Mapping Online Publics

OK, this may be a somewhat esoteric subject for researchers who mainly work with Twitter data from specific countries and cultures, but over the past few weeks I’ve been working on a paper that analyses Twitter activities in the #egypt and #libya hashtags – and as part of that work, I’ve been interested in exploring the interactions between users tweeting in Arabic and users tweeting in other languages (mainly in English). Unfortunately, there’s no reliable means of identifying the language of specific tweets, or of the users who post them; while the Twitter API provides an ISO language code (e.g. ‘en’ for English, ‘no’ for Norwegian, etc.) for each tweet, this is drawn simply from the overall language setting of the user’s account, and not specific to each individual tweet itself. For users who alternate between languages in their tweeting, all tweets will be tagged with their chosen language code; for users who haven’t bothered to change their Twitter profile settings away from the default English, all their tweets will be tagged ‘en’, regardless of their actual language.

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Regular readers of this blog will know that we’ve now examined Twitter activity around a number of recent events in some detail – from the Labor leadership spill in Australian politics in June 2010 through to the subsequent election, to the recent floods in Queensland and beyond. On that basis, we now also in a position to make some comparisons between these events: in the first place, to examine how they unfolded, and how much of the wider Twitter userbase they’ve been able to mobilise.

So, building on the work we’ve already done, and adding a few more case studies into the mix, here’s an overview of activity within selected Twitter #hashtags – in each case, over the course of their most active day. The process is similar in each case: retrieve a full #hashtag archive from Twapperkeeper, run our explodetime.awk Gawk script over the data to identify daily and hourly activity, then pick the 24 hours during which the volume of tweets in the #hashtag was most significant.

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