2017 Impact factor 1.947
Special Topics

EPJ Data Science Highlight - Offline biases in online platforms

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

Copyright: Pixabay License

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

Photo by Con Karampelas on Unsplash

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)


Managing Editors
Anne Ruimy (EDP Sciences) and Sabine Lehr (Springer-Verlag)
The collaboration for this special issue has been a pleasent experience.

Yong Zhou, Xiangtan University, China,
Editor EPJ Special Topics 222/8, 2013

ISSN: 1951-6355 (Print Edition)
ISSN: 1951-6401 (Electronic Edition)

© EDP Sciences and Springer-Verlag