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Simulating skill in the Premier League: part1
I love sports. Every week I watch Tottenham play, and just as regularly I go through the emotional roller-coaster that entails. As a sports fan I use the first person plural to describe 'my' team, and...
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A giant step for man...
no wait, 'a giant step for me'... a massively underwhelming one for mankind: the Memory at War Project newsletter features a writeup I did on detecting Memory Events in Russian press coverage of the...
View ArticlePutin’s bot army - part one: a bit about bots
In this first of a three-part series on how the pro-Kremlin youth group Nashi simulated popular support for Putin around the 2011 and 2012 elections, I briefly introduce what bots are and how they are...
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Putin's bot army - part two: Nashi's online campaign (and undesirable bots)
Part one of 'Putin's bot army'examined what bots are, and how they have been used to simulate online interaction. In part two I explore in detail email correspondence between Nashi activists to...
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Topic maps
I've been exploring ways of calculating the subject matter discussed by Russian newspapers recently. In the end I settled on using TF-IDF (Term Frequence - Inverse Document Frequency) keywords on a...
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Tennis simulator
This Wimbledon has seen a large number of injuries and withdrawals, prompting commentators to ask - could this be coincidence, or has something changed to make the courts more dangerous? Hearing people...
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Fun simulating Wimbledon in R and Python
R and Python have different strengths. There's little you can do in R you absolutely can't do in Python and vice versa, but there's a lot of stuff that's really annoying in one and nice and simple in...
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Failures in Gephi
I'm sure most users of Gephi have had moments where they stumble across incredible visualizations that make no apparent sense. I have found the most spectacular failures often to give the most...
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Scaling up text processing and Shutting up R: Topic modelling and MALLET
In this post I show how a combination of MALLET, Python, and data.table means we can analyse quite Big data in R, even though R itself buckles when confronted by textual data. Topic modelling is great...
View ArticleThe Challenge of not-quite-Gargantuan Data (and why DH needs SQL)
I felt strangely belittled when Andrew Goldstone tweeted about a recent blog entry:So familiar…Dealing w/ R's habit of choking on not-even-medium data. MT @RolfFredheim: Shutting up R:...
View ArticleDatabases for text analysis: archive and access texts using SQL
This post is a collection of scripts I've found useful for integrating a SQL database into more complex applications. SQL allows quickish access to largish repositories of text (I wrote about this at...
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[deleted post on] d3 visualisations of the GDELT data
I accidentally deleted my post on visualising the GDELT data using d3, and because it was really fiddly to make blogger display it properly in the first place, I won't reupload it. Instead here is the...
View ArticleTop Seven Tips for Processing 'Foreign' Text in Python (2.7)
Following on from my guide to making R play nice with utf-8, here is a seven-step guide to understanding Python's handling of unicode. Trust me, if you work with non-latin characters, you need to know...
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Topic Modelling Media Coverage of Memory Conflicts
Ostensibly this is a blog about memory conflict. It has become more of a repository of script snippets and visualisations, but here I get back to my roots and apply topic modelling to the...
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Visualising Structure in Topic Models
How exactly should we visualise topic models to get an overview of how topics relate to each other? This post is a brief lit review of that debate - I realise the subject matter is sooo last year. I...
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Plugging hierarchical data from R into d3
Here I show how to convert tabulated data into a json format that can be used in d3 graphics. The motivation for this was an attempt at getting an overview of topic models (link). Illustrations like...
View ArticleWeb-Scraping: the Basics
Slides from the first session of my course about web scraping through R: Web scraping for the humanities and social sciencesIncludes an introduction to the paste function, working with URLs, functions...
View ArticleProtests in Ukraine 18-20 February
The map below, based on data from here shows where the people killed in the protests of 18-20 February came from.* I can't vouch for the accuracy of the data. Apparently most of the protesters killed...
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Detecting bots
This is part 3 of the series about Nashi bots. If doing this currently I would approach the problem differently, but to my knowledge NodeXL is still a viable way of accessing the Twitter API.Part -...
View ArticleWeb Scraping part2: Digging deeper
Slides from the second web scraping through R session: Web scraping for the humanities and social sciencesSlides from the first session here...the third session here... and the fourth and final session...
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