Twitter is known for the abundance of racialized messages posted on its platform. Eruptions of racist abuse occur within a contested array of Twitter discourses, e.g. racial banter, ambivalent humour, hate comments and (anti-)racist sentiments. Surprisingly, only limited studies of everyday ‘race-talk’ on Twitter have been undertaken, and little is known about its ambient stream of racialized expression.
This project explored how racialised messages unfold in Twitter by focusing on the hashtag #notracist. Twitter users can include this (and other related) hashtags in messages to label seemingly ‘racist’ statements (and images/videos) as ‘not racist’. The practice of hashtagging as a strategy of denying racist expression or propagating the ambiguities of race talk enables an understanding of the contested racialized digital ecology of Twitter.
The project is funded by British Academy/Leverhulme Small Research Grant (Apr 2013 – May 2014).
Research Associate: Dr Phillip Brooker (Brunel University)
A significant tweeting practice we note in the #notracist dataset is the usage of more than one hashtag in a tweet – a phenomena we call Multi-Hashtagging.1) For the #notracist dataset, aside from the original#notracist term, there are a further 7717 hashtags, which are used in a variety of ways, for example:2)
helen_louise_: I literally cant stop eating watermelon. & Im not even black. #NotRacist #JustSaying
PaneKilla: How to say the alphabet in vietnamese #funny #notracist #accent #alphabet #vietnamese#peace #lol http://instagram.com/p/…
We used Chorus to plot a ‘cluster map’ of key multi-hashtags (fig. 1). The Chorus software suite includes a Cluster Explorer feature, which enables the co-occurrence of any terms within a Tweet to be identified.
In this map, each node (i.e. point in the map) is a hashtag, and the position and connectivity of nodes is determined by how frequently those hashtags are used together (co-occurrence), such that ‘similar’ hashtags cluster together (i.e. ones which are used in comparable ways to express similar sentiments). The model reveals how hashtags relate to each other as ‘semantic’ entities. The two radials overlaid on the image facilitate the process of reading the visualisation.