We have a new exciting publication out! 🎸

Is it getting harder to make a hit? Evidence from 65 years of US music chart history, by Marta Ewa Lech, Sune Lehmann & Jonas L. Juul, published in EPJ Data Science

We show that the dynamics of the Billboard Hot 100 chart have changed significantly since the chart’s founding in 1958, and, in particular, in the past 15 years. Whereas most songs spend less time on the chart now than songs did in the past, we show that top-1 songs have tripled their chart lifetime since the 1960s, and the highest-ranked songs maintain their positions for far longer than previously. At the same time, churn has increased drastically, and the lowest-ranked songs are replaced more frequently than ever. Together, these observations support two competing and seemingly contradictory theories of digital markets: The Winner-takes-all theory and the Long Tail theory. Who occupies the chart has also changed over the years: In recent years, fewer new artists make it into the chart and more positions are occupied by established hit makers. Finally, investigating how song chart trajectories have changed over time, we show that historical song trajectories cluster into clear trajectory archetypes characteristic of the time period they were part of. Our results are interesting in the context of collective attention: Whereas recent studies have documented that other cultural products such as books, news, and movies fade in popularity quicker in recent years, music hits seem to last longer now that in the past.

We have two new publications out!

1. Characterizing NFT Markets through a Multilayer Network Approach, by Alessia Galdeman, Lucio La Cava, Matteo Zignani, Andrea Tagarelli, Sabrina Gaito published in Blockchain: Research and Applications
   
   In this study, we explore a multilayer network modeling approach to analyze transactions in multiple NFT markets. We reveal previously unnoticed macroscopic and mesoscopic traits by investigating indicators that discern whether markets are independent or linked: users trading NFTs are organized in cross-market communities where multi-market users act as bridges across marketplaces, adapting to the diverse nature of the markets they operate in. We also conduct an in-depth examination of such multi-market users, studying their specific activity patterns that leave a distinctive mark on the system: the majority of multi-market users well differentiate their earnings and expenses among the markets, while a fraction of them is directed toward a more polarized money allocation based on the typology of the markets.
2. Uncovering large inconsistencies between machine learning derived gridded settlement datasets, by Vedran Sekara, Andrea Martini, Manuel Garcia-Herranz & Do-Hyung Kim, published in EPJ Data Science
   
   We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes.

We have a new publication out on a piece of software we wrote:

BikeNodePlanner: A data-driven decision support tool for bicycle node network planning, by A. Vybornova, A.R. Vierø, K.K. Hansen, and M. Szell, published in Environment and Planning B

A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: A fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.

We have three new publications out, on a variety of topics!

1. Extracting Participation in Collective Action from Social Media, by Arianna Pera and Luca Maria Aiello, published in Proceedings of the International AAAI Conference on Web and Social Media.
   
   We present a novel suite of text classifiers designed to identify expressions of participation in collective action from social media posts, in a topic-agnostic fashion. Grounded in the theoretical framework of social movement mobilization, our classification captures participation and categorizes it into four levels: recognizing collective issues, engaging in calls-to-action, expressing intention of action, and reporting active involvement.  We constructed a labeled training dataset of Reddit comments through crowdsourcing, which we used to train BERT classifiers and fine-tune Llama3 models. Our findings show that smaller language models can reliably detect expressions of participation (weighted F1=0.71), and rival larger models in capturing nuanced levels of participation.
2. Disruption of outdoor activities caused by wildfire smoke shapes circulation of respiratory pathogens, by Beatriz Arregui-García, Claudio Ascione, Arianna Pera, Boxuan Wang, Davide Stocco, Colin J. Carlson, Shweta Bansal, Eugenio Valdano, Giulia Pullano published in PLOS Climate.
   
   This study investigates how wildfire-induced changes in human behavior during the U.S. West Coast wildfires of 2020 may affect the spread of airborne diseases. Using a mobility data-driven indoor activity index, we find that the wildfire-induced deterioration of air quality led to a substantial increase in indoor activities, fostering conditions conducive to airborne disease transmission. Specifically, counties in Oregon and Washington experienced an average 10.8% and 14.3% increase in indoor activity, respectively, during the wildfire events, with major cities like Portland and Seattle experiencing increases of 11% and 16%, respectively. We quantify these behavioral changes and integrate them into an SIR epidemic model to characterize the increased indoor activity and disease dynamics. The model predicts the greatest impact on diseases with shorter generation times, such as RSV and influenza.
3. Urban Mobility, by Laura Alessandretti and Michael Szell, book chapter in Compendium of Urban Complexity (Springer)
   
   In this chapter, we discuss urban mobility following a complexity science approach. First, we give an overview of the datasets that enable this approach, such as mobile phone records, location-based social network traces, or GPS trajectories from sensors installed on vehicles. We then review the empirical and theoretical understanding of the properties of human movements, including the distribution of travel distances and times, the entropy of trajectories, and the interplay between exploration and exploitation of locations. Next, we explain generative and predictive models of individual mobility, and their limitations due to intrinsic limits of predictability. Finally, we discuss urban transport from a systemic perspective, including system-wide challenges like ridesharing, multimodality, and sustainable transport.