News / Highlights / Colloquium
EPJ Data Science Highlight - Social media trending: real or manufactured?
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- Published on 11 July 2017
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.
EPJ Data Science Highlight - Are your tweets feeling well? Opinion and emotion in tweets change when you get sick
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- Published on 02 July 2017
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.
EPJ Data Science Highlight - Are your friends happier than you?
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- Published on 06 June 2017
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.
EPJ Data Science Highlight - Using social media for large-scale studies of gender differences
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- Published on 02 June 2017
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.
EPJ Data Science - View featured video: How teams and players stack up and why
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- Published on 14 March 2017
“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.
EPJ Data Science Highlight - Predicting future sports rankings from evolving performance
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- Published on 16 December 2016
Competitive sport ranking evolution over time is used to predict the future evolution of rankings
Competitive sports and games are all about the performance of players and teams, which results in performance-based hierarchies. Because such performance is measurable and is the result of varied rules, sports and games are considered a suitable model to help understand unrelated social or economic systems characterised by similar rules-based complexity. Now, a team of Mexican scientists have used the performance of national teams in tennis, chess, golf, poker and football as a test-bed for identifying universal features in the creation of hierarchies—such as the stratified structure found in the global hierarchical distribution of wealth. José Morales from the National Autonomous University of Mexico and his colleagues found they could, in principle, predict changes in rank occupancy over the course of a contender's lifetime, regardless of the particularities of the sports or activity. These findings, published in EPJ Data Science, enhance our ability to forecast how stratification occurs in competitive activities.
EPJ Data Science Highlight - Fiction-book narratives: only six emotional storylines
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- Published on 18 November 2016
How scientists are using big data analysis to deconstruct the art of storytelling
Our most beloved works of fiction hide well-trodden narratives. And most fictions is based on far fewer storylines than you might have imagined. To come to this conclusion, big data scientists have worked with colleagues from natural language processing to analyse the narrative in more than a thousand works of fiction. By deconstructing some of the magic of narrative in fiction books, they have also confirmed that there are six different, common ways of telling a story that can be found time and time again in popular stories. They were inspired by the work of US fiction author Kurt Vonnegut, who originally proposed the similarity of emotional story lines in a Masters’s thesis rejected by the University of Chicago. These findings have just been published in EPJ Data Science by Andrew Reagan from the University of Vermont, USA, and colleagues.
EPJ Data Science Highlight - Mining digital crumbs helps predict crowds’ mobility
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- Published on 02 November 2016
Analysing the traces of human behaviour from geolocalisation data gives clues for more accurate urban planning
Getting urban planning right is no mean feat. It requires understanding how and when people travel between different places. This knowledge, in turn, helps in dimensioning roads and motorways and in scaling the capacity of utilities, such as power grids or mobile phone towers. Now, physicists at the Institute for Scientific Interchange Foundation in Turin, Italy, have exploited the geolocalisation data from millions of users of the photo sharing site Flickr to show how it is possible to predict crowd movements. Mariano Beiró and colleagues have combined this data with existing theoretical models explaining the movement of people. In a study published in EPJ Data Science, they show that their approach can help improve predictions concerning the nature of travel of large crowds of people between two places.
EPJ Data Science Highlight - Behavioural studies from mobile crowd-sensing
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- Published on 18 December 2015
Smart phone monitoring has become a boon for scientists studying human behaviour and factors influencing stress
Using mobile phones for research is not new. However, interpreting the data collected from volunteers’ own smart phones--which has the potential to emulate randomised trials--can advance research into human behaviour. In a new study published in EPJ Data Science, scientists have just demonstrated the potential of using smart phones for conducting large-scale behavioural studies.The results stem from the work of Fani Tsapeli from the University of Birmingham, UK, and her colleague and Mirco Musolesi from University College London, UK. In their study, they evaluate the cause of increased stress levels of participants using user-generated data, harvested from their phones.
EPJ Data Science Highlight - Using patients’ trail of digital crumbs for public health surveillance
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- Published on 20 October 2015
© bakhtiarzein / Fotolia
Public health agencies could capitalise on streams of data related to patients on the internet but only once interpretation methods have been validated.
Data is ubiquitous. In the area of heath, there are growing data streams directly initiated by patients through their activities on the internet and on social networks and other related ones such as electronic medical records and pharmacy sales data. These so-called Novel Data Streams (NDS) are very appealing to public health surveillance officials due to their ease of collection. A new paper published in EPJ Data Science evaluates the currently available NDS surveillance papers before outlining a conceptual framework for integrating such data into current public health surveillance systems. The authors, who hail from public health agencies, academia, and the private sector, highlight the need for future rigorous evaluation and validation of standards before NDS can effectively reinforce existing public health surveillance systems.
