Three new NERDS publications: Polarization, image-to-text-mapping, and candidate recommendation

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

  1. Estimating affective polarization on a social network, by Marilena Hohmann and Michele Coscia, published in PLOS ONE.

    Concerns about polarization and hate speech on social media are widespread. Affective polarization, i.e., hostility among partisans, is crucial in this regard as it links political disagreements to hostile language online. However, only a few methods are available to measure how affectively polarized an online debate is, and the existing approaches do not investigate jointly two defining features of affective polarization: hostility and social distance. To address this methodological gap, we propose a network-based measure of affective polarization that combines both aspects – which allows them to be studied independently. We show that our measure accurately captures the relation between the level of disagreement and the hostility expressed towards others (affective component) and whom individuals choose to interact with or avoid (social distance component). Applying our measure to a large-scale Twitter data set on COVID-19, we find that affective polarization was low in February 2020 and increased to high levels as more users joined the Twitter discussion in the following months.
    See also Michele’s blog post: https://www.michelecoscia.com/?p=2466
  2. Leveraging VLLMs for Visual Clustering: Image-to-Text Mapping Shows Increased Semantic Capabilities and Interpretability, by Luigi Arminio, Matteo Magnani, Matías Piqueras, Luca Rossi, and Alexandra Segerberg published in Social Science Computer Review.

    We test an approach that leverages the ability of Vision-and-Large-Language-Models (VLLMs) to generate image descriptions that incorporate connotative interpretations of the input images. In particular, we use a VLLM to generate connotative textual descriptions of a set of images related to climate debate, and cluster the images based on these textual descriptions. In parallel, we cluster the same images using a more traditional approach based on CNNs. In doing so, we compare the connotative semantic validity of clusters generated using VLLMs with those produced using CNNs, and assess their interpretability. The results show that the approach based on VLLMs greatly improves the quality score for connotative clustering. Moreover, VLLM-based approaches, leveraging textual information as a step towards clustering, offer a high level of interpretability of the results.
  3. Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring, by Mesut Kaya and Toine Bogers, published in RecSys ’25: Proceedings of the Nineteenth ACM Conference on Recommender Systems

    Past analyses of fairness in algorithmic hiring have been restricted to single-side fairness, ignoring the perspectives of the other stakeholders. In this paper, we address this gap and present a multi-stakeholder approach to fairness in a candidate recommender system that recommends relevant candidate CVs to human recruiters in a human-in-the-loop algorithmic hiring scenario. We conducted semi-structured interviews with 40 different stakeholders (job seekers, companies, recruiters, and other job portal employees). We used these interviews to explore their lived experiences of unfairness in hiring, co-design definitions of fairness as well as metrics that might capture these experiences. Finally, we attempt to reconcile and map these different (and sometimes conflicting) perspectives and definitions to existing (categories of) fairness metrics that are relevant for our candidate recommendation scenario.

Roberta Sinatra and Vedran Sekara talk at Rigsrevisionen

In August, NERDS faculty Roberta Sinatra and Vedran Sekara were invited to Rigsrevisionen, the Danish Audit national agency, to give a talk on bias in algorithms.

TheRoberta Sinatra giving a talk at Rigsrevisioneny presented their latest research project, focusing on how algorithmic bias can affect decisions in sensitive areas of public policy. In particular, they focused on their FAccT paper about a Decision Support System used by the Danish social sector, where algorithms have been tested to assess the risk of maltreatment for children. Their work showed that the algorithm was biased with respect to age: for instance, a 16-year-old shoplifter was rated at higher risk than a 2-month-old baby living with two parents struggling with substance abuse. This illustrates how algorithmic outputs can amplify bias if not critically examined, and why human oversight remains crucial.

The talk at Rigsrevisionen also raised the broader question of whether algorithms can be used responsibly for risk assessments in complex social contexts, and emphasized the need for careful scrutiny when deploying algorithmic solutions, especially AI-driven, in the public sector.

The event was organized by Rigsrevisionen’s internal data analytics network and drew a large audience, underscoring the relevance of these issues for public accountability and governance.

Read Rigsrevisionen’s post about the talk on LinkedIn →

Read the paper: “Failing Our Youngest: On the Biases, Pitfalls, and Risks in a Decision Support Algorithm Used for Child Protection” →

Read the Danish news piece “Kan algoritmer se ind i et barns fremtid?” →

Morten Boilesen has joined NERDS

We welcome our latest NERDS member: Morten Boilesen.Headshot of Morten

Morten joins as a new PhD student, coming with degrees in mathematics, musicology, and engineering, and experience as a start-up data scientist.

Morten will work with Jonas L Juul on the InForM project (funded by the Novo Nordisk Foundation), using Danish register data to study how COVID-19 spread in Denmark. 3 exciting years ahead! We are excited to have you with us, Morten. Welcome!

New NERDS publication on hitting the music charts

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.

Two new NERDS papers: NFT markets, settlement data

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.

Jonas Juul wins the H.C. Ørsted Research Talent Prize

On August 14 2025, our Jonas Juul was awarded the 2025 H.C. Ørsted Research Talent Prize.

Every year, the H.C. Ørsted society celebrates Danish physicist and father of electromagnetism H.C. Ørsted’s birthday with a grand party in Rudkøbing Langeland. At the party, the society hands out two prizes to talented researchers. This year, Jonas received one of these prizes in recognition of his outstanding research, its societal impact, and its potential for addressing security threats such as misinformation and epidemics.

At the party, Jonas was celebrated along with Nobel Prize winner Professor Morten Meldal (University of Copenhagen), who was awarded the main H.C. Ørsted Research Prize, Associate Professor Luisa Sinischalchi (Technical University of Denmark), who won the other H.C. Ørsted Research Talent prize, and several university students and school pupils who won monetary awards in recognition of excellence.

Congratulations, Jonas!

ITU also wrote about Jonas’ award here: https://en.itu.dk/About-ITU/Press/News-from-ITU/2025/Jonas-Juul-receives-the-HC-Orsted-Research-Talent-Award-2025

Jonas receiving the prize

Photo credit: Vedran Sekara

NERDS at IC2S2 in Norrköping

After several NERDS organizing, meeting up, presenting, and keynote-talking at various conferences over the summer (like ICWSM & ICSSI), we reached our grand finale at last week’s IC2S2 in Norrköping where at least 14 of us attended:

 

 

 

 

 

 

 

 

 

Apart from networking and enjoying the talks and discussions, we were also very busy in both the presenting talks and posters departments. Here just a few of the many impressions:


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Congratulations to the organizers of this year’s IC2S2 for another smashing edition! We know how hard it is to organize an IC2S2 and make it so successful, and hope you will see us at many more future IC2S2. Over the years this conference has become the one which us NERDS tend to attend in highest numbers. Apart from the general fit to our research, this is also thanks to the awesomely open computational social science community and the always exciting mix of sub-topics plus fresh new keynotes. What a time to be doing computational social science!

One of the next times you will see again several of us will be at the CCS 2025 in Siena. See you around!

New NERDS publication on the BikeNodePlanner tool

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.

NERDS at NetSci 2025 in Maastricht

The NERDS have just returned from an exciting week at the NetSci conference in Maastricht.

Alessia G. presented her work on mapping climate discourse on TikTok both at the main conference and at the UNCSS satellite. She also spearheaded the TENET satellite, which turned out to be a real hit!

Michele gave us a tour through time with a talk on the economic complexity of the Roman Empire, and another on piecing together the social networks of Çatalhöyük using clues from material culture.

Elisabetta shared her research on fairness in network rankings at the Women in Network Science satellite, and also in the main conference.

Anastassia and Luca jumped right into the action, joining the scientific discussions.

We also had the joy of reuniting with our former NERDS Alessia A. and Daniele, who were part of the sweet Honai satellite🍯.

Last but not least, a special shout-out to Alessia G., mastermind of the wildly successful “Match them all!” game, created with the awesome folks at NetPlace.

All in all, it was a full and rewarding week—one that reinforced how vibrant and collaborative the network science community continues to be.

Three new NERDS publications: Collective action, wildfire smoke, and urban mobility

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.