Collective response to the media coverage of COVID-19 pandemic on Reddit and Wikipedia: mixed-methods analysis

Background: The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioral changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. Objective: The goal of this work is to provide a characterization of media coverage and online collective response to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. Methods: We collect a heterogeneous dataset including 227’768 online news articles and 13’448 YouTube videos published by mainstream media, 107’898 users posts and 3’829’309 comments on the social media platform Reddit, and 278’456’892 views to COVID-19 related Wikipedia pages. Results: Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural change

Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

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X-hinter: a framework for implementing social oriented recommender systems

Accordingly to the nature of data-driven applications that produce information overload, users need a support to make choices, even without sufficient personal experience of the alternatives. In this context, social networking techniques could be useful applied for finding affinities between users and filter information in a personalized way. After proposing a generalized model for social recommender systems, called X-Hinter, we describe a Java API that provides a set of libraries and tools to build social filtering systems in a wide range of domains. A prototype implementation, named DeHinter, shows the feasibility of the proposed approach in a P2P file sharing application

DeHinter: A Social-oriented Peer-to-Peer Recommender System

DeHinter is a Peer-to-Peer (P2P) recommender system that exploits social filtering techniques in order to implement a fully decentralized resource sharing platform. The system provides to users a way to share, search and retrieve contents in a scalable, flexible and efficient way. The spontaneous relationships between users that show similar interests shape highly connected thematic clusters that can be exploited to provide personalized advices. DeHinter's goal is to reduce the impact of the information overload providing a decentralized, autonomous and efficient way to filter contents exploiting social-oriented phenomena. The P2P communication layer of DeHinter is based on the P2P Gnutella protocol, that is a fully distributed overlay network. Main features: - Starts from the evidence that affinities between Gnutella users form scale-free and small world patterns. - Implement a distributed recommender engine able to suggest contents in a transparent way. - Fully decentralized and autonomous. - Exploits a de facto “word of mouth” mechanism. - Independent from content metadata descriptions or a specific ontology. - Users can give both implicit and explicit feedbacks. - Simple, completely anonymous and protecting users privacy mechanism to gather usage data for evaluation purposes

X-Hinter

Social applications are rapidly popularizing collaborative tools for indexing, retrieval, access and distribution of content over the Internet. Very recently navigational paradigm has emerged due to the diffusion of folksonomies within popular tagging systems (e.g., Flickr, del.icio.us, and so on); in fact, folksonomies have been showed to overperform monolithic hierarchical classifications in social domains where many users with different mental attitudes and vocabularies are active. Moreover, social tagging provides us with a powerful way to manage online resources collaboratively. Users can freely choose words to describe resources and therefore resources get descriptions from various users represented as sets of tags, collectively forming a folksonomy. Tags are widely used in many Web 2.0 and social media Web sites, by which users can organize their own collections, discover interesting resources, and find friends with similar interests. Annotations are proved to be a useful source for aggregated services like social search, social link suggestion or personalized recommendation services. The descriptive power of folksonomies can be exploited to provide a recommender engine to cope with the problem of information overloading in digital media dataset. The X-Hinter project provides a REST API in python that provides the following services to social applications and mashups: - A Tagging Service that enables the users to annotate media content and services with the possibility to search and navigate them according to the tag space. - A Rating Service that provides a way to collect explicit feedbacks on content - A Recommender Service that suggests to the users media content related to their personal taste

MobHinter: Epidemic Collaborative Filtering and Self-Organization in Mobile Ad-Hoc Networks

We focus on collaborative filtering dealing with self-organizing communities, host mobility, wireless access, and ad-hoc communications. In such a domain, knowledge representation and users profiling can be hard; remote servers can be often unreachable due to client mobility; and feedback ratings collected during random connections to other users' ad-hoc devices can be useless, because of natural differences between human beings. Our approach is based on so called Affinity Networks, and on a novel system, called MobHinter, that epidemically spreads recommendations through spontaneous similarities between users. Main results of our study are two fold: firstly, we show how to reach comparable recommendation accuracies in the mobile domain as well as in a complete knowledge scenario; secondly, we propose epidemic collaborative strategies that can reduce rapidly and realistically the cold start problem