The first presenter at AoIR 2015 this morning is Fabio Giglietto, whose interest is in the Twitter response to the Charlie Hebdo attack. Very quickly, the hashtag #JeSuisCharlie emerged to express sympathy and support for the magazine; a negative #JeNeSuisPasCharlie also emerged, however, to critique the magazine's actions. Fabio's interest here is in how this hashtag was discursively positioned.
There were some 74,000 tweets by 41,000 unique users with the negative hashtag, between 7 and 11 January 2015, and Fabio's team used quantitative and qualitative approaches to analyse them. They followed the method developed by Stefan Stieglitz and me to classify hashtags by the percentage of URLs and retweets in the dataset, and the hashtag fit the general pattern for crisis events.
Amongst the most common retweets in the datasets were tweets containing images of cartoons critical of Charlie Hebdo, stating that the magazine had falsely used free speech as a defence for publishing offensive content. Indeed, many common retweets contained images; links to news sites were rare because the main #JeSuisCharlie hashtag served that purpose already. Many of the tweets with the negative hashtag contained only that hashtag, indeed.
A further content analysis of the tweets through manual coding revealed additional patterns. Earlier tweets often contained hashtags only (17% in the earliest phase), but this declines, and more links gradually appear. Images embedded in the tweets often contained text, and were used to move beyond the 140-character limit for tweets; posting images with text also means that the text cannot be modified in subsequent retweets, of course.
Much of the discussion especially in the middle phase of the hashtag addresses what the limits to free speech are, and this shows a resistance to the mainstream media framing of the Charlie Hebdo shooting; at the same time, users employed strategies to defend themselves against accusations of endorsing violence. These evolved over time, and decreased as the total number of tweets in the negative hashtag also declined over time.
