Racist tweets and countermessages by Stop Hate UK


Uploaded: 2022-03-02
Languages: English
Collected from: 2021
Access category: Open
Email: Not available
To: 2021

Summary

Excel spreadsheet of online hate speech identified by the charity "Stop Hate UK" as well as counter messages written by the charity in response to hate messages.


Subject keywords: online language, racism, twitter
Data types: Written
Funders: N/A
Associated AIFL centres: None
License: Non-Commercial Government Licence for public sector information

Description

The data is recorded on an excel spreadsheet, consisting of 160 rows of data. A charity worker from Stop Hate UK has created this excel spreadsheet for the purpose of my dissertation, so that I could clearly see the data. This dataset includes 160 examples of original hate messages sent by the hate user and 132 counter messages sent by the charity, in response to the hate messages. The 28 hate messages that were not responded to were deemed as too hateful to counter/reply to. The dataset also includes 20 examples where the original hate user has replied to the charity’s counter message, resulting in reply pairs and an online conversational interaction between the hate user and the charity worker. It is indicated within the hate speech columns where there were two hate tweets following on from each other, but by different users. This is indicated by ‘OTHER USER’ before the start of the next part of the tweet. If there were any media such as images, videos or links within the tweet, this is described in brackets (what the media was) to give context to the messages. The charity does not have the same person replying to each hate message identified. The dataset does not give information on how many different charity workers were involved and does not distinguish between which charity worker replied within each example. The dataset has been anonymised by omitting full names or twitter @ names for anonymity, using ‘BLANK’ to indicate where their names were. Tweets where people are only referenced by their first name have been left in the dataset. The charity worker who provided me with the data stated that these messages being countered are not necessarily classed as ‘hate speech’ in the legal sense, but have been identified as stirring up hatred, or contributing to hateful narratives. In cases where the messages would pass the legal threshold for ‘hate speech’, the charity worker said they would likely report this to a social media provider or the Police, rather than countermessaging. The messages have vastly come from the charity’s work in monitoring the online space, with only a few examples coming from people reporting hate to their helpline.


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