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.

We have two new publications out!

1. Emergent social conventions and collective bias in LLM populations, by Ariel Flint Ashery, Luca Maria Aiello, and Andrea Baronchelli, published in Science Advances.
   We explore the collective behavior of LLMs starting from social conventions, the fundamental building blocks of coordinated societies. We used the naming game, a well-established framework that has been applied for decades to study conventions in humans. We found that LLM populations can spontaneously develop shared conventions through local interactions. These interactions can produce collective biases, invisible at the individual level, raising important considerations for AI alignment. Small committed minorities can trigger tipping points, steering the entire group toward new conventions—a dynamic well known in human societies
2. The experience of running: Recommending routes using sensory mapping in
   urban environments, by Katrin Hänsel, Luca Maria Aiello, Daniele Quercia, Rossano Schifanella, Krisztian Zsolt Varga, Linus W. Dietz, and Marios Constantinides, published in the International Journal of Human-Computer Studies.
   We set out to build running routes not around distance, but around how people feel: before, during, and after a run. We surveyed 387 runners and found that not everyone wants the same kind of run. Some seek quiet and greenery; others thrive on the buzz of people and traffic. Their preferences often match their personality. Runners who prefer scenic paths (quiet, green, and natural) tended to score higher in neuroticism. Those who preferred urban paths (lively and full of people) were more likely to be extroverted. Then, we built a routing engine. Using millions of geotagged Flickr photos and open London data, we scored streets for beauty, noise, safety, and surface quality. We tested the engine on hundreds of 5-km London loops. Most runners preferred the scenic routes.

We have two new publications out at CHIIR 2025 in Melbourne (Proceedings of the 2025 ACM SIGIR Conference on Human Information Interaction and Retrieval):

1. Exploring the Zero-Shot Known-Item Retrieval Capabilities of LLMs for Casual Leisure Information Needs, by T. Bogers, M. Gäde, M. Hall, M. Koolen, V. Petras, M. Skov, published in CHIIR 2025
   We compared four LLMs on their capability to answer a specific type of complex search task: known-item requests from casual leisure domains. We constructed a test collection by gathering known-item requests for books, games and movies from online forums along with verified answers by the original requester. We prompted four LLMs multiple times with the same prompt and analyzed the results with respect to accuracy and the degree to which answers were fabricated by the LLM. Our results show that LLMs are not particularly effective in fulfilling these complex casual leisure needs, but there are are big differences between LLMs and across domains.
2. From Queries to Candidates: Exploring Search and Source Interaction Behavior of Recruiters, by T. Bogers, M. Kaya, M. Gäde, published in CHIIR 2025
   
   Recruitment is a professional search domain that has been largely overlooked in IR research, even though better support of recruiters could have a big impact on job seekers, companies and society as a whole. In this paper, we analyze the search formulation and source selection behavior of the recruiters at one of Scandinavia’s largest job portals and recruitment agencies using search logs for close to 18,000 recruitment search tasks. We provide an analysis of the search sessions of recruiters in terms search tactics, query operators, query length, term re-use and filter usage, and break down their behavior both by task type and task complexity. We also relate their short-term tactics to different learning stages in the search process and investigate their influence on search success. We find that identifying and assessing relevant candidates for a job posting is a complex task: recruiters usually submit multiple queries during sessions that can last for hours and that increase in complexity. Recruiters all spend more time per query as their session progresses. We also observed query reformulation strategies that indicate distinct patterns of knowledge gaining during sessions. Relating these tactics to positive responses from candidates we aim at predicting successful strategies.

We have two new publications out, one on closing triads in social networks, and one on shaded walks (CoolWalks) in cities:

1. Exploring Time-Ordered Triadic Closure in Online Social Networks, by A. Galdeman, C. Tidiane Ba, M. Zignani, S. Gaito, published in ACM Transactions on the Web
   
   By analyzing networks with timestamped links from diverse platforms based on different architectures, including communication, Web3-based, and trade networks, we developed a comprehensive analytical pipeline to support the study of triadic closure patterns. This pipeline includes an algorithm for the census of time-ordered triads, a vector-based model for representing growing networks (growth triadic profile), the identification of triadic closure rules (TERs), and the evaluation of the speed of the formation of closed triads. Our findings reveal significant variations in the impact of triadic closure across different OSNs, marked by diverse growth triadic profiles and varying formation speeds of closed triads as well as diversity in the predictability of evolutionary patterns based on triads. This study not only enhances the comprehension of triadic closure in the temporal evolution of OSNs but also provides valuable insights to be taken into account for the design and administration of online social platforms.
2. CoolWalks for active mobility in urban street networks, by H. Wolf, A.R. Vierø & M. Szell, published in Scientific Reports
   
   Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter alpha and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of alpha, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.