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Bits 'n Pieces on Big Data
Innovative information and insight into Big Data (if you like the content, please consider donating to my bitcoin address #3Pjof6N9xRAYXXSPZ4EAFLfHGn51ZdPcxi)
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Understanding Brains: Details, Intuition, and Big Data

Understanding Brains: Details, Intuition, and Big Data | Bits 'n Pieces on Big Data | Scoop.it

Understanding how the brain works requires a delicate balance between the appreciation of the importance of a multitude of biological details and the ability to see beyond those details to general principles. As technological innovations vastly increase the amount of data we collect, the importance of intuition into how to analyze and treat these data may, paradoxically, become more important.

 

Marder E (2015) Understanding Brains: Details, Intuition, and Big Data. PLoS Biol 13(5): e1002147. http://dx.doi.org/10.1371/journal.pbio.1002147 


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Engineering the public: Big data, surveillance and computational politics | Tufekci | First Monday

Engineering the public: Big data, surveillance and computational politics | Tufekci | First Monday | Bits 'n Pieces on Big Data | Scoop.it

Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these tools to carry out highly effective, opaque and unaccountable campaigns of persuasion and social engineering in political, civic and commercial spheres. I examine six intertwined dynamics that pertain to the rise of computational politics: the rise of big data, the shift away from demographics to individualized targeting, the opacity and power of computational modeling, the use of persuasive behavioral science, digital media enabling dynamic real-time experimentation, and the growth of new power brokers who own the data or social media environments. I then examine the consequences of these new mechanisms on the public sphere and political campaigns.

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CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises

CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises | Bits 'n Pieces on Big Data | Scoop.it

Locating timely and useful information during crises is critical for making potentially life-saving decisions. As the use of Twitter to broadcast useful information during such situations becomes more widespread, the problem of locating it becomes more difficult. CrisisLex is a lexicon of terms that frequently appear in crisis-relevant tweets. CrisisLex can be used to collect crisis-related messages from Twitter, and to automatically identify new terms that describe a specific crisis.

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The Parable of Google Flu: Traps in Big Data Analysis

The Parable of Google Flu: Traps in Big Data Analysis | Bits 'n Pieces on Big Data | Scoop.it
onur savas's insight:

The paper in PDF is at http://gking.harvard.edu/files/gking/files/0314policyforumff.pdf.

 

 

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Twitter buzz about papers does not mean citations later

Twitter buzz about papers does not mean citations later | Bits 'n Pieces on Big Data | Scoop.it
Analysis of science on social media service finds little correlation with standard measures of academic success.
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Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies

Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies | Bits 'n Pieces on Big Data | Scoop.it

In the wake of the on-going digital revolution, we will see a dramatic transformation of our economy and most of our societal institutions. While the benefits of this transformation can be massive, there are also tremendous risks to our society. After the automation of many production processes and the creation of self-driving vehicles, the automation of society is next. This is moving us to a tipping point and to a crossroads: we must decide between a society in which the actions are determined in a top-down way and then implemented by coercion or manipulative technologies (such as personalized ads and nudging) or a society, in which decisions are taken in a free and participatory way and mutually coordinated.

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Data Mining Reveals the Factors Driving the Price of Bitcoins | MIT Technology Review

Data Mining Reveals the Factors Driving the Price of Bitcoins | MIT Technology Review | Bits 'n Pieces on Big Data | Scoop.it
Two years ago a single bitcoin was worth around $5. Today it is worth around $600. Now one economist has worked out exactly what forces are behind this dramatic increase.
onur savas's insight:

Ref: arxiv.org/abs/1406.0268 : What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis

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Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual dating preferences. We study how users' online dating behaviors correlate with various user attributes using a large real-world dateset from a major online dating site in China. Many of our results on user messaging behavior align with notions in social and evolutionary psychology: males tend to look for younger females while females put more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. In addition, we observe that the geographic distance between two users and the photo count of users play an important role in their dating behaviors. Our results show that it is important to differentiate between users' true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, and there is significant discrepancy between a user's stated dating preference and his/her actual online dating behavior. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.

 

Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site
Peng Xia, Kun Tu, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, Cindy Chen, Benyuan Liu, Don Towsley

http://arxiv.org/abs/1401.5710


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Urbansocial's curator insight, July 14, 2014 11:41 AM

Urban Social - Online dating for sociable singles www.urbansocial.com

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Governing Algorithms: A Provocation Piece

Governing Algorithms: A Provocation Piece | Bits 'n Pieces on Big Data | Scoop.it

"Algorithms have developed into somewhat of a modern myth. They “compet[e] for our living rooms” (Slavin 2011), “determine how a billion plus people get where they’re going” (McGee 2011), “have already written symphonies as moving as those composed by Beethoven” (Steiner 2012), and “free us from sorting through multitudes of irrelevant results” (Spring 2011). Nevertheless, the nature and implications of such orderings are far from clear. What exactly is it that algorithms “do”? What is the role attributed to “algorithms” in these arguments? How can we turn the “problem of algorithms” into an object of productive inquiry? This paper sets out to trouble the coherence of the algorithm as an analytical category and explores its recent rise in scholarship, policy, and practice through a series of provocations.

onur savas's insight:

You can download the paper form the very link.

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