Slide 1
Social network analysis in violence reduction
Slide 2
Area: Violence regulation.
Hypothesis: There is a relationship between group structure and the outcome of an aggressive incident.
Slide 3
The causes of violence have been examined for a long time
As research suggests, it may be caused by:
1.
release of aggressive energy
2.
the failure of parental control
3.
or an adaptive function that facilitates sexual selection.
However, there is still little known about how aggression leads to violence, what can be a predictor of a violent or non-violent
outcome of an aggressive incident and how it can be prevented.
The literature review has shown that most psychological theories tend to categorise the causes of violence into person factors
(traits, sex, values, attitudes, beliefs, goals and scripts) and situational factors (aggressive cues, provocation, frustration, pain and
discomfort, drugs/alcohol, incentives).
De Waal suggested that it is more important to understand how aggression is transitioned into violence within a particular social
context.
In animal research the studies are focused much on the relationship between the individuals who are directly involved in the
aggressive incident or violence.
However, other researchers suggest that third-party interventions may play the key role in violence regulation.
Slide 4
Previous research has shown that at a behavioural level, third parties serve to inhibit, rather than facilitate, the likelihood of
violence, and are even more likely to do so as the group size increases.
Much more is known about conflict resolution in animal world rather than in human society: there are practical difficulties in
investigation of human violence, as it emerges in real time. That is why CCTV is a great opportunity to learn more about
aggressive behaviour and violence in human groups in real time.
Social network analysis is the mapping and measuring of flows and relationships between interacting units.
Social network analysis is used to explore group structures, relationships between the actors in these groups, influence and
potential of human actors, communication flows in organisations, conflict management in animal behaviour, and organisations in
animal populations.
One of the most recent studies on social network analysis in context of terrorism and Politics has shown that SNA is important for
identification of actors who are more likely to be engaged in terrorist activities, for prediction of which group structures are more
vulnerable, and for examining how these groups can be destabilized.
Network-oriented research has highlighted three dimensions of network effects on crime and deviance:
(1) a social group’s level of cohesion,
(2) a person’s position within this network
(3) the levels of social interactions within these networks.
Another study investigated how the ties between the actors and their nature are associated with the group’s radicalization, and
engagement in violent activities.
However, little research has examined the role of social network analysis in regulation of violence in real time.
To explore the role of physical contact in violence reduction, we will conduct a behavioural analysis of footage from CCTV of
public places, and will construct and analyse social networks to explore the relationship between group structure and the outcome
of an aggressive incident on the footage.
Slide 5 -- Method Data
42 CCTV clips lasting between 1 and 8 minutes recorded in public drinking places in a city in North West England.
The clips conform specific criteria:

The footage was continuous

in each clip there is a fight or argument between two people;

there are at least two third parties;

footage is continuous;

incidents do not involve police officers, door security personnel or community wardens;

all the clips contain a sequence of at least 20 behaviours by the opponents and third parties.
Slide 6-- Coding
1.
Identifying the perpetrator, the victim, and the third parties
2.
After pre-analysing the video, all the actions of the actors were divided by type into such behaviours:
pushing (applying pressure against other person, mainly in chest, back, or shoulders), patting (light touch with an open
hand, stroke or tap, mainly in shoulder, chest, arm, or back),
grasping (gripping with one or two hands, capturing opponent’s arms, hands, neck, or clothes),
holding (having and keeping in one’s grasp, hugging, mainly using one or two arms, holding from the back, from the
front, holding hands, arms, shoulders, legs, neck), kicking (striking the opponent with feet), punching (hitting an
opponent with a fist in head (including neck), and in body) and kissing.
.
3.
Coding the behavioural contribution of each actor, victim, and where the behaviour fell in the sequence of acts
4.
The behavioural contribution of each actor, victim, and where the behaviour fell in the sequence of acts will coded using
such approach: behaviours by the same individual will be treated as separate only when there was a 2 second or more gap
between the acts.
5.
We will construct a sequence of behavioural codes for each incident. To capture the contribution of each bystander, a
unique numeric identity code will be given to each actor in each clip.
To test reliability of this coding, we will first code 20% of the clips. After the discussion of this coding we will work
separately on each clip and then compare and analyse the results of the coding.
Slide 7 -- Social network analysis
For building and mapping the social networks the UCINet software will be used. We will construct networks for each group of
people on each CCTV clip. The coding output from the previous stage will be used in the network construction. First 20% of the
clips will be first mapped to check the reliability of the data used.
On the UCINet output, all of the actors will be displayed as nodes (dots),
and their relations (shown by touching in each clip) will be displayed as ties (lines).
The network strength and strength of relationships between the actors in groups will be depicted in the tie thickness, and will be
measured by the number and duration of physical contacts between the actors.
We will evaluate the location of the actors in each network, which means finding the centrality of a node (it is measured by the
number of people attached to the person, their interaction).
Slide 8 --- Results
Due to exploratory nature of the analysis, the results of the study are not yet predictable.
In general, descriptive statistics analysis may show dependence of the outcome of the incident on the group size, on the amount of
touch and group structure.
Social network analysis may show the strength of the relationship between the actors through the amount and duration of physical
contact.
It may also show that stronger relationships lead to a non-violent outcome.
More dense networks may show more third-party intervention- the closer they are, the more they intervene. In opposite, less dense
networks may show less third-party intervention (this can happen in groups where people are less close to each other or they are
strangers to each other).
Therefore, it is predicted that there will be a difference between the outcomes of each incident depending on the group structure
and the amount of touch.
That means, after comparing the maps of all the group structures in each clip, we may find the evidence that a particular structure
and amount of touch may lead to a non-violent outcome.
Limitations:

quality of the clips ----- clearly touches and gestures

measuring distance ----- show extra information

alcohol ----- public drinking places

interpretation of touch ----- Some kinds of physical contact can be interpreted as both touch and gesture (i.e. blocking
with elbows, putting an index finger on someone’s body)

gender differences ----- our sample did not allow us to compare male and female actors.
Slide 9