Vidgen, B. and Yasseri, T. (2018), Detecting weak and strong Islamophobic hate speech on social media
Country
United Kingdom
Title
Vidgen, B. and Yasseri, T. (2018), Detecting weak and strong Islamophobic hate speech on social media
View full ResearchYear
2018
Type of publication
Report
Geographical coverage
National
Area/location of interest
Not applicable - national level
Type of Institution
Academic
Institution
UK, University of Oxford
Main Thematic Focus
Hate crime Incitement to hatred or violence
Target Population
Muslims Religious minorities
Key findings
Having acknowledged the harm that Islamophobic hate speech on social media causes to individuals and society as a whole, this research aims to development a tool to detect and classify Islamophobic hate speech.
The multi-class classifier that was developed distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content with 77.6% accuracy and 83% balanced accuracy.
Methodology (Qualitative/Quantitative and exact type used, questionnaires etc)
Quantative
Sample details and representativeness
The dataset consisted of 140 million tweets posted in 2017 and the first six months of 2018 by followers of mainstream and far0right political parties.
• 7,500 users randomly selected from followers of UKIP.
• 7,500 users randomly selected from followers of the Conservatives.
• 7,500 users randomly selected from followers of Labour.
• 7,500 users randomly selected from followers of the Liberal Democrats.
• All ~15,000 followers of the BNP.
• All 32,000 followers of Britain First.
• Every tweet produced by a set of 45 far right accounts (consisting of every group which appear in Hope Not Hate’s 2015 and 2017 reports on the far right.
• Every @ mention of the same 45 far right accounts.
DISCLAIMERThe information presented here is collected under contract by the FRA's research network FRANET. The information and views contained do not necessarily reflect the views or the official position of the FRA.