The Effect of People Recommenders on Echo Chambers and Polarization
Bibliographic Data
| ID | 7520252 |
|---|---|
| Authors | Federico Cinus (0009-0007-5910-7354, Institute for Scientific Interchange, corresponding author), Marco Minici (University of Pisa), Corrado Monti (0000-0001-6846-5718, Institute for Scientific Interchange), Francesco Bonchi (0000-0001-9464-8315) |
| Year | 2022 |
| Volume | 16 |
| Pages | 90-101 |
| Publication date | 2022-05-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Proceedings of the International AAAI Conference on Web and Social Media (JOURNAL) |
| Journal identifiers | ISSN: 2162-3449 • E-ISSN: 2334-0770 |
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) (PUBLISHER) |
| DOI | 10.1609/icwsm.v16i1.19275 |
| OpenAlex | W3216636428 |
| Language | EN |
| Citations received | 7 |
| References cited | 18 |
The effects of online social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the information and the opinions users are exposed to. In this paper, we propose a novel framework to assess the effect of people recommenders on the evolution of opinions. Our proposal is based on Monte Carlo simulations combining link recommendation and opinion-dynamics models. In order to control initial conditions, we define a random network model to generate graphs with opinions, with tunable amounts of modularity and homophily. Finally, we join these elements into a methodology able to study the causal relationship between the recommender system and the echo chamber effect. Our method can also assess if such relationships are statistically significant. We also show how such a framework can be used to measure, by means of simulations, the impact of different intervention strategies. Our thorough experimentation shows that people recommenders can in fact lead to a significant increase in echo chambers. However, this happens only if there is considerable initial homophily in the network. Also, we find that if the network already contains echo chambers, the effect of the recommendation algorithm is negligible. Such findings are robust to two very different opinion dynamics models, a bounded confidence model and an epistemological model
Data science · Homophily · Information retrieval · Misinformation · Modularity (biology · Preprint · Recommender system · Social media · Social network (sociolinguistics · World Wide Web · Complex Network Analysis Techniques · Computer Science · Opinion Dynamics and Social Influence · Psychology · Social Media and Politics · Social Psychology
YSocial
Algorithmic recommendations and echo chambers
Mitigating radicalization in recommender systems by rewiring graph with deep reinforcement learning
How Misinformation Diffuses on Online Social Networks
Coevolution of Opinion Dynamics and Recommendation System
Exposure Inequality in People Recommender Systems
AI and the problem of knowledge collapse
The link‐prediction problem for social networks
Benchmark graphs for testing community detection algorithms
Authoritative sources in a hyperlinked environment
Mixing beliefs among interacting agents
Echo Chambers on Facebook
Learning from Neighbours
Tweeting From Left to Right
Exposure to ideologically diverse news and opinion on Facebook
The Geographic Embedding of Online Echo Chambers
Social networks that matter
Role of the New Media in the Arab Spring
Are people incidentally exposed to news on social media? A comparative analysis
Echo chambers online
Twitter as arena for the authentic outsider
| Citations per year | 1.75 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |