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Week 7 - Hate Crime - Coggle Diagram
Week 7 - Hate Crime
Predicting Crime
High fidelity data to improve predictions
Conventional data patterns
Geographic specific locations
Tweeting and TOD
Bendler et al - relationship of activity on Twitter and likeliness of crime in SanFran
Malieson and Anderson - twitter to measure mobile populationat risk from violent crime in leeds
Crime hot spots
Twtiter data represents pop at higher spatial and temporal resolutions
Gerber - statistical topic analysis of tweet text and correlates with crime in Chicago
Social Media monitoring
COSMOS
Geographically located social media
Network diagrams - interactions
Discourse analysis
Connection/tensions of users
Exposures of info
CLustering
Early warning mechanism
Cyberhate Woolwich Terror Attack - Impact
Lee Rigby death
Social media rapid consumption
Islamic Hate speech
Disaster strikes and immediate unorganized response
Rapid response to understand crime
INVENTORY
REACTION
Group Selection Model
US
No prejudice or hatred prescribes in law
No expression of identity based hostility
Suspect must simply intentionally select victim because of belonging to a protected group
Captures more hate crime cases
Difficult to prove selection of victim based on hate
Survival of Hate crime + User flows
Hate life span after impact
Erupting fast and declining rapidly
Far right agents outlast all others - 36/42 hrs
Police outlast all but far right
Police and far right lots of traction in aftermath
Half life of cyber hate
Online first responders
Agent Reactors and individual people
Counter hate narratives in the press
Counter hate from police
Prominence Far right political groups in impact
Hatred Motivation Model - Racial animus model
Europe - predjudice + hatred
Motivated by identity based hostility
Verbal expression of hotility
Previous hate crime, symbols, ideology = evidence
Model omits selection of victim based on vulnerability
Big Social Data
Expansion of digital publics
Production of hundred terabytes
Changes to how we do research
Gaining access to info