Piotr Sapiezynski

Measuring Large-Scale Social Networks with High Resolution

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.

Tracking human mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking.

Evidence of complex contagion of information in social media: An experiment using Twitter bots

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.

Opportunities and Challenges in Crowdsourced Wardriving

Knowing the physical location of a mobile device is crucial for a number of context-aware applications. This information is usually obtained using the Global Positioning System (GPS), or by calculating the position based on proximity of WiFi access points with known location (where the position of the access points is stored in a database at a central server). To date, most of the research regarding the creation of such a database has investigated datasets collected both artificially and over short periods of time (e.g., during a one-day drive around a city). In contrast, most in-use databases are collected by mobile devices automatically, and are maintained by large mobile OS providers. As a result, the research community has a poor understanding of the challenges in creating and using large-scale WiFi localization databases. We address this situation using the deployment of over 800 mobile devices to real users over a 1.5 year period. Each device periodically records WiFi scans and its GPS coordinates, reporting the collected data to us. We identify a number of challenges in using such data to build a WiFi localization database (e.g., mobility of access points), and introduce techniques to mitigate them. We also explore the level of coverage needed to accurately estimate a users location, showing that only a small subset of the database is needed to achieve high accuracy.

Inferring Stop-Locations from WiFi

Human mobility patterns are inherently complex. In terms of understanding these patterns, the process of converting raw data into series of stop-locations and transitions is an important first step which greatly reduces the volume of data, thus simplifying the subsequent analyses. Previous research into the mobility of individuals has focused on inferring ‘stop locations’ (places of stationarity) from GPS or CDR data, or on detection of state (static/active). In this paper we bridge the gap between the two approaches: we introduce methods for detecting both mobility state and stop-locations. In addition, our methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select the most important routers and one which uses a density-based clustering algorithm to detect router fingerprints. We validate our results using participants’ GPS data as well as ground truth data collected during a two month period.

Evidence for a Conserved Quantity in Human Mobility

Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations1–3. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time4–7. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse 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. We use this finding to improve state-of-the-art modelling of human mobility4,8. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot 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 behavioural sciences.Analysing high-resolution mobility traces from almost 40,000 individuals reveals that people typically revisit a set of 25 familiar locations day-to-day, but that this set evolves over time and is proportional to the size of their social sphere.

Academic performance and behavioral patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

Inferring Person-to-person Proximity Using WiFi Signals

Todays societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. While mobility is an important aspect of human behavior, it is also crucial to study physical interactions among individuals. Sensing proximity that enables social interactions on a large scale is a technical challenge and many commonly used approaches—including RFID badges or Bluetooth scanning—offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth Bluetooth proximity collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals as a tool for social sensing and show how collections of WiFi data pose a potential threat to privacy.

Multi-scale spatio-temporal analysis of human mobility

The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals’ digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life.

The role of gender in social network organization

The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than 800 university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individual’s characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive?