Prolific NERDS researcher Luca Maria Aiello published 2 more papers over the summer. They already received wide media coverage:

1. Streetonomics: Quantifying culture using street names, by M. Bancilhon, M. Constantinides , E.P. Bogucka, L.M. Aiello, D. Quercia, published in PLOS ONE
   

   This paper studies the names of 4,932 honorific streets in the cities of Paris, Vienna, London and New York, finding that street names greatly reflect the extent to which a society is gender biased, which professions are considered elite ones, and the extent to which a city is influenced by the rest of the world, quantifying a society’s value system.

   The paper was covered in media here:
   https://www.bbc.com/future/article/20210712-streetonomics-what-our-addresses-say-about-us
   https://www.fastcompany.com/90652762/how-streets-in-new-york-london-paris-and-vienna-got-their-names-according-to-streetonomics
   https://www.thetimes.co.uk/article/street-names-show-why-great-cities-are-worlds-apart-x06lbdwgj
   https://elpais.com/ciencia/2021-06-30/el-machismo-esta-en-las-calles.html
   https://www.lefigaro.fr/sciences/l-ame-d-une-ville-peut-elle-se-lire-dans-les-noms-de-ses-rues-20210701

2. How epidemic psychology works on Twitter: evolution of responses to the COVID-19 pandemic in the U.S., by L.M. Aiello, D. Quercia, K. Zhou, M. Constantinides, S. Šćepanović, S. Joglekar, published in Humanities and Social Sciences Communications
   
   The paper studies the use of language of 122M tweets related to the COVID-19 pandemic posted in the U.S. during the whole year of 2020. On Twitter, we identified three distinct phases. Each of them is characterized by different regimes of the three psycho-social epidemics.
   See also: https://www.fastcompany.com/90659372/pandemic-emotions-research-twitter

This month we have published four new papers, in Nature, Sustainability, IEEE Computer Graphics and Applications, and ACM Computing Surveys:

1. The universal visitation law of human mobility, by M. Schläpfer, L. Dong, K. O’Keeffe, P. Santi, M. Szell, H. Salat, S. Anklesaria, M. Vazifeh, C. Ratti, G.B. West, published in Nature
   More info at the accompanying interactive website: https://senseable.mit.edu/wanderlust/
   

   This paper reveals a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe.

2. Implementing Gehl’s Theory to Study Urban Space. The Case of Monotowns, by D. Cerrone, J. López Baeza, P. Lehtovuori, D. Quercia, R. Schifanella, and L.M. Aiello, published in Sustainability
   
   The paper presents a method to operationalize Jan Gehl’s questions for public space into metrics to map Russian monotowns’ urban life in 2017. With the use of social media data, it becomes possible to scale Gehl’s approach from the survey of small urban areas to the analysis of entire cities while maintaining the human scale’s resolution.
3. The Dreamcatcher: Interactive Storytelling of Dreams, by E.P. Bogucka, B.A. Aseniero, L.M. Aiello, D. Quercia, published in IEEE Computer Graphics and Applications
   
   Here we designed “The Dreamcatcher,” an interactive visual tool that explores the link between dreams and waking life through a collection of dream reports. We conducted a user study with 154 participants and found a 25% increase in the number of people believing that dream analysis can improve our daily lives after interacting with our tool. The visualization informed people about the potential of the continuity hypothesis to a surprising extent, to the point that it increased their concerns about sharing their own dream reports, thus opening new questions on how to design privacy-aware tools for dream collection.
4. Community Detection in Multiplex Networks, by M. Magnani, O. Hanteer, R. Interdonato, L. Rossi, A. Tagarelli, published in ACM Computing Surveys
   
   Here we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. Besides offering a much needed overview of the methods and assumptions of CD in multiplex networks the paper attempts to provide few guiding principles for the choice of a community detection approach to multiplex data. 

We have a new exciting paper out: Temporal and cultural limits of privacy in smartphone app usage by Vedran Sekara et al. published in Nature Scientific Reports, asking:

The paper looks into which apps people use and creates app-fingerprints for 3.5 million individuals. Similar to forensic science where you need 12 points to distinguish between fingerprints we ask how many apps do we need to distinguish between two users? We find people’s smartphone app behavior is very unique and 3 apps are enough to identify more than 90% of all individuals. But app-fingerprints change over time and are different between countries. We find that people have more unique app-fingerprints during summer because we use more unique apps, and Americans have the most unique fingerprints (need the fewest apps to identify them) while Finns are the least unique (need more apps to identify their fingerprint). Why is this important? Because the work highlights problems with current policies intended to protect user privacy and emphasizes that policies cannot directly be ported between countries.