Policing, Technology and Hate Crime Flashcards
Untangling the Web: Defining Cybercrime
Spatial and Temporal Dimensions
Traditional crimes:
Static
Crimes often socially, politically and legally rationalised
Offenders are often socio-economically marginalised
Cybercrimes:
Distanciated (Giddens 1990)
Lack of social, political and legal recognition
Offender most likely to be socio-economically privileged
Untangling the Web: Defining Cybercrime
3 categories of Cybercrime (Wall 1998)
Facilitates existing criminal activity
New crimes recognised by existing laws
New ‘harms’ unrecognised by laws
Counting Cybercrime
Cyber-enabled (existing crimes transformed e.g. hate)
Cyber-dependent (new crimes, e.g. viruses, ddos)
Personal Cybercrime across Europe
- Eurobarometer Cybersecurity Survey (ID Theft)
Personal Cybercrime Eurobarometer Cybersecurity Survey (ID Theft) Increased chance of victimisation: Selling on auction sites Using university computers Using public computers (Library) Younger Poorer Countries with lower Internet penetration
Cybercrime laws
Computer Misuse Act 1990
Hacking
Malware
Denial of Service (including terrorism)
Offences Against the Person Act 1861, Malicious Comms Act 1988
Threats of violence or messages of menacing character
Protection from Harassment Act 1997
Online stalking (fear of violence or serious alarm or distress)
Criminal Justice and Courts Act 2015
Disclosing private sexual images without consent
Sexual Offences Act 2003
Online Grooming
Online Hate Speech laws
Crime and Disorder Act 1998, Malicious Communications Act 1998 and the Communications Act 2003
Hateful social media posts (other than those which amount to specific offences in their own right such as making threats to kill, blackmail, stalking etc.) will be considered to be criminal if:
Their content is grossly offensive
Their content is threatening or abusive and is intended to or likely to stir up racial hatred
Their content is threatening and is intended to stir up hatred on the grounds of religion or sexual orientation
It is important to note that when considering cases involving hateful communications, prosecutors operate a high threshold at the evidential stage and consider whether a prosecution is in the public interest based on the nature of the communication and the impact upon the targeted victim.
They must also be satisfied that the communication is not protected under the free speech principle (under Article 10) of the European Convention on Human Rights, that provides the freedom to cause offence.
Regulatory Mechanisms
More useful to speak of Governance
“regulation of relationships in complex systems”
Mix of public/private, state/non-state, national/inter-national institutions and practices
Diverse, dynamic, decentralised approach
Current Policy Initiatives in the UK
UK National Security Strategy (HM Government, 2015)
Cybercime a tier one threat (above organised crime)
UK Cyber Security Strategy (Cabinet Office, 2016-2021)
Invest £1.9 billion over next 5 years (lots of graduate jobs!)
DEFEND against attacks
DETER via detection, investigation, disruption & prosecution
DEVELOP cyber security skills
INTERNATIONAL ACTION via partnerships
GCHQ National Cyber Security Centre
Supports Gov, industry and SMEs
Education/Training
University Academic Centres of Excellence
UK first response to national cyber threats (e.g. WannaCry)
Cyber Security Information Sharing Partnership (CiSP)
Current Police Initiatives in the UK
National Crime Agency
CEOP Command
- Coordinates child protection online and offline
Economic Crime Command
- Money laundering; International corruption; Fraud; Counterfeit Currency
National Cybercrime Unit
- Investigate; Target hardening; Intelligence; Partnership.
Action Fraud & National Fraud Intelligence Bureau (City of London Police)
National Online Hate Crime Hub
True Vision
Public/private partnerships
Nhan and Huey (2008)
“nodal clusters” that form the cybercrime reduction network
government (including international and national criminal justice, non-criminal justice and local) law enforcement (from international to the local) private industry (across all sectors, large, medium, small and micro) general public (civil society groupings both on and offline)
Public/private partnerships
Dupont (2004)
> Five forms of capital that shape nodal networks
- social capital (foster and sustain mutually beneficial social relations with other nodes)
- cultural capital (knowledge possessed by a node that can be used and offered up to other nodes)
- political (theoretical and/or working knowledge of local, national and international political structures)
- economic (knowledge of international markets and purchasing power)
- Symbolic capital (overarching form that affords organisational legitimacy)
Public/private partnerships
eCrime Partnership Mapping Study (Levi & Williams 2011)
Perceptions and measures of
eCrime prevalence largely
symmetrical
Significant gaps in cooperation
frequency and quality between
government and finance sector
and private sector other (SMEs?)
Third sector organisations and
local government on the
periphery of the UKIA network
Public/private partnerships
Explanations for poor cooperation:
over-crowded cybersecurity space
criminal justice system’s poor record in apprehension and prosecution
inhibiting legislation and historically poor engagement with SMEs
difficult to justify a business case for spending in austere times
low levels of network capital
Technology as Regulator
Williams (2015) Guardians Upon High
Applied RAT to Online ID Theft
Offender, Opportunity, Lack of Guardianship
Eurobarometer Special Cybersecurity Survey
Interested in effectiveness of 3 types of guardianship
Guardianship (Williams 2015)
Heightened positive effect of country Internet penetration on both avoidance personal and passive physical guardianship.
Those residing in countries with low Internet penetration (and hence possibly under-developed infrastructure), who adopt these types of guardianship were likely to experience more incidents of online identity theft, compared to those adopting these types of guardianship in countries with higher Internet penetration (and hence possibly better developed infrastructure)
Maturity of cyber security strategy moderated differences in incidents of online identity theft between adopters of different types of individual guardianship
Adopters of passive guardianship residing in countries with more mature cyber security strategies experienced decreased levels of online identity theft.
Technology as Regulator
Tri-modal regulation
> Law - retroactive
> Market/Social – retroactive
> Technical – proactive
- Situational preventative methods
- Increasing the perceived effort, increasing the perceived risks and reducing anticipated rewardsTechnology as Regulator
> Lessig and ‘digital realism’ Disrupt human action Technology is malleable Pervasive technology Rapidly adaptive Preventative Non-contentious
Technology as Regulator
Criticism
> Hosein, Tsiavos and Whitley (2003)
- Technology a biased cultural artefact
- Code-writers as alternative sovereigns
Police Use of Social Media
Mainly used as a tool to broadcast information to public, engagement on social media not fully applied in practice (Crump, 2011).
Communications on social media have been considered best during certain situations, see literature on 2011 riots, and digital events such as 24/7 tweetathons.
During the 2011 riots, Twitter communications ‘differed detrimentally’ between GMP and the Met Police (Denef, Bayerl and Kaptein, 2013).
Analysis of police Twitter use in Canada suggests the public have greater opportunities to interact and hold the police to account, while the police have further opportunities to enhance police-community parternships (O’Connor, 2015).
Police Use of Social Media(continued)
Communication is a central factor in the relationship between the police and communities (Hohl et al., 2010).
Increases the opportunities to connect with people who might not routinely come into contact with police.
Enables police forces to increase efficiency of practice and to try out public communications or engagements not previously possible (Brainard and Derrick-Mills, 2011).
Bring humour and positive messages from the police forces centrally, to build on favourable images of policing.
Allows police to provide swift counter communications, particularly in response to content which may impact perceived legitimacy or police image.
Normalisation or Transformation?
Social media use has not been observed as transformative by some academics, due to a lack of public engagement (Bullock, 2017).
Platforms such as Twitter have not been utilised to there full potential by police forces across England and Wales (Crump,2011).
However, other academics have argued that social media has been transformative for police communications (see, Kudla and Parnaby, 2018; Schneider, 2014).
Big Social Data
Ninety percent of the world’s data has been created in the past two years (BIS 2013)
Expansion of ‘digital publics’ to an unprecedented level
2.77 billion non-unique users, with Facebook, Google+ and Twitter accounting for over half of these
Production of hundreds of petabytes of information daily, with Facebook users alone uploading 500 terabytes (5.2 million megabytes) of data daily
UK Data Archive (Census, CSEW, all Gov. surveys) currently holds between 2.2 and 15 terabytes of data
Within the UK alone there are 15 million+ registered Twitter users posting on average 30 million tweets per day
Of these online social interactions, a sizable portion are relevant to policing (crime, fear of crime, perceptions of the police)
Predicting Crime Patterns
Bendler et al. (2014) examine the relationship between activity on Twitter in San Francisco and location and likeliness of different crime types to emerge in proximity.
Absence of Twitter use was predictive in relation to Burglary, MVT, Robbery and Theft.
Malleson and Andresen (2014) use Twitter data to measure mobile populations at risk from violent crime in Leeds
They find new violent crime hotspots outside of the city centre
Twitter data represents mobile populations at higher spatial and temporal resolutions than other available data.
Gerber (2014) conducts statistical topic analysis of Tweet text and correlates in with crime in Chicago.
Twitter data was shown to improve models for stalking, criminal damage and gambling, but decrease performance for arson, kidnapping and intimidation.
The first two studies simply use Twitter activity as a proxy for population density, and ignore the rich content of the tweets themselves
The third study combines an analysis of text, but fails to show how topics relate to crime types (e.g. CRIM. DAM.: centre united blackhawks bulls; THEFT: aquarium shedd adler planetarium; PROS.: Lounge studios continental village ukrainian)
A more nuanced approach would involve the classification of crime and disorder related content in tweet text and correlating with police recorded crime
Survival of Cyberhate
Extreme Cyberhate dies out within 20-24 hrs (impact stage)
Moderate Cyberhate dies out within 36-42 hrs (inventory stage)
Tweets without Cyberhate last longest (into reaction stage)
Sharp de-escalation resonates with the work of Legewie (2013) and King and Sutton (2014) who postulate that the increase in offline anti-immigration sentiment and hate crimes and incidents following terrorist events have a half-life
Survival of User Flows
Far Right outlast all other agents up to 36-42 hours (impact/inventory stage)
Police outlast all other agents but the Far Right in the 36-42 hour window (impact/inventory stage)
Both lose ground to Political Agents, News Agents who last longest
Information flows emanating from police were most likely to be large and to be long-lasting (bar the Far Right) in the impact and inventory stages
Information flows emanating from Far Right Political Agents were likely to be small in size, but the most long lasting in the same periods
Cyberhate Results
Police were more likely to be retweeted by a factor of 5.7
Requests for information:
@metpoliceuk: “We are appealing for anyone who may have witnessed the incident in #Woolwich to contact us via the Anti-Terrorist Hotline”
Case updates:
@metpoliceuk: “Two men aged 22 and 28 arrested on suspicion of murder remain in hospital in a stable condition #woolwich”
General commentary
@PoliceFedICC: “EDL marches on Newcastle as attacks on Muslims increase tenfold in the wake of Woolwich machete attack”
Half-life of Cyberhate
Counter hate narratives in the press
Moving information flows onto wider contextual issues during the reaction phase
Closing down the possible spaces for the contagion of hate fuelled by the unorganised response to death, speculation and rumour indicative of the impact stage
Counter hate narratives in police tweets
Social media affords police agents with a direct line of communication with citizens, bypassing the usual mass media filter – opening up possibilities to influence public opinion around events
Prominence of Far Right Political Groups in impact stage
The small but sustained nature of these flows indicates that there is limited endorsement of these twitter narratives, but where there is support it emanates from core group who seek out each other’s messages over time. Therefore, contagion of cyberhate information flows is contained and unlikely to spread widely beyond such groups
Defining hate crime
> Hatred Motivation Model (racial animus model)
Mainly adopted in Europe
In law prejudice and hatred prescribed
Law in England & Wales specifies ‘identity based hostility’
Suspect must demonstrate or be (partly) motivated by identity based hostility
Verbal expression of hostility in the moment or a previous history
Model leaves out selection of victim based on vulnerability due to identity
E.g. disabled victim chosen as a theft target as they may be less able to defend themselves, not because of any hatred held by the perpetrator
> Group Selection Model
Mainly used in United States
No prejudice or hatred prescribed in law
No expression of identity based hostility needed
Suspect must simply “intentionally select” their victim “because of” their belonging to a protected group
This model can capture more ‘hate crime’ cases
Difficult to prove selection of victim was based on hate and not something else
Defining hate crime
NPCC & CPS ‘victim’ centred definitions
Hate motivation
“Hate crimes and incidents are taken to mean any crime or incident where the perpetrator’s hostility or prejudice against an identifiable group of people is a factor in determining who is victimised”
Hate incident
“Any non-crime incident which is perceived, by the victim or any other person, to be motivated by a hostility or prejudice based on a victim’s disability, race, religion, sexual orientation or transgender identity.”
Hate crime
“A hate crime is a criminal offence which is perceived, by the victim or any other person, to be motivated by a hostility or prejudice based on a victim’s disability, race, religion, sexual orientation or transgender identity.”
Incitement to hatred
“Incitement to racial hatred, incitement to religious hatred and incitement to hatred on the grounds of sexual orientation are all criminal offences”
Overlap with gang crimes, terrorism, political violence and war.
Legislation in England and Wales
Hate Crimes
Crime and Disorder Act 1998† (as amended by Anti-terrorism, Crime & Security Act 2001)
Racially or religiously aggravated assault, criminal damage, public order offences, and harassment
† Not all crimes covered (sexual offences, burglary, robbery, fraud and forgery, homicide etc.).
Enhanced Sentencing
Criminal Justice Act 2003
Sentence enhancement where the following are aggravating factors
Hostility towards race and religion (where the offence is not covered by CDA 1998)
Hostility towards sexual orientation and disability
Sentencing and Punishing Offenders Act 2012 (amending CJA 2003)
Hostility towards transgender identity
Stirring up hatred
Public Order Act 1986
Incitement to hatred against persons on racial grounds (threatening, abusive or insulting)
Racial and Religious Hatred Act 2006 (amending POA 1986)
Incitement to hatred against persons on religious grounds (threatening only)
Immigration and Criminal Justice Act 2008 (amending POA 1986)
Incitement to hatred against persons on grounds of sexual orientation (threatening only)
