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Saturday, September 27 • 14:16 - 14:35
"#Hastagging Hate: Using Twitter to track racism online"

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Background: Under our current social context, discussing issues related to race is often very difficult and perceived as impolite in society. As a result, there is strong sentiment from people to feel that race (and consequently racism) is a thing of the past. Although overt forms of racism in a public setting aren’t as common as in the past (for the most part), one just has to shift focus to the online world, where overt forms of racism are rampant on various social media sites like Twitter. What is important to point out is that “new modes of communication mean it is easier than ever to find and capture this type of language” (Bartlett et. al, 2014: 5).

Objective: My current research project is looking at racist tweets in Canada (www.twitterracism.com).  Initially, I was interested in understanding how racist Canadians are on twitter and how the online level of racial intolerance connects to reported incidents of racism in the offline world (if at all).  As the project progressed, however, it became clear to me that a quantitative approach for this study was not feasible.  As a result, my research focus shifted qualitatively towards understanding the context these racist terms were being used on Twitter in six Canadian cities: Edmonton, Calgary, Vancouver, Toronto, Montreal, and Winnipeg.  This project is an overview of my 2013 case study of a small sample of racist tweets in Canada. 

Methods:  Between June, July, and August of 2013, data was collected on Twitter (using the Streaming API method) via Hootsuite, a social media management platform, to track the amount of times certain racist words were used on Twitter originating from users located in certain Canadian cities. Hootsuite was used to collect data since the platform allows users to search for tweets by both keywords and geographic location (something which cannot be done using the basic Twitter search API). Locations provide an ideal access point for researchers interested in geographically bounded research, however, it is still fairly limited since only about 1% of all traffic on Twitter is “geotagged” (Gaffney and Puschman, 2014), meaning only a small number of users are opting in to have their geographical location shown with their tweet. This data, however, carries immense commercial value (Wilken, 2014) and it is likely that the proportion of geotagged tweets will increase in the future (Gaffney and Puschman, 2014).

For this project, each city was searched via hootsuite, looking for any tweets containing the following racist words in a negative context: native(s); white trash; nigger(s); paki(s); and chink(s). These words were chosen because the majority of the words represent the most common racist terms associated with specific racialized groups. The term native, which is not usually associated with being a racist word, was surprisingly used in a negative way on twitter, and as such, was included as one of the key terms for this data set.  In order to determine if a tweet was used in a negative fashion, I would read each tweet individually and only capture tweets that were overtly negative in nature (see Appendix I for examples of tweets captured).  I would not capture any tweets which were used in a joking nature, nor would I capture tweets which appeared to use the terms in a “collegial” way (i.e. people referring to other similar race groups as “My nigga” or “that’s my paki”).

In order to capture this data using hootsuite, a search query was created to reflect the key word being searched originating from the cities latitude and longitude coordinates. For example, when searching for the key word paki originating from the city of Edmonton, the following was typed into the Hootsuite search bar: paki geocode:53.5381965637,-113.5029678345,100km. Essentially, what this syntax is looking for is the word (paki) originating from the coordinates of 53.3 and -113.5 (which is Edmonton’s geo-location) and the surrounding 100 kilometers. The same search query was used for Calgary, Vancouver, Winnipeg, Montreal, and Toronto, and over the span of three months, each tweet that came up which contained one of the above racist words from these cities was read by the researcher to determine the context of the tweet.

Results: For the purpose of this project, only tweets which contained the above words in a negative context were captured in the data and saved in an excel spreadsheet for further content analysis. The total number of tweets collected over the three month period was 776.

Conclusions: This data offers some initial results that generate a sense of a) where the most racist tweets come from and b) what racialized groups are commented on in a negative way on Twitter in the six Canadian cities.  After conducting a content analysis of the tweets, the following seven categories were established to understand how racist language is being used on Twitter in these six Canadian cities: 50% “Real time” response; 28% Negative Stereotype; 12% Casual use of slur; 4% Responses to racism; 2% Targeted abuse (online), Appropriated, and “Non-Derogatory”.


Bartlett, Jamie, Reffin, Jeremy, Rumball, Noelle, and Williamson, Sarah. (2014). Anti-Social Media. Retrieved from: http://www.demos.co.uk/files/DEMOS_Anti-social_Media.pdf?1391774638

Gaffney, Devin and Puschman, Cornelius. (2014). “Data Collection on Twitter”.  In: Twitter and Society, eds. . Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt, and Cornelius Puschman (New York: Peter Lang Publishing): 55-68. 

Wilken, Rowan. (2014).  “Twitter and Geographical Location”.  In: Twitter and Society, eds. . Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt, and Cornelius Puschman (New York: Peter Lang Publishing): 155-168.

[1]  Apart from the “real time response” category, the remainder of the categories used for the content analysis were inspired by the 2014 DEMOS study Anti-Social Media.  A full list of the definition of each category can be found here: http://www.demos.co.uk/files/DEMOS_Anti-social_Media.pdf?1391774638, p. 24


Irfan Chaudhry

University of Alberta

Saturday September 27, 2014 14:16 - 14:35
TRS 1-147 Ted Rogers School of Management

Attendees (6)