether social identity-based motivations to identify posts that will be liked by one's political in-group interfere with accuracy motivations. On social media, content that appeals to social-identity motivations, such as expressions of out-group derogation, tends to receive high engagement online [46][47][48] . False news stories may be good at fulfilling these identity-based motivations, as false content is often negative about out-group members 26,49 .…”

Social drivers and algorithmic mechanisms on digital media

“…Avoiding to perpetuate the influence of social and cognitive biases will require asking for user decisions in advance and at an abstract level. When asked to reflect, rather than when thoughtlessly scrolling through a feed, a majority of users across political and demographic groups opts for seeing more accurate, nuanced, friendly, positive and educational content (Rathje et al, 2022), although such content currently does not typically go viral by itself. It needs to be tested if users would actually make such decisions on a platform, and if this would reduce misinformation and polarization at the macro-scale of digital platforms.…”

“…It needs to be tested if users would actually make such decisions on a platform, and if this would reduce misinformation and polarization at the macro-scale of digital platforms. Similarly, research needs to test if users, after reflection, would actually make the choices they report preferring (Rathje et al, 2022), or give in to attention and social biases.…”

“…Because algorithmic content distribution gives greater control to the platform, compared to popular users, algorithms could select content based on metrics that foster well-being of individuals and societies. They could highlight overlap between opposed groups (Bail, 2021), prioritize news a user actually wants to see (Rathje et al, 2022), or simply limit how far fake news spreads (Bak-Coleman et al, 2022).…”