Trust Models Go to the Web: Learning How to Trust Strangers

We study emerging traits of interpersonal and social trust in online social networks of needs (OSNNs), where trust interactions start online and evolve into in-person meetings. We present a lightweight web scraping solution to harness data from online social networks; thanks to it we were able to monitor a nation-wide portal for childcare and see the evolution of online reviews from both families and carers. We analysed the data by first considering topological information to test centrality metrics as proxies for trustworthiness. Next, we focused on features/profile analysis and tested the Castelfranchi–Falcone trust model from psychology (CF-T), fitting it to online reviews of childcare services. Even though such reviews are relatively scarce and seemingly skewed, we feature-engineered the CF-T model to predict the evolution of reviews, treated as proxies for trust. By aggregating CF-T scores at the regional level, we discovered a strong correlation with per capita GDP, which suggests that high levels of trust in social networks of needs reflect social capital.