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Twitter mood predicts the stock market.

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech...

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Critical questions for big data

Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions) and are natural targets for machine-learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural-language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure and in cases where the invariances of these structures are built into networks used to model them.

Geometric Deep Learning: Going beyond Euclidean data

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

/pdf/private-traits-and-attributes-are-predictable-from-digital-1g2f7jd8kv.pdf

Private traits and attributes are predictable from digital records of human behavior

In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?

/pdf/the-parable-of-google-flu-traps-in-big-data-analysis-52z3z6f7bu.pdf

The Parable of Google Flu: Traps in Big Data Analysis

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

/pdf/computational-social-science-5el7814wob.pdf

Computational Social Science

Life in the network: The coming age of computational social science: Science

We live our lives in digital networks We wake up in the morning, check our e-mail, make a quick phone call, commute to work, buy lunch Many of these transactions leave digital breadcrumbs--tiny records of our daily experiences Reality mining, which pulls together these crumbs using statistical analysis and machine learning methods, offers an increasingly comprehensive picture of our lives, both individually and collectively, with the potential of transforming our understanding of ourselves, our organizations, and our society in a fashion that was barely conceivable just a few years ago It is for this reason that reality mining was recently identified by Technology Review as one of "10 emerging technologies that could change the world" Many everyday devices provide the raw database upon which reality mining builds; sensors in mobile phones, cars, security cameras, RFID ('smart card') readers, and others, all allow for the measurement of human physical and social activity Computational models based on such data have the potential to dramatically transform the arenas of both individual and community health Reality mining can provide new opportunities with respect to diagnosis, patient and treatment monitoring, health services planning, surveillance of disease and risk factors, and public health investigation and disease control Currently, the single most important source of reality mining data is the ubiquitous mobile phone Every time a person uses a mobile phone, a few bits of information are left behind The phone pings the nearest mobile-phone towers, revealing its location The mobile phone service provider records the duration of the call and the number dialed In the near future, mobile phones and other technologies will collect even more information about their users, recording everything from their physical activity to their conversational cadences While such data pose a potential threat to individual privacy, they also offer great potential value both to individuals and communities With the aid of data-mining algorithms, these data could shed light on individual patterns of behavior and even on the well-being of communities, creating new ways to improve public health and medicine To illustrate, consider two examples of how reality mining may benefit individual health care By taking advantage of special sensors in mobile phones, such as the microphone or the accelerometers built into newer devices such as Apple's iPhone, important diagnostic data can be captured Clinical pilot data demonstrate that it may be possible to diagnose depression from the way a person talks--a depressed person tends to speak more slowly, a change that speech analysis software on a phone might recognize more readily than friends or family do Similarly, monitoring a phone's motion sensors can also reveal small changes in gait, which could be an early indicator of ailments such as Parkinson's disease Within the next few years reality mining will become more common, thanks in part to the proliferation and increasing sophistication of mobile phones Many handheld devices now have the processing power of low-end desktop computers, and they can also collect more varied data, due to components such as GPS chips that track location The Chief Technology Officer of EMC, a large digital storage company, estimates that this sort of personal sensor data will balloon from 10% of all stored information to 90% within the next decade While the promise of reality mining is great, the idea of collecting so much personal information naturally raises many questions about privacy It is crucial that behavior-logging technology not be forced on anyone But legal statutes are lagging behind data collection capabilities, making it particularly important to begin discussing how the technology will and should be used Therefore, an additional focus of this chapter will be the development of a legal and ethical framework concerning the data used by reality mining techniques

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Using reality mining to improve public health and medicine.

This position paper argues for the establishment of a "Rosetta Stone" that maps behavioral signatures of population behavior to meaningful social categories. We propose a method for accomplishing this through the deployment of behavioral tracking combined with survey questionnaires. The goalis to identify salient patterns of behavioral activity and ask subjects to "explain" these patterns through their survey responses. As a case study, we provide analyses regarding calling patterns related to emergencies, such as bombings and hurricanes.

Devon Brewer

Papers

Computational Social Science

Life in the network: The coming age of computational social science: Science

Using reality mining to improve public health and medicine.

Matching Data and Interpretation: Towards a Rosetta Stone Joining Behavioral and Survey Data