#Hashtagging hate: using Twitter to track racism online

Chaudhry, Irfan

#Hashtagging hate: using Twitter to track racism online

Author

Chaudhry, Irfan

Date

2015

Keywords

Twitter, race, racism, social media

Abstract (summary)

Under our current social context, discussing issues related to race are often very difficult and perceived as impolite. As Malinda Smith notes, “there is a belief that to talk openly about race matters is an affront to good manners.” [1]. As a result, there is strong sentiment from people to feel that race (and consequently racism) is a thing of the past. While it is important to acknowledge this may be a byproduct of living in a multicultural and pluralistic society such as Canada, not being able to talk openly about issues related to race makes it difficult for Canada to become a place that is diverse and inclusive of all people, as both overt and covert forms of racism are able to persist. Although overt forms of racism in a public setting are less frequent than in the past (for the most part), one can shift focus to the online world, where overt forms of racism are rampant on social media sites, such as Twitter. A recent report released by Demos (a U.K.-based think tank), for example, found that on average, there are roughly 10,000 uses (per day) of racist and ethnic slurs in English being used on Twitter (Bartlett, et al., 2014). While this appears to be a high number, it is important to note there are no comparative figures which this finding can be contrasted with. For example, is this figure any higher or lower than what one might find on sites such as Facebook or Instagram? Although we currently do not have the information to make this comparison, it is important to remember that “new modes of communication mean it is easier than ever to find and capture this type of language” [2]. In light of new communication technology, social media sites like Twitter allow us to view and track racist language like we have never been able to do before. In recent years, racist graffiti sprawled on the sides of businesses or homes would have been the most overt text-based form of seeing racist language in a public area, however, with the rise and growth of communication technology (and social media specifically), the online realm has turned into a space where racist language is used openly. As Manuel Castells points out, “the fundamental change in the realm of communication has been the rise of self communication — the use of Internet and wireless networks as platforms of digital communication” [3]. The rise of digital communication tools (like Twitter) has given anyone with something to say a ‘digital soapbox’, where they can tweet their thoughts, values, and opinions on a variety of issues. While most Twitter users will tweet about news stories (Tao, et al., 2014), some users may take to Twitter to espouse hateful sentiment. The older Twitter gets, the more its service (like the rest of the Web) becomes a vehicle for trolls [4] to challenge the social contract in a way that they might not be able to on the street (Greenhouse, 2013). Although the use of racist language online is not a new phenomenon (see Foxman and Wolf, 2013), what is new is the ability for users to strategically track and monitor racism online. Due to Twitter’s “free speech” ideal (Greenhouse, 2013), it does not filter out terms or threads that are racist in nature. As a result, users can easily track and monitor racist language. The ability to track racist language on Twitter provides researchers interested in examining race and racism with a unique way to collect research data. While there are a number of paid services that can provide Twitter data for users (such as Gnip or Datasift), these services are often costly, focus on large sets of data, and require added expertise with different data formats for users to utilize. As a result, researchers have been hesitant to utilize Twitter as a data gathering source. Due to the structure of Twitter, however, users can still collect data in an efficient and strategic manner, without the need to rely on costly data providers or learning a new data format. In this paper, I will consider three different projects that have used Twitter to track racist language: 1) Racist Tweets in Canada (the author’s original work); 2) Anti-social media (a 2014 study by DEMOS); and, 3) The Geography of Hate Map (created by researchers at Humboldt University) in order to showcase the ability to track racism online using Twitter. As each of these projects collected racist language on Twitter using very different methods, I will provide a discussion of each data collection method used as well as the strengths and challenges of each method. More importantly, however, I will highlight why Twitter is an important data collection tool for researchers interested in studying race and racism. Before discussing these projects, however, I will provide a brief genealogy of Twitter and how it is transforming from a social media platform to a useful space for researchers.