Special Issue "Predictive Analytics and Illicit Activities"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 5719

Special Issue Editor

Prof. Dr. Josiane Mothe
E-Mail Website
Guest Editor
Institut de Recherche en Informatique de Toulouse, Université de Toulouse, 31400 Toulouse, France
Interests: information retrieval and access; information mining and applied machine learning; weak signal and outlier detection

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the use of advanced techniques to mine and exploit heterogeneous digital data on illicit activities for descriptive or predictive analyses.

Digital technologies as well as digital information and communication networks facilitate illicit activities such as illicit financial flows, illicit art and drug markets, forbidden posts and comments on social media, and recruitment or incitement of illicit activities such as any form of radicalization, etc. New models, methods and tools need to be developed to prevent, detect or warn, and possibly mitigate or hinder these illicit actions.

These methods encompass machine learning or data mining models as well as visualization models to help in detecting or mitigating possible illicit activities.

This Special Issue aims to gather both research papers reporting new scientific results and technical papers reporting project results, demos/prototypes/tools.

This Special Issue invites submissions covering, but not limited to, the following topics:

- Crime detection and investigation;
- Risk analysis;
- Misinformation and misbehavior analysis and detection;
- Trend detection, analysis and tracking;
- Weak signal detection;
- Information / opinion / knowledge spread and modelling;
- Information quality in social network;
- Community detection, expertise and authority discovery;
- Social influence, recommendation and media;
- Behavior analysis in social networks;
- Sentiment analysis;
- Network visualization and modeling;
- Data mining and machine learning;
- Real-world case studies;
- Ongoing projects based on social media and/or social networks;
- Ethics.

Prof. Dr. Josiane Mothe
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crime detection and investigation
  • risk analysis
  • misinformation and misbehavior analysis and detection
  • trend detection, analysis and tracking
  • weak signal detection
  • information/opinion/knowledge spread and modelling
  • information quality in social network
  • community detection
  • expertise and authority discovery
  • social influence, recommendation and media
  • behavior analysis in social networks
  • sentiment analysis
  • network visualization and modeling
  • data mining and machine learning
  • real-world case studies
  • ongoing projects based on social media and/or social networks
  • ethics

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
Instruments and Tools to Identify Radical Textual Content
Information 2022, 13(4), 193; https://doi.org/10.3390/info13040193 - 12 Apr 2022
Viewed by 459
Abstract
The Internet and social networks are increasingly becoming a media of extremist propaganda. On homepages, in forums or chats, extremists spread their ideologies and world views, which are often contrary to the basic liberal democratic values of the European Union. It is not [...] Read more.
The Internet and social networks are increasingly becoming a media of extremist propaganda. On homepages, in forums or chats, extremists spread their ideologies and world views, which are often contrary to the basic liberal democratic values of the European Union. It is not uncommon that violence is used against those of different faiths, those who think differently, and members of social minorities. This paper presents a set of instruments and tools developed to help investigators to better address hybrid security threats, i.e., threats that combine physical and cyber attacks. These tools have been designed and developed to support security authorities in identifying extremist propaganda on the Internet and classifying it in terms of its degree of danger. This concerns both extremist content on freely accessible Internet pages and content in closed chats. We illustrate the functionalities of the tools through an example related to radicalisation detection; the data used here are just a few tweets, emails propaganda, and darknet posts. This work was supported by the EU granted PREVISION (Prediction and Visual Intelligence for Security Intelligence) project. Full article
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
Show Figures

Figure 1

Article
A Machine Learning-Based Method for Content Verification in the E-Commerce Domain
Information 2022, 13(3), 116; https://doi.org/10.3390/info13030116 - 26 Feb 2022
Viewed by 692
Abstract
Analysis of extreme-scale data is an emerging research topic; the explosion in available data raises the need for suitable content verification methods and tools to decrease the analysis and processing time of various applications. Personal data, for example, are a very valuable source [...] Read more.
Analysis of extreme-scale data is an emerging research topic; the explosion in available data raises the need for suitable content verification methods and tools to decrease the analysis and processing time of various applications. Personal data, for example, are a very valuable source of information for several purposes of analysis, such as marketing, billing and forensics. However, the extraction of such data (referred to as person instances in this study) is often faced with duplicate or similar entries about persons that are not easily detectable by the end users. In this light, the authors of this study present a machine learning- and deep learning-based approach in order to mitigate the problem of duplicate person instances. The main concept of this approach is to gather different types of information referring to persons, compare different person instances and predict whether they are similar or not. Using the Jaro algorithm for person attribute similarity calculation and by cross-examining the information available for person instances, recommendations can be provided to users regarding the similarity or not between two person instances. The degree of importance of each attribute was also examined, in order to gain a better insight with respect to the declared features that play a more important role. Full article
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
Show Figures

Figure 1

Article
Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators
Information 2021, 12(7), 274; https://doi.org/10.3390/info12070274 - 02 Jul 2021
Cited by 1 | Viewed by 1176
Abstract
Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in [...] Read more.
Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness. Full article
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
Show Figures

Figure 1

Article
An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions
Information 2021, 12(1), 34; https://doi.org/10.3390/info12010034 - 16 Jan 2021
Cited by 3 | Viewed by 1195
Abstract
Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. [...] Read more.
Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation. Full article
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
Show Figures

Figure 1

Review

Jump to: Research

Review
Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review
Information 2021, 12(11), 444; https://doi.org/10.3390/info12110444 - 27 Oct 2021
Cited by 1 | Viewed by 1193
Abstract
Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to [...] Read more.
Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end. Full article
(This article belongs to the Special Issue Predictive Analytics and Illicit Activities)
Show Figures

Figure 1

Back to TopTop