News / Highlights / Colloquium
- Published on 27 April 2018
In an article just published in EPJ Data Science, Valentin Kassarnig, Sune Lehmann and Andreas Bjerre-Nielsen look into smartphone data of undergraduate students to assess factors influencing social behavior and educational performance.
(Guest post by Valentin Kassarnig, Sune Lehmann & Andreas Bjerre-Nielsen, originally published on the SpringerOpen blog)
EPJ Data Science Highlight - Discovering temporal regularities in retail customers’ shopping behaviour
- Published on 18 April 2018
Why do we buy certain items when we buy them? A new study published in EPJ Data Science analyzes personal retail data to extract a temporal purchasing profile, which is able to summarize whether and when a customer makes a purchase. Its results show that certain patterns and types of shoppers are detectable, which can be used both by customers to enable personalized services, and by the retail market chain for providing offers and discounts tailored to the individual shoppers personal temporal profile.
(Guest post by Riccardo Guidotti and Anna Monreale, originally published on the SpringerOpen blog)
- Published on 10 April 2018
(This post was originally published on the SpringerOpen blog)
A team of researchers from Northeastern University, Boston, used a big data approach to investigate what makes a book successful. By evaluating data from the New York Times Bestseller Lists from 2008 to 2016, they developed a formula to predict if a book would be a bestseller.
- Published on 14 February 2018
Although urbanization has many advantages, one of its biggest drawbacks is the rise in socio-economic inequality. There have been some attempts at a qualitative analysis of the relationship between certain city features and social inequality, but these kinds of analyses are hard to replicate. A new research article published in EPJ Data Science proposes a new quantitative computer-based method for how to better understand the link between cites and social inequalities.
(Guest post by Alessandro Venerandi, originally published on the SpringerOpen blog)
- Published on 04 December 2017
Nowadays, platforms like Twitter play a big role in the aftermath of disasters, such as natural disasters, mass shootings, or terror attacks, as people try to receive the latest information on what happened through social media channels. A new study published in EPJ Data Science shows how an analysis of social media responses to disasters might help us better understand the dynamic of the public’s attention during these events, what such an analysis shows about people’s attention spans and focus points in the aftermath of disasters, and how analyses like these could be performed in a cost-effective way.
(Guest post by Yu-Ru Lin, originally published on SpringerOpen blog)
EPJ Data Science Highlight - Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs
- Published on 29 November 2017
Due to the emergence and continuously increasing usage of social media services all over the world, it is now possible to estimate in real-time how entire groups of people are feeling at a given point. However, in order to be able interpret the available data correctly, the right tools and methods need to be used. A new article EPJ Data Science examines a range of such methods and shows their ability but also their limitations.
(Guest post by Andrew Reagan, originally published on SpringerOpen blog
As a grad student trying to understand the emotional content of some unreadably large collection of texts, a typical night in grad school can often go something like this: You’re up late at night planning a new research study, thinking about trying some of this fancy sentiment-based text analysis. You resort to your favorite search engine with the query “sentiment analysis package python.” We have all been there, except maybe with R instead of Python (the latter being my favorite).
EPJ Data Science Highlight - Gaining historical and international relations insights from social media
- Published on 17 October 2017
As more and more people get their news from social media platforms, these become hosts to vast amounts of information on human behavior in relation to real-time events around the world. In a study published in EPJ Data Science, Vanessa Peña-Araya and team successfully match geopolitical interactions obtained from Twitter activity with real-world historical international relations.
(Guest post by Vanessa Peña-Araya, Mauricio Quezada, Denis Parra and Barbara Poblete, originally published on the SpringerOpen blog
Online social media platforms, like Twitter, Sina Weibo, or Facebook, have become very popular in recent years. They are primarily used to share personal experiences and to keep in touch with friends. Nevertheless, many users turn to these platforms as reliable sources to find real-time information about world events, such as the Ukrainian Crisis or recent natural disasters. In particular, Twitter has become one of the prefered sources on the Web for breaking news updates
- Published on 16 October 2017
In the summer of 2016 Pokémon Go took the world by storm. Millions of people across the globe descended on their streets, searching their neighbourhoods for monsters. Much has been reported on the health benefits that players gained from using the app; now, research published in EPJ Data Science explores how Pokémon Go was able to change the pulse of a city, encouraging people to use areas in ways they didn't previously.
(Guest post by Eduardo Graells-Garrido, originally published on SpringerOpen blog
The success of Pokémon Go is undeniable. People of all ages and everywhere in the world were using their mobile phones to go around their cities trying to catch the next pocket monster. But “PoGo” had an interesting, perhaps unintended, side-effect: not only did the game let you catch Pokémon in an augmented reality (AR) environment, it also motivated players to walk more and meet new people.
- Published on 31 August 2017
In EPJ Data Science, Alice Patania and colleagues evaluate the collaborative interactions between scientists from a new perspective.
The structure of scientific collaborations has been the object of intense study both for its importance for innovation and scientific advancement, and as a model system for social group coordination and formation thanks to the availability of authorship data.
Over the last few years, complex networks approaches to this problem have yielded important insights and shaped our understanding of scientific communities. In our recently published article in EPJ Data Science, we propose to complement the picture provided by network tools with that coming from topological data analysis, which has at its core the notion of multi-agent interactions.
- Published on 10 August 2017
In the aftermath of recent (and surprising) election results, it became evident that poll results do not tell the whole story about voters' intentions. In a study published in EPJ Data Science, researchers from the University of Leeds have mapped voter sentiment in all United Kingdom constituencies based on data from electronic petitions, achieving a good match with the results of the 2017 General Election.
(Guest post by Stephen Clark, Nik Lomax and Michelle A. Morris, originally published on SpringerOpen blog)
The EU referendum and 2017 General Election are two recent examples where polling companies failed to accurately predict the outcome of voter sentiment. Most predicted that the UK would vote to remain in the European Union and that the Conservative party would increase their parliamentary majority. When neither of these outcomes transpired there was much critique of the data sources and methods used to assess voter sentiment and opinion.