2016 Impact factor 2.787

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EPJ Data Science Highlight - A fresh look into the dynamics of scientific collaborations

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.

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EPJ Data Science Highlight - The power of novel data to understand political sentiment

©Wikimedia commons

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.

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EPJ Data Science Highlight - Instagram photos reveal predictive markers of depression

Right photograph has higher Hue (bluer), lower Saturation (grayer), and lower Brightness (darker) than left photograph. Instagram photos posted by depressed individuals had HSV values shifted towards those in the right photograph, compared with photos posted by healthy individuals.

Research published in EPJ Data Science finds that early-warning signs of depression can be detected in Instagram posts before a clinical diagnosis is made. Here to tell us how the image filter, colour and the number of faces in the post can all be predictors are authors of the study, Andrew G. Reece and Christopher M. Danforth.

Guest post by Andrew G. Reece and Christopher M. Danforth, originally published on SpringerOpen blog

When you’re feeling sad, the people around you probably know it. Moody playlists, slumped shoulders, drawn-out sighs – there are many ways we signal to the rest of the world when we’re having a down day. It’s not all that much of a stretch, then, to imagine your Instagram posts might look happier when you’re feeling happy, and sadder when you’re feeling sad.

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EPJ Data Science Video – A new method for giving voting advice: How researchers can turn voter “Hmm’s” into HMMs

Indecision is quickly becoming a thing of the past. Whether it’s content, cuisine, or companionship we crave, technology seems to know just what to serve up. But what about life’s bigger decisions? The ones that probably should give us pause? A recent study suggests that there might soon be an app for those too, namely for voting.

Applying Hidden Markov Models to Voting Advice Applications, Marilena Agathokleous and Nicolas Tsapatsoulis (2016), EPJ Data Science, 5:34, DOI: 10.1140/epjds/s13688-016-0095-z

EPJ Data Science Highlight - Estimating unemployment rates from Twitter user routines

Pixabay, CC0 Public Domain.
Pixabay, CC0 Public Domain

The buzz of busy commuters, as well as the lack of it, leave behind digital footprints that are rich in information about all aspects of people's lives. In EPJ Data Science, Eszter Bokányi and team analyze 63 million tweets originating all over the US for a period of 10 months, and find links between unemployment rates and and the users' Twitter activity.

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EPJ Data Science Highlight - Social media trending: real or manufactured?

Pixabay, CC0 Public Domain.
Pixabay, CC0 Public Domain

The era of "fake news" is upon us. Navigating social media is a constant exercise of judgement, but data science can be a helpful to distinguish real from fabricated trending topics. In EPJ Data Science, Emilio Ferrara and team set out to determine from very early on whether information is being organically or artificially disseminated on social media.

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EPJ Data Science Highlight - Are your tweets feeling well? Opinion and emotion in tweets change when you get sick

©Max Pixel (edited)
©Max Pixel (edited)

Can we tell if a person is physically ill by the way they tweet? On a recently published article in the journal EPJ Data Science, researchers at the Pacific Northwest National Laboratory uncover links between the health of users and the emotional tone of their social media output.

Guest post by by Svitlana Volkova, originally published on SpringerOpen blog

Any doctor or nurse knows good public health begins with prevention. Whether it’s a severe strain of the flu or mental illness, identifying the need for help early can save lives. Social media could be the game-changing solution public health workers have been looking for. Whereas traditional data from clinics may take weeks to collect, social media streams in real time. In other words, public health workers could monitor social media like a heartbeat, and take action before people visit a doctor.

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EPJ Data Science Highlight - Are your friends happier than you?

Photo from Pixabay, CC0 public domain.
Photo from Pixabay, CC0 public domain.

In an era of fleeting but constant contact with extended online communities, it is common to find yourself wondering: are your friends happier/more popular than you? To put these feelings to the test, scientists have sifted through the timelines of thousands of Twitter users, to understand the ways in which social networks affect how we feel and relate to one another.

Guest post by Johan Bollen

Social media platforms have garnered billions of users, possibly because they satisfy a strong human need for feeling connected. However, do they actually contribute to our social happiness?

In EPJ Data Science we attempt to shed some light on this issue from the perspective of network science.

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EPJ Data Science Highlight - Using social media for large-scale studies of gender differences

Photo from Pixabay, CC0 public domain.
Photo from Pixabay, CC0 public domain.

Social networks capture data about most aspects of the daily lives of millions of people around the world. The analysis of this rich and ready-available source of information can help us better understand the complex dynamics of society.

In a recent article published in EPJ Data Science the authors propose the use of location-based social networks to study the activity patterns of different gender groups, which they summarise in a guest post on the SpringerOpen blog.

Gender differences have a subjective nature and may vary greatly across cultures, making them challenging to explain. Indeed, over the past decades, this topic has received a lot of attention by researchers, but there is still a long way to reach a consensus on the subject.

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EPJ Data Science - View featured video: How teams and players stack up and why

“Winning is not a sometime thing; it’s an all the time thing. Winning is a habit,” said legendary American football coach Vince Lombardi.

Human sports and games, with their rules of competition and measures of performance, serve as an ideal test-bed to look for universal features of hierarchy formation. In a recent article published in EPJ Data Science, José A. Morales and colleagues study the behaviour of performance rankings over time of players and teams for several sports and games, and find statistical regularities in the dynamics of ranks. This finding dispels the commonly held notion that rank changes are due to the intrinsic strengths or qualities of teams and players. The same phenomenon may apply to more complex competition settings with further examinations.

Read more in the highlight of this article.

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Frank Schweitzer and Alessandro Vespignani

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