Basic Twitter Analytics

I joined Twitter in early 2008, 18 months after it was officially launched, but only started tweeting regularly after 2010. I have however never attempted to do any data mining on my tweets. I should probably say text mining instead, as Twitter is essentially a platform that captures words in sentences limited to 140 characters,1 Some studies suggest that in English the average number of words in a sentence should be between 9 and 14 to increase readability. See http://www.onlinegrammar.com.au/how-many-words-are-too-many-in-a-sentence/. Note that hashtags are part of the analysis., including web links.

mytweetsallOne easy way to do a simple analysis is to generate a word cloud of tweets. A word could presents in graphical format the words used in tweets, ranked by frequency of use. Words most used

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Endnotes   [ + ]

1. Some studies suggest that in English the average number of words in a sentence should be between 9 and 14 to increase readability. See http://www.onlinegrammar.com.au/how-many-words-are-too-many-in-a-sentence/. Note that hashtags are part of the analysis.

Towards a Political Economy of (Open) Data

Apps and data

Almost five years ago, while working together with former UN colleagues, we decided to create a mobile app that could show data on the progress towards  the achievement of the UN Millennium Development Goals (MDGs). The key purpose of the project was to raise awareness of people in general on how the MDG targets (almost 50 of them) were moving in time at the national level. The app also allowed to compare targets across countries as well as aggregate data by regions.

mmdgsDeveloping the app, which we did initially for Android mobiles, was the easy part. Getting the actual data however proved to be an almost insurmountable challenge. While a few countries did have available data, getting official data for most of them was complicated as in some case the data did not exist in digital

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