A text-mining project for the social scientist (who likes computers, but doesn’t know how to code)

Part 2 — Your first taste of R

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This tutorial follows “From Spiders to R and back — Part 1”.

So we have a dataset with lines and lines of textual information, a group of tweets, newspaper articles, extended web posts — what do we get from all this? How can all this data actually inform our social scientific work? How could it be more meaningful than interviewing real groups of people?

To answer this last question: it is honestly not more meaningful than interviewing people, but it’s definitely meaningful in a different way. We’ll talk about the ‘how so’ as we go through this post. For now, it is important that we understand that these computational methods answer different questions, and that they sometimes makes us reflect on things we might have never thought about when looking at social issues. They give us an oddly blank-slate perspective. And this in itself is quite useful. Knowing how to approach both methodological worlds, I would argue, is the best option by far. …


Fairness and Bias

Pushing the boundaries of Social Data Science

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Photo by NASA on Unsplash

The buzz-phrase of our time, “Artificial intelligence,” has inspired all sorts of musings: from visions of utopic — and dystopic — futures freed from human labour, to more focused critiques on algorithmic discrimination. The reality is the ‘good, bad and the ugly’ of AI drives some of the most fundamental philosophical and societal questions we face today — and consequently much of our evolving tech legislation and policy.

Algorithms have become ever faster at processing unfathomable amounts of information, as computing power continues to increase. Algorithms have taken over such diverse aspects of our lives it is hard to keep track of the decision-making and ‘intelligent’ technologies they enable: search engine results, ad content, creditworthiness, recommendation for movies we might like, eligibility for a job, Uber routes and drivers, genetic predispositions — they even help doctors make diagnoses and compose music in a particular style. …


A text-mining project for the social scientist (who likes computers, but knows not how to code)

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For those who are curious about what these new computational methods and machine learning have to offer the discipline, here’s a taster! I’m going to be taking you through how to start (and hopefully finish) a computational social science project throughout the course of this post (and another 2).

Granted, I’m going to take you through a highly niche — can’t stress that enough — area of study: groups on the extreme left in the United Kingdom that are supporters of the Maduro government in Venezuela. Niche indeed. For some context on the situation in Venezuela at the moment (and as a means to draw conclusions on your own) I recommend you check out this documentary on BBC’s iPlayer. …


A text-mining project for the social scientist (who likes computers, but knows not where to start)

Part 1 — Webscraping with Python

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For those who are curious about what these new computational methods and machine learning have to offer the social sciences, here’s a taster! I’m going to be taking you through how to start (and hopefully finish) a computational social science project throughout the course of this post (and another 2).

Granted, I’m going to take you through a highly niche — can’t stress that enough — area of study: groups on the extreme left in the United Kingdom who are supporters of the Maduro government in Venezuela. Niche indeed. For some context on the situation in Venezuela at the moment (and as a means to draw conclusions on your own) I recommend you check out this documentary on BBC’s iPlayer. …

About

Parvathi A. Subbiah

PhD Politics and Sociology | University of Cambridge | Gates Scholar | Social_Data_Science

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