Aims and scope

The 21st century is witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.

EPJ Data Science offers a publication platform to showcase the latest contributions to the study of techno-socio-economic systems, wherein “digital traces” of human activity and their derivative models are used as first-order objects for the investigation. Specifically, the focus of the journal is on analyzing and synthesizing massive data sets and models learning from them to achieve new insights into societal phenomena and behavior. Application domains include, but are not limited to, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, generative language models, as well as general risk and crisis scenario forecasting up to and including policy advice. Methodologically, EPJ Data Science welcomes approaches from a broad range of disciplines, spanning statistically rigorous analysis of data, social network analysis, complex systems, applied machine learning, and more.

Papers submitted to this journal should not only strive to improve on existing data science methodologies and models but must provide new insight into human or social behavior or systems, in the areas outlined above. Submissions that focus on purely descriptive statistics or apply standard techniques to mainstream datasets or modelling approaches are unlikely to be considered for publication.

Thus, EPJ Data Science offers a publication platform to bring together diverse academic disciplines concerned with challenges around:

    How to extract signals about techno-socio-economic systems from large, complex data
    How to interpret these signals in the theoretical context of the relevant disciplines
    How to find new empirical laws, or fundamental theories, concerning how societies work
    How to study predictive and generative models trained on digital traces of human behavior, for example Large Language Models 

Special Issue on Navigating Information Integrity in the Age of Misinformation
Submission Date: 2026-02-28

This topical collection seeks to explore the pressing issue of misinformation and disinformation through the combined lenses of Data Science and Network Science. Misinformation is a complex problem emerging from the interactions among individuals, media platforms, and communication networks, often leading to widespread societal consequences. Both fields provide powerful frameworks for understanding these complexities — Data Science through the analysis of large datasets and pattern recognition, and Network Science through the examination of interconnected systems and emergent behaviors.

The aim of this collection is to understand how false information spreads across various platforms and networks—social, communication, and technological — and to evaluate the structural properties, dynamic behaviors, and data-driven patterns that facilitate this dissemination. Misinformation spreads not just because of individual actors but through intricate and interconnected systems that amplify and accelerate its reach, creating cascading effects that are difficult to mitigate.

By bringing together cutting-edge research from both Data Science and Network Science, this collection addresses the challenges of detecting, mitigating, and managing the effects of false information across connected systems. Understanding how data patterns and network structures influence the spread of misinformation can provide key insights into developing interventions and strategies for reducing its impact.

We welcome submissions that employ data-driven methodologies, network analysis, or a combination of both to tackle issues related to misinformation and disinformation. All papers will undergo the standard peer-review process as followed by the respective journals. Authors can submit their manuscripts to either EPJ Data Science or Applied Network Science depending on their affinity to data science or network science. The editors of the collection will assess each manuscript to ensure the most suitable publication venue.

Submission Information: Authors should select the appropriate Collection “Navigating Information Integrity in the Age of Misinformation” under the “Details” tab during the submission stage.

This collection supports United Nations Sustainable Development Goals 4: Quality Education; & United Nations Sustainable Development Goals 16: Peace, Justice and Strong Institutions