By Ronald V. Clarke and Phyllis A. Schultze

This guide, one of the Problem-Solving Tools Series, summarizes knowledge about information gathering and analysis techniques that might assist police at any of the four main stages of a problem-oriented project: scanning, analysis, response, and assessment. This tool takes the mystery out of conducting research on problems by helping the user to define their problem, use technology to conduct Internet searches, get advice from experts, visit libraries, and evaluate their primary sources of information. The guide offers helpful hints to understanding and identifying responses to problems based on the research gathered.

Problem-oriented policing focuses, one-by-one, on specific problems of crime and disorder with the intention of identifying and altering the particular factors giving rise to each problem. The problems addressed in problem-oriented policing tend not to be confined to just a few police jurisdictions, but are more widely experienced. It is therefore likely that some other agency has tried to solve the kind of problem that you are dealing with now. Or perhaps some researcher has studied a similar problem and learned things that might be useful to your work. You could save yourself a lot of time and effort by finding out what they did and why. In particular, you can learn which responses seemed to be effective and which were not. So long as they made available a written report of their work, this guide will help you discover what they did. Having found out what others have done, you cannot simply copy what they did. You will have to adapt any successful responses they used to your own situation. This guide does not tell you how to analyze and understand your own problem.† It will only help you to profit from the work of those who have dealt with a similar problem. It is designed to take you as quickly as possible to the information you need and to help you evaluate and make the best use of this information. In doing this, it assumes: • You are familiar with problem-oriented policing. The guide assumes that a problem-solving model, such as SARA (Scanning, Analysis, Response, Assessment), is guiding your project. The guide will assist you at the Analysis and Response stages by pointing you to the possible cause of the problem you are tackling and to the ways you might respond. • You are willing to consider new responses to the problem. Rarely does police enforcement alone solve a persisting problem. To bring a lasting improvement, it is almost always necessary to modify the conditions giving rise to the problem, such as a lack of security or surveillance. Whatever measures you adopt must be carefully matched to the nature of your problem. Many of the measures are likely to be outside your experience and, indeed, that of most police officers. So, you need to learn about the ones that have been successfully used before in dealing with the kind of problem you face. While it is not usually recommended that a police agency blindly adopt another agency's responses to a problem, neither is it a good idea to be blind to what others have done. The key is to understand whether lessons learned elsewhere would apply under the conditions that exist for your problem. • You have limited time. The guide assumes that you have limited time to research best practice and that you want results quickly. You are not writing an academic paper where you might be faulted for missing a particular article or book. You are simply trying to find information that will help you with the practical task of dealing with your problem. For this reason, the guide does not provide a comprehensive description of all information sources, whether on the Internet † or in libraries. † Comprehensive descriptions are provided by Benamati et al. (1998) and Nelson (1997). Rather, it is intended to help you find two main categories of information relevant to your task: (1) articles by researchers who have studied the problem you are facing and, (2) reports of police projects dealing with the problem. The first category of information will help you understand the factors giving rise to your problem; the second will help you find effective responses. Later in the project, you might wish • You have Internet access. Nowadays, it is very difficult to research a problem without having access to the Internet. The guide assumes that you have this access and that you are familiar with searching for information on the Internet. (Indeed, you might have found this guide on the Internet.) The computer you use will need † a copy of Adobe Reader, which allows you to read and download articles in portable document format (.pdf) that you find at websites on the Internet. Unless your computer has a high speed connection, this process of visiting websites and reading and downloading material can be slow and frustrating. Most computers in libraries have high speed connections and you can usually pay to obtain print copies of the material you have downloaded

Problem-Oriented Guides for Police Problem-Solving Tools Series No. 2

Washington, DC: U.S. Department of Justice Office of Community Oriented Policing Services, 2005. 72p.

By The European Institute for Gender Equality (EIGE)

Introduction The European Institute for Gender Equality (EIGE) has developed 13 indicators on intimate partner violence and domestic violence to guide the data collection efforts of the police and the justice sector. EIGE’s indicators support EU Member States to measure intimate partner violence and domestic violence and to assess the progress made to combat and monitor these forms of violence. Ultimately, the indicators enhance the comparability of national administrative data on intimate partner and domestic violence in alignment with the minimum requirements of Directive (EU) 2024/1385 on combating violence against women and domestic violence; Directive 2012/29/EU on establishing minimum standards on the rights, support and protection of victims of crime; and the Council of Europe convention on preventing and combating violence against women and domestic violence (the Istanbul Convention). Between 2018 and 2020, EIGE used the 13 indicators to measure intimate partner violence across the EU. Between 2021 and 2022, EIGE revised and simplified the 13 indicators, the data collection tool used to populate the indicators and the data collection methodology. Between 2023 and 2024, EIGE conducted a new EU-wide data collection exercise to measure intimate partner violence and domestic violence using the updated indicators, data collection tool and methodology. This document describes the methodological approach for the 2023–2024 data collection exercise. The structure of this report is as follows. • The following section provides an overview of EIGE’s data collection exercise, indicators and data collection tool and provides general methodological details. • The remaining sections provide indicator-specific details on the methodological approach used to collect data on EIGE’s 13 indicators on intimate partner violence and domestic violence. • The annexes present mapping tables with further information on the availability and comparability of the data collected, and the sources of the data.

Luxembourg: Publications Office of the European Union, 2025, 152p.

By Felix Soldner, Bennett Kleinberg, Shane D. Johnson

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26–69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19–74.17%), or with data-origin (84.44–86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78–88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy. 

PLoS ONE 17(12): 2022