Detecting crime patterns from Swahili newspapers using text mining

Matto, George; Mwangoka, Joseph

doi:10.1504/ijkedm.2017.086244

Cited by 6 publications

(6 citation statements)

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“…Still, automated methods for large-scale processing of free text known as text mining have been used for over 30 years to harvest information from unstructured text in many domains, particularly in biomedicine [ 12 - 15 ]. Recent attempts have aimed to utilize text mining to identify crime-related information from online media publications [ 16 , 17 ]. However, few efforts have been conducted in processing police reports [ 18 - 20 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

Karystianis¹,

Adily²,

Schofield³

et al. 2018

J Med Internet Res

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BackgroundVast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes.ObjectiveIn this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text.MethodsWe used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims.ResultsThe precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671).ConclusionsThe results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

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Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

Karystianis¹,

Adily²,

Schofield³

et al. 2018

J Med Internet Res

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“…Text mining has been used for more than 30 years to harvest information from unstructured text in many fields, particularly in biomedicine [ 15 - 20 ]. Recent efforts have sought to text mine crime-related information from online media publications [ 21 - 23 ], with limited attempts to process police reports [ 13 , 24 - 28 ]. Previous work extracted data on the names, narcotic drugs, and weapons with varying degrees of success (F1-score ranging from 46% to 81%) through named entity extraction [ 24 , 25 ] and police report classification of events as domestic violence or nondomestic violence related, using an unsupervised clustering technique that correctly classified 44% of the reports set aside for manual inspection [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study

Karystianis¹,

Adily²,

Schofield³

et al. 2019

J Med Internet Res

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BackgroundThe police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives.ObjectiveWe aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force.MethodsWe used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports.ResultsTesting our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events; 35.97%) contained victim injuries. “Emotional/verbal abuse” (33.46%; 117,488) was the most common abuse type, followed by “punching” (86,322 events; 24.58%) and “property damage” (22.27%; 78,203 events). “Bruising” was the most common form of injury sustained (51,455 events; 29.03%), with “cut/abrasion” (28.93%; 51,284 events) and “red marks/signs” (23.71%; 42,038 events) ranking second and third, respectively.ConclusionsThe results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status.

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“…To improve extraction of frequent itemsets studies have proposed the use of multiple minimum supports approach (see for example [26] and [28]). Liu et al [20] used this approach to mine rare itemsets through an Apriori-like algorithm called Multiple Support Apriori (MSApriori).…”

Section: Approaches That Use Multiple Minimum Supportsmentioning

confidence: 99%

“…One of such domains is crime patterns analysis, in which crime analysts mine association rules from crime datasets. Such rules help to discover patterns in criminal behaviour that can help to predict crime, anticipate criminal activities, and prevent further crimes [2], [3] and [26].…”

Section: Introductionmentioning

confidence: 99%

“…Comparative studies among such algorithms (see for example [4] and [5]) indicate FP-Growth as more efficient in terms of number of database scans, execution time and memory consumption. FP-Growth has also been used intensively in crime patterns mining [5], [6], and [26]. Classical FP-Growth mines frequent patterns by using a single user-specified minimum support (abbreviated as minsup) [7].…”

Section: Introductionmentioning

confidence: 99%

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Mining Frequent Patterns of Crime using FP-Growth with Multiple Minimum Supports based on Shannon Entropy

Matto¹,

Mwangoka²

2018

IJCA

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FP-Growth is one of the most effective and widely used association rules mining algorithm for discovering interesting relations between items in large datasets. Unfortunately, classical FP-Growth mines frequent patterns by using single user-defined minimum support threshold. This is not adequate for real life applications such as crime patterns mining. On one side, if minimum support is set too low, huge amount of crime patterns (including uninteresting patterns) may be generated, and on the other side, if it is set too high lots of interesting patterns (including seasonal patterns) may be lost. This paper proposes the use of Multiple Item Support (MIS) thresholds instead of single minimum support to tackle the challenge. We employ Shannon entropy method to develop an algorithm that obtains MIS values from crime datasets. The proposed approach is tested on different sizes of input data via a developed working prototype. Experimental results show that our suggested approach outperforms classical FP-Growth in terms of running time and memory use.

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Detecting crime patterns from Swahili newspapers using text mining

Cited by 6 publications

References 0 publications

Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study

Mining Frequent Patterns of Crime using FP-Growth with Multiple Minimum Supports based on Shannon Entropy

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