Identifying a Criminal's Network of Trust

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (Presented at Third International Workshop on Complex Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27, November 2014

Mapping networks of influence: tracking Twitter conversations through time and space

The increasing use of social media around global news events, such as the London Olympics in 2012, raises questions for international broadcasters about how to engage with users via social media in order to best achieve their individual missions. Twitter is a highly diverse social network whose conversations are multi-directional involving individual users, political and cultural actors, athletes and a range of media professionals. In so doing, users form networks of influence via their interactions affecting the ways that information is shared about specific global events. This article attempts to understand how networks of influence are formed among Twitter users, and the relative influence of global news media organisations and information providers in the Twittersphere during such global news events. We build an analysis around a set of tweets collected during the 2012 London Olympics. To understand how different users influence the conversations across Twitter, we compare three types of accounts: those belonging to a number of well-known athletes, those belonging to some well-known commentators employed by the BBC, and a number of corporate accounts belonging to the BBC World Service and the official London Twitter account. We look at the data from two perspectives. First, to understand the structure of the social groupings formed among Twitter users, we use a network analysis to model social groupings in the Twittersphere across time and space. Second, to assess the influence of individual tweets, we investigate the ageing factor of tweets, which measures how long users continue to interact with a particular tweet after it is originally posted. We consider what the profile of particular tweets from corporate and athletes’ accounts can tell us about how networks of influence are forged and maintained. We use these analyses to answer the questions: How do different types of accounts help shape the social networks? and, What determines the level and type of influence of a particular account

How to Hide One's Relationships from Link Prediction Algorithms

Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.M.W. was supported by the Polish National Science Centre grant 2015/17/N/ST6/03686. T.P.M. was supported by the Polish National Science Centre grants 2016/23/B/ST6/03599 and 2014/13/B/ST6/01807 (for this and the previous versions of this article, respectively). Y.V. and K.Z. were supported by ARO MURI (grant #W911NF1810208). Y.V. was also supported by the U.S. National Science Foundation (CAREER award IIS- 1905558 and grant IIS-1526860). E.M. acknowledges funding by Ministerio de Economa y Competitividad (Spain) through grant FIS2016-78904-C3-3-P

Central Actor Identification of Crime Group using Semantic Social Network Analysis

The Police as law enforcers who authorize in terms of social protection are expected to do both the prevention and investigation efforts also the settlement of criminal cases that occurred in the society. This research can help police to identify the main actor faster and leads to solving crime-cases. The use of overall centrality is very helpful in determining the main actors from other centrality measures. The purpose of this research is to identify the central actor of crimes done by several people. Semantic Social Network Analysis is used to perform central actor identification using five centrality measurements, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and overall centrality. As for the relationship between actors, this research used social relation such as friendship, colleague, family, date or lover, and acquaintances. The relationship between actors is measured by first four centrality measures then accumulated by overall centrality to determine the main actor. The result showed 80.39% accuracy from 102 criminal cases collected with at least 3 actors involved in each case

Targeting the Most Harmful Co-Offenders in Denmark: a Social Network Analysis Approach

Abstract: Research Question: Is there a ‘power few’ individuals in Denmark who, through consistent co-offending, produce the highest frequency of crimes and the most harm to society amongst all co-offenders? Data: We analysed official statistics from the Police Crime Case Management System in Denmark on all 437,717 charges for violations of the Danish Criminal Code, the Illegal Substances Act and the Weapons Act, in which co-offender relationships were identified from 2007 to 2017, equal to 28% of the national total of all 1,554,943 such charges filed against both solo offenders and co-offenders in that time period. Methods: We cross-referenced charging records with crime harm values taken from the Danish Crime Harm Index to measure the severity of all offence types charged. A social network analysis (SNA) algorithm was applied to the data to test for centrality and identify key co-offenders. Findings: While 7.5% of the co-offending population accounted for 50% of crime volume, only 3.6% of the co-offenders accounted for 50% of total crime harm. The latter made up just 1.2% of the overall offender population in Denmark, but contributed 24% of overall harm. Social network analysis of how central that power few was in relation to other co-offenders suggests an even smaller cohort of co-offenders—the ‘power few of the power few’—who are disproportionality more connected to other co-offenders. Conclusions: The ‘power few’ phenomenon exists in co-offender networks, with a pronounced concentration of harm caused by a small number of co-offenders. The evidence suggests that targeting co-offenders based on social network analysis can enhance the harm potentially reduced by both investigations and crime prevention strategies

Understanding the Changing Cultural Value of the BBC World Service and the British Council

This project investigated the changing cultural value of the BBC World Service (WS) and the British Council (BC) and how their cultural value can be assessed and measured. For eight decades, these organisations have been the face and voice of Britain overseas. Our research found that their attraction and influence abroad remains strong, but is on the wane, reflecting the UK’s declining economic and political significance on the world stage. Among the key findings of our historical and contemporary research: Cultural value is the catalyst of all aspects of value at WS and BC, founded on their capacity to act as transcultural intermediaries, fostering international understanding, and setting benchmarks in global standards for journalism and cultural relations work. Cultural value is relational, never independent of political and economic value. It is perspectival: audiences trust the quality and credibility of outputs; high professional standards and prestige benefit staff; funders appreciate the diplomatic and soft power assets. Cultural value accrues slowly over time but can be quickly lost. Social media afford new ways of connecting, informing and engaging citizens at home and abroad. Our case studies analysing the uses of Twitter and Facebook by BC and WS around global media events underscore the so far limited role of social media in democratising participation and promoting intercultural dialogue. We developed an innovative, theoretically grounded and empirically informed Cultural Value Model (CVM). This is an innovative device for conceptualising, analysing and assessing value in a multidimensional, composite, visual way. The CVM is designed for planning, monitoring and evaluating projects and organisations over time, alongside existing performance indicators and impact measures. It is currently being tested and developed on further projects at WS and BC as well as at the Swedish Institute

Locating Central Actors in Co-offending Networks

Abstract—A co-offending network is a network of offenders who have committed crimes together. Recently different researches have shown that there is a fairly strong concept of network among offenders. Analyzing these networks can help law enforcement agencies in designing more effective strategies for crime prevention and reduction. One of the important tasks in co-offending network analysis is central actors identification. In this paper, firstly we introduce a data model, called unified crime data level and co-offending network mining level. Using this data model, we extract the co-offending network of five years real-world crime data. Then we apply different variations of centrality methods on the extracted network and discuss how key player identification and removal can help law enforcement agencies in policy making for crime reduction. I