This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years—the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where di erent users receive di erent results for the same search query. The increasing level of personalization is leading to concerns about Filter Bubble e ects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quanti cation of the extent of personalization in Web search today, or the user attributes that cause it.In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute di erences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search and 100 users on Bing. We nd that, on average, 11.7% of results show di erences due to personalization on Google, while 15.8% of results are personalized on Bing, but that this varies widely by search query and by result ranking. Third, we investigate the user features used to personalize on Google Web Search and Bing. Surprisingly, we only nd measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a rst step towards understanding the extent and e ects of personalization on Web search engines today.

Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. 1-3 A concurrent literature has emphasized the explorative nature of human behavior, showing that the number of visited places grows steadily over time. [4][5][6][7] How to reconcile these seemingly contradicting facts remains an open question. Here, we analyze high-resolution multi-year traces of ∼40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ∼25 locations. We use this finding to improve state-of-theart modeling of human mobility. 4, 8 Furthermore, shifting the attention from aggregated quantities to individual behavior, we show that the size of an individual's set of preferred locations correlates with the number of her social interactions. This result suggests a connection between the conserved quantity we identify, which as we show can not be understood purely on the basis of time constraints, and the 'Dunbar number' 9, 10 describing a cognitive upper limit to an individual's number of social relations. We anticipate that our work will spark further research linking the study of Human Mobility and the Cognitive and Behavioral Sciences.There is a disagreement between the current scientific understanding of human mobility as highly predictable and stable over time, 1,4,5 and the fact that individual lives are constantly evolving due to changing needs and circumstances. 11 The role of cultural, social and legal constraints on the spacetime fixity of daily activities has long been recognized. 2, 12, 13 Recent studies based on the analysis of human digital traces including mobile phone records, 14, 15 online location-based social networks, 16-20 and Global Positioning System (GPS) location data of vehicles 21-26 have shown that individuals universally exhibit a markedly regular pattern characterized by few locations, or points of interest, 27, 28 where 1 arXiv:1609.03526v3 [physics.soc-ph] 19 Jun 2018 they return regularly 6, 29 and predictably. 4 However, the observed regularity mainly concerns human activities taking place at the daily 28,30,31 or weekly 14, 15, 17 time-scales, such as commuting between home and office, 14, 15,32,33 pursuing habitual leisure activities, and socializing with established friends and acquaintances. 16 Thus, while the role played by slowly occurring changes on the evolution of individuals' social relationships has been widely investigated, 34-41 their effects on human mobility behavior are not well understood and not included in most available models. 4,8,[42][43][44][45][46][47] Here, we investigate individuals' routines across months and years. We reveal how individuals balance the trade-off between the exploitation of familiar places and the exploration of new opportunities...

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

We describe the multi-layer temporal network which connects a population of more than 700 university students over a period of four weeks. The dataset was collected via smartphones as part of the Copenhagen Networks Study. We include the network of physical proximity among the participants (estimated via Bluetooth signal strength), the network of phone calls (start time, duration, no content), the network of text messages (time of message, no content), and information about Facebook friendships. Thus, we provide multiple types of communication networks expressed in a single, large population with high temporal resolution, and over a period of multiple weeks, a fact which makes the dataset shared here unique. We expect that reuse of this dataset will allow researchers to make progress on the analysis and modeling of human social networks.