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A powerful means to help users discover new content in the overwhelmingamount of information available today is sharing in online communities such associal networks or crowdsourced platforms. This means comes short in the caseof what we call communities of a place: people who study, live or work at thesame place. Such people often share common interests but either do not knoweach other or fail to actively engage in submitting and relaying information.To counter this effect, we propose passive crowdsourced content discovery, anapproach that leverages the passive observation of web-clicks as an indicationof users' interest in a piece of content. We design, implement, and evaluateWeBrowse , a passive crowdsourced system which requires no active userengagement to promote interesting content to users of a community of a place.Instead, it extracts the URLs users visit from traffic traversing a networklink to identify popular and interesting pieces of information. We firstprototype WeBrowse and evaluate it using both ground-truths and real tracesfrom a large European Internet Service Provider. Then, we deploy WeBrowse in acampus of 15,000 users, and in a neighborhood. Evaluation based on ourdeployments shows the feasibility of our approach. The majority of WeBrowse'susers welcome the quality of content it promotes. Finally, our analysis ofpopular topics across different communities confirms that users in the samecommunity of a place share common interests, compared to users from differentcommunities, thus confirming the promise of WeBrowse's approach.

WeBrowse: Mining HTTP logs online for network-based content recommendation