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big data and social movements

Mobilizing Ideas has a month long discussion about dig data and movement research. From Part I:

Part II:

50+ chapters of grad skool advice goodness: Grad Skool Rulz ($2!!!!)/From Black Power/Party in the Street!!

Written by fabiorojas

April 8, 2015 at 12:11 am

Posted in big data, fabio, social movements

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  1. “The Roller”, eh?

    Like

    Alex Hanna

    April 8, 2015 at 12:21 am

  2. Urban Dictionary has a pretty awesome definition for haterade: “a figurative drink representing a modality of thought. those who consume it are themselves consumed by the negativity which with they speak.”

    If that makes you too scared to follow the link, I can offer a one sentence summary: It will be tricky to incorporate machine learning techniques into the sociological toolkit because ML methods are not primarily designed to test hypotheses.

    If you are still intent on playing with some new toys, I’ve been putting together a list of tutorials that are useful for social scientist who want to learn Python: http://nealcaren.github.io/python-tutorials/

    Like

    neal caren

    April 8, 2015 at 1:03 am

  3. “ML methods are not primarily designed to test hypotheses.”

    I think this sort of comment baffles me. Sociologists use lots of methods that aren’t hypothesis tests. For example, coding qualitative data. Not a test, but a way of generating data that could be used for descriptive work and/or testing. The issue is the quality of coding, which can be assessed with specific methods.

    Similarly for machine learning. No, it isn’t hypothesis testing. It’s a form of data coding. And like all data generation methods, it can be subject to assessment. And, thankfully some basic forms of machine learning are showing improvements and, in some cases, have exceeded the capacity of humans to correctly code data (e.g., computers can more accurately identify gender from a portrait than a person). There are limits, but it is promising.

    Like

    fabiorojas

    April 8, 2015 at 1:54 am


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