Limited individual attention and online virality of low-quality information

Abstract

Social media are massive marketplaces where ideas and news compete for our attention [1]. Previous studies have shown that quality is not a necessary condition for online virality [2] and that knowledge about peer choices can distort the relationship between quality and popularity [3]. However, these results do not explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy [4]. We investigate quality discrimination in a stylized model of online social network, where individual agents prefer quality information, but have behavioral limitations in managing a heavy flow of information. We measure the relationship between the quality of an idea and its likelihood to become prevalent at the system level. We find that both information overload and limited attention contribute to a degradation in the market's discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model predicts that high-quality information has little advantage over low-quality information. * These two authors contributed equally to this work

Political Discourse on Social Media

“…Furthermore, our study shows the interdependence between content production & consumption and network properties in the context of echo chambers. This finding could help us in revisiting existing models for the dynamics of opinion formation and polarization on social networks [11,34] that take into account not only the opinion (content) spread over the social network, but also its impact of structure of the network itself.…”

Influence of fake news in Twitter during the 2016 US presidential election

“…These investigations, as well as theoretical modeling [16,17], suggest that confirmation bias [18] and social influence results in the emergence, in online social networks, of user communities that share similar beliefs about specific topics, i.e. echo chambers, where unsubstantiated claims or true information, aligned with these beliefs, are as likely to propagate virally [6,19]. A comprehensive investigation of the spread of true and false news in Twitter also showed that false news is characterized by a faster and broader diffusion than true news mainly due to the attraction of the novelty of false news [12].…”

The spread of low-credibility content by social bots

“…A complex mix of cognitive, social, and algorithmic biases contribute to our vulnerability to manipulation by online misinformation [27]. These include information overload and finite attention [41], novelty of false news [57], the selective exposure [52,40,39] caused by polarized and segregated online social networks [9,8], algorithmic popularity bias [45,20,37], and other cognitive vulnerabilities such as confirmation bias and motivated reasoning [50,26,29].…”