Name
Abstract | ||
|---|---|---|
Online social media provide users with unprecedented opportunities to engage with diverse opinions. Simultaneously, they allow the spread of misinformation by empowering individuals to self-select the narratives they want to be exposed to, both through active (confirmation bias) and passive (personalized news algorithms) self-reinforcing mechanisms. A precise theoretical understanding of such trade-offs is still largely missing. We introduce a stylized social learning model where most participants in a network update their beliefs unbiasedly based on the arrival of new information, while a fraction of participants display confirmation bias, enabling them to reject news that are incongruent with their pre-existing beliefs. We show that this simple confirmation bias mechanism can generate permanent opinion polarisation. Furthermore, the model results in states where unbiased agents behave if they were biased, due to their biased neighbours effectively functioning as gatekeepers, restricting their access to free and diverse information. We derive analytic results for the distribution of individual agentsu0027 beliefs, explicitly demonstrating the aforementioned trade-off between confirmation bias and social connectivity, which we further validate against US county-level data on the impact of Internet access on the formation of beliefs about global warming. Our findings indicate that confirmation bias in small doses may actually result in improved accuracy across individuals by preserving information diversity in a social network. However, results also indicate that when confirmation bias grows past an optimal value, accuracy declines as biased agents restrict information flow to subgroups. We discuss the policy implications of our model, highlighting the downside of debunking strategies and suggesting alternative strategies to contrast misinformation. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Physics and Society | Social learning theory,Information flow (information theory),Confirmation bias,Social network,Social media,Stylized fact,Cognitive psychology,Misinformation,Artificial intelligence,Mathematics,restrict,Machine learning |
DocType | Volume | Citations |
Journal | abs/1808.08524 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Orowa Sikder | 1 | 0 | 0.34 |
| Robert E. Smith | 2 | 0 | 0.68 |
| Pierpaolo Vivo | 3 | 0 | 0.68 |
| Giacomo Livan | 4 | 9 | 3.78 |