Abstract. Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit 'static' relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities -i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world -e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts.

With the advent of Over-The-Top content providers (OTTs), Internet Service Providers (ISPs) saw their portfolio of services shrink to the low margin role of data transporters. In order to counter this effect, some ISPs started to follow big OTTs like Facebook and Google in trying to turn their data into a valuable asset. In this paper, we explore the questions of what meaningful information can be extracted from network data, and what interesting insights it can provide. To this end, we tackle the first challenge of detecting "user-URLs", i.e., those links that were clicked by users as opposed to those objects automatically downloaded by browsers and applications. We devise algorithms to pinpoint such URLs, and validate them on manually collected ground truth traces. We then apply them on a three-day long traffic trace spanning more than 19,000 residential users that generated around 190 million HTTP transactions. We find that only 1.6% of these observed URLs were actually clicked by users. As a first application for our methods, we answer the question of which platforms participate most in promoting the Internet content. Surprisingly, we find that, despite its notoriety, only 11% of the user URL visits are coming from Google Search.

Abstract. In earlier papers we characterised the notion of diachronic topicbased communities -i.e., communities of people who work on semantically related topics at the same time. These communities are important to enable topic-centred analyses of the dynamics of the research world. In this paper we present an innovative algorithm, called Research Communities Map Builder (RCMB), which is able to automatically link diachronic topic-based communities over subsequent time intervals to identify significant events. These include topic shifts within a research community; the appearance and fading of a community; communities splitting, merging, spawning other communities; etc. The output of our algorithm is a map of research communities, annotated with the detected events, which provides a concise visual representation of the dynamics of a research area. In contrast with existing approaches, RCMB enables a much more fine-grained understanding of the evolution of research communities, with respect to both the granularity of the events and the granularity of the topics. This improved understanding can, for example, inform the research strategies of funders and researchers alike. We illustrate our approach with two case studies, highlighting the main communities and events that characterized the World Wide Web and Semantic Web areas in the 2000 -2010 decade.

Most of our knowledge about online news consumption comes from survey-based news market reports, partial usage data from a single editor, or what people publicly share on social networks. This paper complements these sources by presenting the first holistic study of visits across online news outlets that a population uses to read news. We monitor the entire network traffic generated by Internet users in four locations in Italy. Together these users generated 80 million visits to 5.4 million news articles in about one year and a half. This unique view allows us to evaluate how usage data complements existing data sources. We find for instance that only 16% of news visits in our datasets came from online social networks. In addition, the popularity of news categories when considering all visits is quite different from the one when considering only news discovered on social media, or visits to a single major news outlet. Interestingly, a substantial mismatch emerges between self-reported news-category preferences (as measured by Reuters Institute in the same year and same country) and their actual popularity in terms of visits in our datasets. In particular, unlike self-reported preferences expressed by users in surveys that put “Politics”, “Science” and “International” as the most appreciated categories, “Tragedies and Weird news”’ and “Sport” are by far the most visited. We discuss two possible causes of this mismatch and conjecture that the most plausible reason is the disassociation that may occur between individuals’ cognitive values and their cue-triggered attraction.

One of the limits of web content discovery tools, let them be recommender systems or content curation tools such as social rating, social bookmarking and other social media, is the scarcity of user input (e.g. rate, submit, share). This problem is even worse in the case of what we call communities of a place: people who study, live or work at the same place. Such people often share common interests but either do not know each other or fail to actively engage in submitting and relaying information. In this paper, we investigate the feasibility of using the aggregated clicks of entire communities of users to passively emulate a content curation service a la Reddit. To this end, we prototype and deploy WeBrowse, a content curation service based on the processing of raw HTTP logs. Evaluation based on our deployments demonstrates feasibility at scale while respecting user privacy. The majority of WeBrowse's users welcome the quality of content it promotes.

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