We published a new paper:

THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT, by V. Sekara, M. Karsai, E. Moro, D. Kim, E. Delamonica, M. Cebrian, M. Luengo-Oroz, R. Moreno Jimenez, M. Garcia-Herranz, published in Advances in Complex Systems

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

We published a new multi-NERDS paper, concluding a successful previous internship of Chiara Zappalà!

Early career wins and tournament prestige characterize tennis players’ trajectories, by C. Zappalà, S. Sousa, T. Cunha, A. Pluchino, A. Rapisarda and R. Sinatra, published in EPJ Data Science

We study the unfolding of tennis players’ careers to understand the role of early career stages and the impact of specific tournaments on players’ trajectories. We employ a comprehensive approach combining network science and analysis of the Association of Tennis Professionals (ATP) tournament data and introduce a novel method to quantify tournament prestige based on the eigenvector centrality of the co-attendance network of tournaments. Focusing on the interplay between participation in central tournaments and players’ performance, we find that the level of the tournament where players achieve their first win is associated with becoming a top player. This work sheds light on the critical role of the initial stages in the progression of players’ careers, offering valuable insights into the dynamics of success in tennis.

We published a new all-NERDS paper, applying our BikeDNA tool to the whole country of Denmark as part of our Cykelpulje project!

How Good Is Open Bicycle Network Data? A Countrywide Case Study of Denmark, by A. Rahbek Vierø, A. Vybornova, and M. Szell, published in Geographical Analysis

We compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data.

Explore also the interactive map: https://anerv.github.io/bikedna_webmap/

We have been very productive this year already! Five new NERDS publications are released this week:

1. Which sport is becoming more predictable? A cross-discipline analysis of predictability in team sports, by M. Coscia, published in EPJ Data Science
   
   

   We analyze more than 300,000 professional sports matches in the 1996-2023 period from nine disciplines, to identify which disciplines are getting more/less predictable over time. We investigate the home advantage effect, since it can affect outcome predictability and it has been impacted by the COVID-19 pandemic. Going beyond previous work, we estimate which sport management model – between the egalitarian one popular in North America and the rich-get-richer used in Europe – leads to more uncertain outcomes. Our results show that there is no generalized trend in predictability across sport disciplines, that home advantage has been decreasing independently from the pandemic, and that sports managed with the egalitarian North American approach tend to be less predictable. We base our result on a predictive model that ranks team by analyzing the directed network of who-beats-whom, where the most central teams in the network are expected to be the best performing ones.

2. Algorithmic Fairness: Learnings From a Case That Used AI For Decision Support, by V. Sekara, T.S. Skadegard Thorsen, and R. Sinatra, published by the Crown Princess Mary Center
   
   

   This policy brief provides a small introduction to algorithmic fairness and an example of auditing fairness in an algorithm which was aimed at identifying and assessing children at risk from abuse.

3. The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks, by A.G. Møller, J.A. Dalsgaard, A. Pera, L.M. Aiello (accepted at EACL’24).
   How good are Large Language Models in generating synthetic examples for training classifiers? To find out, we used GPT4 and Llama2 to augment existing training sets for typical Computational Social Science tasks. Our experiments show that the time to replace human-generated training data with LLMs has yet to come: human-generated text and labels provide more valuable information during training for most tasks. However, artificial data augmentation can add value when encountering extremely rare classes in multi-class scenarios, as finding new examples in real-world data can be challenging. 

4. Shifting Climates: Climate Change Communication from YouTube to TikTok, by A. Pera, L.M. Aiello (accepted at WebSci’24).

   

   How do video content creators tailor their communication strategies in the era of short-form content? We conducted a comparative study of the YouTube and TikTok video productions of 21 prominent climate communicators active on both platforms. We found that when using TikTok, creators use a more emotionally resonant, self-referential, and action-oriented language compared to YouTube. Also, the response of the public aligns more closely to the tone of the videos in TikTok.

5. The role of interface design on prompt-mediated creativity in Generative AI, by M. Torricelli, M. Martino, A. Baronchelli, L.M. Aiello (accepted at WebSci’24).
   We analyze 145k+ user prompts from two Generative AI platforms for image generation to see how people explore new concepts over time, and how their exploration might be influenced by different design choices in human-computer interfaces to Generative AI. We find that creativity in prompts declines when the interface provides generation shortcuts that deviate the user attention from prompting.

We have three brand new papers out, this time in PNAS, Scientific Reports, and the Journal of Quantitative Description:

1. Socioeconomic reorganization of communication and mobility networks in response to external shocks, by L. Napoli, V. Sekara, M. García-Herranz, and M. Karsai, published in PNAS
   
   We analyze mobile phone communication data to investigate the dynamics of network segregation patterns of the same set of people both in terms of mobility and of social communication during the initial wave of COVID-19 in Sierra Leone. Interestingly, we find opposite trends in the network segregation dynamics, characterized overall by simultaneous increase in mobility segregation and reduction in social network segregation. Our results underscore the significance of data-driven studies going beyond single-axis approaches to assess the impact of emergency policies.
2. Drivers of social influence in the Twitter migration to Mastodon, by L. La Cava, L.M. Aiello, and A. Tagarelli , published in Scientific Reports
   
   We analyzed the social network and the public conversations of about 75,000 users who migrated from Twitter to Mastodon, as we NERDS did too a year ago, and observed that the temporal trace of their migrations is compatible with a phenomenon of social influence, as described by a compartmental epidemic model of information diffusion. Drawing from prior research on behavioral change, we delved into the factors that account for variations of the effectiveness of the influence process across different Twitter communities.
   Read more in our blog post: https://communities.springernature.com/posts/get-out-of-the-nest-drivers-of-social-influence-in-the-twitter-migration-to-mastodon
3. Cracking Open the European Newsfeed, by L. Rossi, F. Giglioetto, and G. Marino, published in Journal of Quantitative Description: Digital Media
   
   This paper contributes to the ongoing effort to describe and quantify the quality of information that is shared on large social media platforms. We do this by complementing existing research that provided a first quantitative assessment of the quality of the information circulating on Facebook among US users. Leveraging an updated version of the same data source — Meta’s URL Shares Dataset — and replicating much of the methodology, we quantify the trustworthy and untrustworthy links to external websites that have been shared on Facebook in the period between 2019 and 2022 in three major European countries (Germany, France, and Italy). We observe a clear decline in the number of URLs present in the dataset and an increase in the URLs from untrustworthy domains as a percentage of the total URLs shared in a year. This increase seems to be higher in electoral years (in Germany and in Italy) but it does not translate into an increase of Views received from untrustworthy sources.
   

Sidewalk networks: Review and outlook, by D. Rhoads, C. Rames, A. Solé-Ribalta, M.C. González, M. Szell & J. Borge-Holthoefer, published in Computers, Environment and Urban Systems

From a transport perspective, increasing active travel –and walking in particular– is crucial for the future of sustainable cities, as reflected in global decarbonisation policies and agendas. Further, walking is much more than a mere mode of transport: it provides a fundamental social function, fostering vibrant cohesive communities. Arguably, walking and its associated infrastructure –sidewalks– should rank among the highest priorities for planning authorities. However, efficiency- and speed-driven urbanisation has gradually reallocated street space to private cars, leading to automobiles being the prioritised mode of transport today. Empirical research has generally followed suit, and a systemic understanding of walking as a phenomenon is largely missing, i.e., questions like how connected, resilient, accessible, or socially equitable is the pedestrian infrastructure of whole neighbourhoods and cities. Such relative neglect of sidewalk network research is, first and foremost, the consequence of a generalised lack of publicly available data on sidewalk infrastructure worldwide. A second reason might be its apparent lack of interest from a systemic standpoint: pedestrian mobility does not produce coordination challenges on the scale that cars do. In this work, we confront this perception by showing that there is ample research potential in the study of system-wide sidewalk networks, with both structural and dynamical challenges which might be critical to pursue the latest aspirations towards sustainable mobility in cities.

Future directions in human mobility science, by L. Pappalardo, E. Manley, V. Sekara, L. Alessandretti, published in Nature Computational Science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.