Networks, Data, and Society (NERDS)

Talk: Luca Maria Aiello at ANET Lab Seminar Series
2022-04-01 10:00 - 11:00 (Europe/Copenhagen)
Abstract
Social relationships are the key determinant of crucial societal outcomes, including diffusion of innovation, productivity, happiness, and life expectancy. To better attain such outcomes at scale, it is therefore paramount to have technologies that can effectively capture the type of social relationships from digital data. NLP researchers have tried to do so from conversational text but mostly focusing on sentiment or topic mining, techniques that fall short on either conciseness or exhaustiveness. We propose a theoretical model of 10 dimensions (colors) of social relationships that is backed by decades of research in social sciences and that captures most of the common relationship types. We trained a deep-learning model to classify text along these ten dimensions, and we reached performance up to 0.98 AUC. By applying this tool on large-scale conversational data, we show that the combination of the predicted dimensions suggests both the types of relationships people entertain and the types of real-world communities they shape.
Bio
Luca Aiello is an Associate Professor at the IT University of Copenhagen. Previously, he worked for 10 years as a Research Scientist in the industry: at Yahoo Labs in Barcelona, and at Bell Labs in Cambridge (UK). He conducts research in Computational Social Science, a field of studies that uses Social Science theories to inform solutions to Data Science problems. His work focuses on the analysis of conversations and collective behavior through the lens of machine-learning algorithms applied to large scale data. He was recently awarded the Carlsberg Young Investigator Fellowship to study how to build technologies to support collective cooperation in the face of societal dilemmas.
Sign up at: http://anet.krtk.mta.hu/seminars/

Invited Talk Anastassia Vybornova at Citizens Collective (CUSP, TU Delft)
2022-05-18 16:00 - 17:00 (Europe/Copenhagen)
Will Cycling Save Our Cities? A Story of Love and Panic

The climate crisis is putting our future in danger. Cycling is often described as the go-to “green bullet” to make transportation in cities more sustainable and to reduce our exacerbated fossil fuel consumption. But can we still win the global war on cars, or are our beloved cities (and with them, their inhabitants) doomed anyways? I will share a personal account of my journey into cycling research, which is a story of reciprocated love and well-founded panic. I invite you to join me on a bumpy ride to rethink urban mobility: Let’s pet the elephants in the room – and share our visions of being on the road.

Keynote talk Michael Szell at Alan Turing Institute
2022-06-15 11:20 - 12:05 (Europe/Copenhagen)
Establishing a quantitative science of bicycle networks
Making transportation in cities sustainable is one major key to solving the climate, urban livability, and urban transport crises. To this end, urban data science offers new tools to quantify societal problems in cities and to guide policy makers with human-centric solutions. In this talk I outline our recent and ongoing efforts towards that end, focusing on vulnerable road users and active travel, in particular towards establishing a data-driven science of bicycle networks. I first discuss inequalities between transport modes through mobility space and collision threat analysis, then present new computational methods based on network science to grow and plan bicycle networks optimally and equitably for various urban development stages. All research comes with open code and data, and with concrete policy recommendations towards better urban livability, public health, and decarbonization.

Luca Aiello lecturing at Summer School Ghent Summer of DANS 2022
2022-09-19 | Ghent University, Tweekerkenstraat 2, Ghent, Belgium
Introduction to Data Science

Invited talk Ane, Anastassia, Michael at University of Liverpool, Geographic Data Science Lab
2022-09-22 16:00 - 17:00 (Europe/Copenhagen)
Note 3PM BST is 4PM Copenhagen time.

Ane Rahbek Vierø at CRBAM 2022
2022-10-06 10:30 - 12:00 (Europe/Copenhagen) | University of Amsterdam, Oudezijds Achterburgwal 237, Amsterdam, Netherlands
Reproducible Quality Assessment of OSM Data for Cycling
Applications

Anastassia Vybornova at CRBAM 2022
2022-10-06 13:00 - 14:30 (Europe/Copenhagen) | University of Amsterdam, Oudezijds Achterburgwal 237, Amsterdam, Netherlands
The Cycle Node Network Planner: A data-driven decision support tool

Keynote by Luca Aiello at International Conference on Discovery Science 2022
2022-10-11 09:00 - 10:00 (Europe/Copenhagen) | Campus St Priest, 161 Rue Ada, 34095, Montpellier, France
Coloring Social Relationships
Social relationships are the key determinant of crucial societal outcomes, including diffusion of innovation, productivity, happiness, and life expectancy. To better attain such outcomes at scale, it is therefore paramount to have technologies that can effectively capture the type of social relationships from digital data. NLP researchers have tried to do so from conversational text but mostly focusing on sentiment or topic mining, techniques that fall short on either conciseness or exhaustiveness. We propose a theoretical model of 10 dimensions (colors) of social relationships that is backed by decades of research in social sciences and that captures most of the common relationship types. We trained a deep-learning model to classify text along these ten dimensions, and we reached performance up to 0.98 AUC. By applying this tool on large-scale conversational data, we show that the combination of the predicted dimensions suggests both the types of relationships people entertain and the types of real-world communities they shape.
We believe that the ability of capturing interpretable social dimensions from language using AI will help closing the gap between the oversimplified social constructs that existing social network analysis methods can measure and the multifaceted understanding of social dynamics that has been developed by decades of theoretical research.

Roberta Sinatra at Women in Network Science Seminar
2022-10-18 17:00 - 18:30 (Europe/Copenhagen)
Pathways in Network Science – A seminar with Roberta Sinatra

Michele Coscia at DINS Seminar Series
2022-10-19 17:00 - 18:00 (Europe/Copenhagen)
Quantifying Political Polarization on a Network Using Generalized Euclidean Distance

Roberta Sinatra at Danish Data Science Academy / Pioneer Centre for AI
2022-10-22 10:00 - 11:00 (Europe/Copenhagen) | Frederiksdal Sinatur Hotel, Frederiksdalsvej 360, Frederiksdal, Denmark
Keynote to: Science of Science for Data Scientists

Luca Rossi chairing and moderating NLP, Fake News and Social Media @ Digital Tech Summit
2022-10-25 14:30 - 15:15 (Europe/Copenhagen) | Bella Center, Center Blvd. 5, København, 2300, Denmark
Program:
Welcoming talk: The many faces of Fake News, by session chair, Luca Rossi
NLP, Fake News and Social Media
Scientific Talk: NLP and social media, Marina Charquero Ballestar, Postdoc, AU
Scientific Talk: Editorial media and social media platforms: it’s complicated, Aske Kammer, RUC
Business Talk: Daniel Hanse, tjekdet.dk
Business Talk: Complexities of tacking the challenge of misinformation at scale: tools and possibilities for researchers, Nicole Lyon, Meta
Debate:
Will be announced
Chair: Luca Rossi, Associate Professor, ITU
Moderator: Luca Rossi, Associate Professor, ITU
Session coordinator: Nikolaj Oppermann, Network, Innovation & Collaboration manager, ITU

Roberta Sinatra at Aarhus University, The Science Studies Colloquium
2022-11-02 16:00 - 17:00 (Europe/Copenhagen)
Quantifying biases and inequalities in science
Every day our life is made easier by efficient algorithms that search and rank scientific information. Yet, these algorithms have an issue: they are trained on citation data that is ingrained with human biases. Therefore the output is inherently biased too, creating inequalities, raising concerns of discrimination, and harming economic growth. In this talk, I focus on recent quantitative efforts in science studies to (1) uncover bias mechanisms in science, (2) use this knowledge about biases to uncover inequalities in the scientific enterprise, and (3) create fair metrics and algorithms.

Michele Coscia holds a tutorial at Complex Networks
2022-11-07 13:30 - 16:30 (Europe/Copenhagen) | University of Palermo, Viale delle Scienze Building 19, Palermo, Italy
Node Vector Distances: Methods and Applications
Michele is an associate professor at IT University of Copenhagen since 2020. He mainly works on algorithms for the analysis of complex networks, with methods spanning from Computer Science to physics, and on applying the extracted knowledge to a variety of problems, including economic development, human mobility, and the analysis of ideological polarization. His background is in Digital Humanities, connecting new technologies with a classical education. He has a PhD in CS, obtained in 2012 at the University of Pisa. In the past, he visited Barabasi’s CCNR at Northeastern University, and worked with Ricardo Hausmann for 6 years at the Center for International Development, Harvard University.
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How quickly is a disease spreading through a social network? How diversified is a country in a network of related exported products? How polarized is the online discourse on social media? These seemingly unrelated questions can be answered with a common toolbox: they all require to compare different types of node attributes. In this workshop, you will learn how to apply Node Vector Distance techniques: given two node attributes — represented as vectors — how far the two are on a network structure? Are they correlated when we take into account not only the value of a node itself, but also the values of its neighbors? Or simply: how spread out in the network is an attribute? The workshop will make you familiar with ways to translate concepts such as Euclidean distance, Pearson correlation, and variance, when the vectors do not live in an Euclidean space, but on the complex landscape that is the topology of a network.

Marilena Hohmann at Complex Networks 2022
2022-11-08 11:45 - 12:00 (Europe/Copenhagen) | University of Palermo, Viale delle Scienze Building 19, Palermo, Italy
Estimating Affective Polarization on a Social Network
with Michele Coscia

Roberta Sinatra at ANETLab Seminar
2022-11-25 08:00 - 17:00 (Europe/Copenhagen)
Details TBA

Roberta Sinatra at Northwestern University Panel on “Inequalities in Scientific Production”
2022-12-08 19:30 - 21:00 (Europe/Copenhagen)

Michael and Ane at CASA UCL Seminar
2022-12-14 16:00 - 17:00 (Europe/Copenhagen)
Bicycle networks: From data quality assessment to computational analysis
Cycling is marginalized despite being a promising solution towards sustainable transport. In parallel, cycling data and cycling research are also marginalized, posing many opportunities for improvement and open questions. Here, we first present our cycling data quality tool which enables intrinsic and extrinsic assessment and reporting of data quality in cycling infrastructure data sets, focusing on the structural network perspective. Then, we explore how such data enable the development of computational methods for automated bicycle network analysis and planning.

Michael at CSH, Vienna
2023-04-04 15:00 - 16:00 (Europe/Copenhagen) | csh, Josefstaedter Strasse 39, Vienna, 1800, Austria
Bicycle infrastructure data and networks: Opportunities and limitations
In this talk, we will explore the opportunities and limitations of bicycle infrastructure data, models and networks. With the increasing popularity of cycling as a mode of transportation, the need for accurate and reliable data on bicycle infrastructure has become crucial for planners, engineers, and policymakers.

CSH Vienna workshop: Sustainable mobility
2023-04-14 09:00 - 12:15 (Europe/Copenhagen)
We are organizing a workhop in Vienna!
Sustainable Mobility: Data, Networks, and Complexity

Why are politicians so reluctant to invest into bicycle infrastructure and pedestrianization given it is the most efficient investment towards sustainable and livable cities? What are the technical and political bottlenecks that keep society in the stranglehold of car dependency, and what are the implications of sustained delay? Which data are we missing, how should we grow network infrastructure, and how to speed up the sluggish political process? The climate crisis demands pressing answers, which we explore here with state-of-the-art insights in Data/Network/Complexity Science and Urban Planning.
This workshop brings together researchers and policymakers from Vienna and Copenhagen who work on understanding the best pathways towards sustainable mobility with focus on cycling, or who are developing tools/methods supporting that aim. Our speakers will cover: Mobility and infrastructure data quality, bicycle network planning, politics & activism, systemic complexity approaches.

Michael at Eurosolar Austria, Vienna
2023-04-20 18:30 - 20:00 (Europe/Copenhagen) | Plutzerbrau, Schrankgasse 2, Vienna, 1070, Austria
Wie man optimale Radweg-Netze plant
Eurosolar Austria

Ane at Velo-City 2023
2023-05-09 11:00 - 12:00 (Europe/Copenhagen)
Examining Bicycle Network Data: A reproducible workflow for evaluating data quality
Session: The potential of open data for better infrastructure

Roberta at Nordic Science of Science workshop, Odense
2023-05-30 10:00 - 11:00 (Europe/Copenhagen)
Academic mentees succeed in big groups, but thrive in small groups

Luca Aiello at FRCCS 2023
2023-06-01 15:45 - 16:30 (Europe/Copenhagen)
Coloring Social Relationships
Social relationships are the key determinant of crucial societal outcomes, including diffusion of innovation, productivity, happiness, and life expectancy. To better attain such outcomes at scale, it is therefore paramount to have technologies that can effectively capture the type of social relationships from digital data. NLP researchers have tried to do so from conversational text but mostly focusing on sentiment or topic mining, techniques that fall short on either conciseness or exhaustiveness. We propose a theoretical model of 10 dimensions (colors) of social relationships that is backed by decades of research in social sciences and that captures most of the common relationship types. We trained a deep-learning model to accurately classify text along these ten dimensions. By applying this tool on large-scale conversational data, we show that the combination of the predicted dimensions suggests both the types of relationships people entertain and the types of real-world communities they shape. We believe that the ability of capturing interpretable social dimensions from language using AI will help closing the gap between the oversimplified social constructs that existing social network analysis methods can measure and the multifaceted understanding of social dynamics that has been developed by decades of theoretical research.

Roberta at Reykjavik University
2023-06-12 12:00 - 13:00 (Europe/Copenhagen)
Quantitative understanding of success and inequality through network science
The unprecedented availability of large-scale datasets on human activities and interactions has enabled us to quantitatively understand how networks shape success and drive inequalities in various fields. In this talk, I will highlight the crucial role of network science in comprehending social phenomena, particularly in the realms of art and science. Specifically, I will present a series of findings on the evolution of careers in the arts, utilizing a random walk model that successfully predicts artists’ success even twenty years into the future. I will then shift my focus to the COVID-19 pandemic and the advent of Large Language models, discussing how they have amplified network inequalities in science. Through these examples, I will demonstrate the importance of network science in measuring, predicting, and designing algorithms for social phenomena.

Michael at Reykjavik University
2023-06-13 12:00 - 13:00 (Europe/Copenhagen)
Computational, evidence-based approaches to bicycle network planning

In this talk, I explore the opportunities and limitations of network and data science for bicycle infrastructure planning. With the increasing popularity of cycling as a mode of transportation, and the necessity to invest into sustainable transport, more accurate and reliable data and computational methods on bicycle infrastructure analysis are becoming a crucial support for planners, engineers, and policymakers. In particular, I will introduce our new bicycle infrastructure data and network quality assessment tool BikeDNA, and discuss various graph-based algorithms for assisting in bicycle network analysis and planning.

Roberta at ICSSI
2023-06-26 13:00 - 14:00 (America/Chicago)
Exploring Gender Bias and Collaborative Dynamics in Science: Lessons from natural and controlled experiments

Anastassia at FOSS4G
2023-06-28 16:00 - 16:30 (Europe/Copenhagen)
https://talks.osgeo.org/foss4g-2023/talk/RPVTPU/
BikeDNA: A tool for Bicycle Infrastructure Data & Network Assessment
2023-06-28, 16:00–16:30 (Europe/Tirane), UBT C / N110 – Second Floor

Access to high-quality data on existing bicycle infrastructure is a requirement for evidence-based bicycle network planning, which can support a green transition of human mobility. However, this requirement is rarely met: Data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality. Currently available tools for road network data quality assessment often fail to account for network topology, spatial heterogeneity, and bicycle-specific data characteristics.
To fill these gaps, we introduce BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell, thus exposing spatial variation in data quality with a focus on network structure and connectivity. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results.
BikeDNA is based on open-source python libraries and Jupyter notebooks, requires minimal programming knowledge, and supports data quality assessments for a wide range of applications – from urban planning to OpenStreetMap data improvement or transportation network research. In this talk we will introduce how to use BikeDNA to evaluate and improve local data sets on bicycle infrastructure, examine what BikeDNA can teach us on the current state of data for active mobility, and discuss the importance of local quality assessments to support increased uptake of open and crowd-sourced data.

Roberta at ISSI 2023
2023-07-03 09:30 - 11:00 (America/New_York)
Quantifying biases and inequalities in science
Every day our life is made easier by efficient measures and algorithms that quantify, search, and rank scientific information. Yet, these measures and algorithms have an issue: they are trained on citations, which are ingrained with human biases. Therefore the output is inherently biased too, creating inequalities, raising concerns of discrimination, even harming growth. In this talk, I focus on recent quantitative efforts in science studies to (1) uncover bias mechanisms in science, (2) use this knowledge about biases to uncover inequalities in the scientific enterprise, and (3) create fair metrics and algorithms.
Roberta Sinatra is Professor in Computational Social Science at the University of Copenhagen (KU), and holds visiting positions at IT University of Copenhagen (ITU), ISI Foundation (Turin, Italy) and Complexity Science Hub (Vienna, Austria). She co-founded the NEtwoRks, Data, and Society (NERDS) Research group at ITU, which she led in 2019-2022, and is a co-lead of the pioneer centre for AI in Copenhagen. Her research is at the forefront of network science, data science, and computational social science. Roberta did her BSc, MSc and PhD in Physics at the University of Catania, Italy. Her research has been published in top-tier venues like Nature and Science, and has been featured in The New York Times, Forbes, The Economist, The Guardian, The Washington Post, among other major media outlets. Her research has been awarded the Complex Systems Society Junior prize, the DPG Young Scientist Award for Socio- and Econophysics, and a Villum Young Investigator grant.

Anastassia at NetSci 2023
2023-07-12 12:15 - 12:30 (Europe/Copenhagen)
https://easychair.org/smart-program/NetSci2023/2023-07-12.html#talk:224079
Luca Maria Aiello, Sándor Juhász, Anastassia Vybornova, Eszter Bokányi and Michael Szell
Highways are barriers to urban social connections
PRESENTER: Anastassia Vybornova

ABSTRACT. Geographic distances influence social connections inside cities – even in our digital age [1,2]. Both the perception of physical distance and the likelihood of forming social ties across locations are influenced by infrastructural elements, as previous studies showed: public transport increases social connectivity along routes [3]; barriers to physical mobility influence the creation of social connections [4]. To directly investigate the impact of highways on social connections, we analyse how the spatial configurations of a city’s social and infrastructural networks are correlated. We use a highly granular, georeferenced social network of mutual follower relationships between Twitter users in the top 50 US metropolitan areas [5]. For each of these cities, we create a gravity-law inspired configurational null model [6] that reflects the population density and distance distribution of users’ home locations. We then overlay the spatial network of social connections with the network of highways from OpenStreetMap, and measure the average number of highways crossed by social network ties. We find that the probability of an edge crossing at least one highway is significantly lower for real social connections than for the null model, and validate these findings by several multivariate regression models. Our results confirm that urban highways, apart from causing spatial segregation patterns, also have a directly measurable negative correlation with the density of social connections, and unveil the importance of infrastructure to the study of increasing inequalities and social network fragmentation in urban areas.
