News / Highlights / Colloquium
EPJ Data Science appoints Dr Yelena Mejova as co-Editor-in-Chief
- Published on 09 January 2023
EPJ is pleased to announce that Dr Yelena Mejova has been appointed as a co-Editor-in-Chief for EPJ Data Science, effective January 2023. She will be responsible for overseeing the editorial process of the journal, working closely with Dr Ingmar Weber, who continues to serve as co-Editor-in-Chief.
Yelena Mejova is a Senior Research Scientist at the ISI Foundation in Turin, Italy. Specializing in social media analysis and mining, her work concerns the quantification of health and wellbeing signals in social media, as well as tracking of social phenomena, including politics and news consumption. In 2022, she co-chaired International AAAI Conference on Web and Social Media (ICWSM) and the Web & Society track at The Web Conference. As a part of the CRT Foundation's Lagrange Project for Data Science and Social Impact, she is also working with the humanitarian sector including the World Food Program, OCHA, and IMMAP to develop NLP and modeling tools to aid in humanitarian data management and forecasting.
EPJ Data Science Highlight - A data-driven approach for assessing biking safety in cities
- Published on 25 March 2021
The bicycle is arguably the most sustainable and eco-friendly mode of transport but biking safety remains a prime concern, especially in cities. In their work recently published in EPJ Data Science Konstantinos Pelechrinis and his co-authors propose a model which provides interpretable findings for practical change.
Continue reading the blog post here.
EPJ Data Science appoints Dr. Ingmar Weber as co-Editor-in-Chief
- Published on 07 January 2021
EPJ is pleased to announce that Dr Ingmar Weber of Qatar Computing Research Institute (QCRI) has been appointed as a co-Editor-in-Chief for EPJ Data Science, effective January 2021. He will be responsible for overseeing the editorial process of the journal, working closely with Prof Markus Strohmaier, who continues to serve as co-Editor-in-Chief. Ingmar Weber is the Research Director for Social Computing at QCRI where his research focuses on using social media and other non-traditional data to study phenomena such as international migration, digital gender gaps, and wealth inequalities. Of particular interest is how such data can be used to provide better statistics on global development and how, in turn, such statistics can be used for better interventions and policy decisions. This work is done in close collaboration with different United Nations entities and NGOs. Ingmar Weber currently serves as an ACM Distinguished Speaker and is a Senior Member of AAAI, IEEE, and ACM. He has been a member of the Editorial Board for EPJ Data Science since 2018.
EPJ Data Science Highlight - Women’s disadvantage: because of who they are, or what they do?
- Published on 02 August 2019
Women often find themselves strongly disadvantaged in the field of software development, in particular when it comes to open source. In a study recently published in EPJ Data Science, Orsolya Vasarhelyi and Balazs argue that this disadvantage stems from gendered behavior rather than categorical discrimination: women are at a disadvantage because of what they do, rather than because of who they are.
Continue reading the guest post by Orsolya Vasarhelyi and Balazs Vedres on the SpringerOpen blog.
EPJ Data Science Highlight - What ‘Twitch Plays Pokémon’ tells us about crowd behavior
- Published on 05 July 2019
No one would deny that the behavior of the people we know, and even of our own, can radically change depending on those who surround us. The problem of understanding how being in a group changes the way we behave has been subject of intense research in psychology since the beginning of the past century. The beginning of the XXI century gave rise to a new kind of group: the online crowds. Nowadays, it is no longer necessary to have all individuals in the same place in order to have a ‘crowd’. What is more, it is possible to connect together thousands, even millions, of individuals in a matter of minutes.
In the work recently published in EPJ Data Science, we study one such occasion that gathered millions of users: Twitch Plays Pokémon.
Continue reading the guest post by Alberto Aleta on the SpringerOpen blog.
EPJ Data Science Highlight - What can we learn from billions of food purchases derived from fidelity cards?
- Published on 23 May 2019
For your health, what you eat is more important than what you earn.
This result comes from our latest project “Poor but Healthy”, which was published in EPJ Data Science, and comes with a @tobi_vierzwo’s stunningly “beautiful map of London” that author Daniele Quercia invites everyone to explore.
By combining 1.6B food item purchases with 1.1B medical prescriptions for the entire city of London for one year, researchers discovered that, to predict health outcomes, socio-economic conditions matter less than what previous research has shown: despite being of lower-income, certain areas are healthy, and that is because of what their residents eat.
Read the full blog post on Medium.
EPJ Data Science Highlight - How news outlets target audiences
- Published on 22 May 2019
The mass media is one of the social forces with the most active transformative power. However, news reach people unequally. Many factors shape the distribution and influence of news media coverage. Some of these factors are the geographic reach of newspapers (national versus regional newspapers), the direct targeting of specific sectors of the population, and/or the political ideology of the media outlet itself.
In a recent article in EPJ Data Science, Erick Elejalde, from the L3S Research Centre in Hannover, Germany, explains how their work helps to identify whether or not an outlet’s coverage deviates from the purely geographic influence to a more sophisticated behavior involving the weight of political and socioeconomic interests.
Read the post on the SpringerOpen blog.
EPJ Data Science Highlight - Offline biases in online platforms
- Published on 09 April 2019
Online booking platforms such as Airbnb or Uber present themselves as and strive to be inclusive, but there is an increasing amount of both anecdotal and scientific evidence of discriminatory behavior among their users. In a study published in EPJ Data Science, researchers at University College London set out to evaluate interaction patterns within Airbnb, in an effort to understand the extent to which offline human biases influence affects their users.
Read the guest post by Giacomo Livan, Licia Capra, Weihua Li and Victoria Koh on the SpringerOpen blog
EPJ Data Science Highlight - Using deep learning to “see” inside homes across the world
- Published on 18 February 2019
How much does someone's living room tell about how they live? Peeking into another person's life might be just part of natural human curiosity, but the answer to this question may provide insights in a wide range of aspects of human behavior. A new study published in EPJ Data Science uses the power of machine learning to explore patterns of home decors—and what they could tell about their owners—in popular accommodation website Airbnb.
See guest post by Clio Andris and Xi Liu originally published in the SpringerOpen blog
EPJ Data Science Highlight - Twitter’s tampered samples: Limitations of big data sampling in social media
- Published on 16 January 2019
Social networks are widely used as sources of data in computational social science studies, and so it is of particular importance to determine whether these datasets are bias-free. In EPJ Data Science, Jürgen Pfeffer, Katja Mayer and Fred Morstatter demonstrate how Twitter’s sampling mechanism is prone to manipulation that could influence how researchers, journalists, marketeers and policy analysts interpret their data.
(Guest post by Jürgen Pfeffer, Katja Mayer and Fred Morstatter, originally published in the SpringerOpen blog)