"If you want to know, down to the individual level, how many people are sick in a population, you would have to survey the population, which is costly and time-consuming," said Adam Sadilek, postdoctoral researcher at the University of Rochester. "Twitter and the technology we have developed allow us to do this passively, quickly and inexpensively; we can listen in to what people are saying and mine this data to make predictions."
Sadilek also explained that many tweets are geo-tagged, which means they carry GPS information that shows exactly where the user was when he or she tweeted.
Collating all this information allows the researchers to map out, in space and in time, what people said in their tweets, but also where they were and when they were there. By following thousands of users as they tweet and go about their lives, researchers also could estimate interactions between two users and between users and their environment.
In a paper to be presented on Feb. 8 at the International Conference on Web Searching and Data Mining in Rome, Italy, Sadilek will show how their new model accounts for many of the factors that affect health and how it can complement traditional studies in life sciences. Using tweets collected in New York City over a period of a month, they looked at factors like how often a person takes the subway, goes to the gym or a particular restaurant, proximity to a pollution source and their online social status. They looked at 70 factors in total. They then looked at whether these had a positive, negative or neutral impact on the users' health.
Some of their results are perhaps not surprising; for example, pollution sources seem to have a negative effect on health. However, this is the first time this impact has been extracted from the online behavior of a large online population. The paper also reveals a broader pattern, where virtually any activity that involves human contact leads to significantly increased health risks. For example, even people who regularly go to the gym get sick marginally more often than less active individuals. However, people who merely talk about going to the gym, but actually never go (verified based on their GPS), get sick significantly more often. This shows that there are interesting confounding factors that can now be studied at scale.
The technology that Sadilek and his colleague Professor Henry Kautz have developed has led to a web application called GermTracker. The application color-codes users (from red to green) according to their health by mining information from their tweets for 10 cities worldwide. Using the GPS data encoded in the tweets the app can then place people on a map, which allows anyone using the application to see their distribution.
"This app can be used by people to make personal decisions about their health. For example, they might want to avoid a subway station if it's full of sick people," Sadilek suggested. "It could also be used in conjunction with other methods by governments or local authorities to try to understand outbursts of the flu."
It is now flu season and as the number of people with the flu across the U.S. increases, so do the number of people monitoring GermTracker. On some days in January 10,000 people visited the http://fount.in website where the app is hosted.
The model that Sadilek and his colleagues developed is based on machine-learning. At the heart of their work is how they are training an algorithm to distinguish between tweets that suggest the person tweeting is sick and those that don't.
"It's like teaching a baby a new language," Sadilek said. He explained that they first generated a training set of data, 5,000 tweets that had been manually categorized and from which the algorithm can start to distinguish what words and phrases are associated with someone being sick. He added, "We need the algorithm to understand that someone who tweets 'I'm sick and have been in bed all day' should be characterized as sick, but 'I'm sick of driving around in this traffic' shouldn't be."
The application is also improving the algorithm. Every time someone goes onto the application and clicks on one of the colored dots that represent the tweeting users, they can see the specific tweet that led someone to be classified in a specific way. The application asks you to assess the tweet yourself and say whether you agree with the classification or not. This gets fed back into the algorithm, which continues to learn from its mistakes.
The authors have recently started two collaborations with researchers at the University of Rochester Medical Center. "In one effort, we are planning to link Twitter predictions to clinical influenza studies," said co-author Kautz, chair of the University's computer science department. "In another effort, we are working with faculty in the Department of Psychiatry and the School of Nursing on extending these techniques to monitor and measure factors impacting depression and other psychological disorders."About the University of Rochester
Leonor Sierra | EurekAlert!
New Technologies for A/V Analysis and Search
13.04.2017 | Fraunhofer-Institut für Digitale Medientechnologie IDMT
On patrol in social networks
25.01.2017 | Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO
Whether you call it effervescent, fizzy, or sparkling, carbonated water is making a comeback as a beverage. Aside from quenching thirst, researchers at the University of Illinois at Urbana-Champaign have discovered a new use for these "bubbly" concoctions that will have major impact on the manufacturer of the world's thinnest, flattest, and one most useful materials -- graphene.
As graphene's popularity grows as an advanced "wonder" material, the speed and quality at which it can be manufactured will be paramount. With that in mind,...
Physicists at the University of Bonn have managed to create optical hollows and more complex patterns into which the light of a Bose-Einstein condensate flows. The creation of such highly low-loss structures for light is a prerequisite for complex light circuits, such as for quantum information processing for a new generation of computers. The researchers are now presenting their results in the journal Nature Photonics.
Light particles (photons) occur as tiny, indivisible portions. Many thousands of these light portions can be merged to form a single super-photon if they are...
For the first time, scientists have shown that circular RNA is linked to brain function. When a RNA molecule called Cdr1as was deleted from the genome of mice, the animals had problems filtering out unnecessary information – like patients suffering from neuropsychiatric disorders.
While hundreds of circular RNAs (circRNAs) are abundant in mammalian brains, one big question has remained unanswered: What are they actually good for? In the...
An experimental small satellite has successfully collected and delivered data on a key measurement for predicting changes in Earth's climate.
The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) CubeSat was launched into low-Earth orbit on Nov. 11, 2016, in order to test new...
A study led by scientists of the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) at the Center for Free-Electron Laser Science in Hamburg presents evidence of the coexistence of superconductivity and “charge-density-waves” in compounds of the poorly-studied family of bismuthates. This observation opens up new perspectives for a deeper understanding of the phenomenon of high-temperature superconductivity, a topic which is at the core of condensed matter research since more than 30 years. The paper by Nicoletti et al has been published in the PNAS.
Since the beginning of the 20th century, superconductivity had been observed in some metals at temperatures only a few degrees above the absolute zero (minus...
16.08.2017 | Event News
04.08.2017 | Event News
26.07.2017 | Event News
17.08.2017 | Physics and Astronomy
17.08.2017 | Earth Sciences
17.08.2017 | Physics and Astronomy