RiskTrack: a new approach for risk assessment of radicalisation based on social media data

Proceedings of the Workshop on Affective Computing and Context Awareness in Ambient Intelligence (AfCAI 2016) Murcia, Spain, November 24-25, 2016The RiskTrack project aims to help in the prevention of terrorism through the identi cation of online radicalisation. In line with the European Union priorities in this matter, this project has been designed to identify and tackle the indicators that raise a red ag about which individuals or communities are being radicalised and recruited to commit violent acts of terrorism. Therefore, the main goals of this project will be twofold: On the one hand, it is needed to identify the main features and characteristics that can be used to evaluate a risk situation, to do that a risk assessment methodology studying how to detect signs of radicalisation (e.g., use of language, behavioural patterns in social networks...) will be designed. On the other hand, these features will be tested and analysed using advanced data mining methods, knowledge representation (semantic and ontology engineering) and multilingual technologies. The innovative aspect of this project is to not offer just a methodology on risk assessment, but also a tool that is build based on this methodology, so that the prosecutors, judges, law enforcement and other actors can obtain a short term tangible results.This work has been supported by the RiskTrack project: "Tracking tool based on social media for risk assessment on radicalisation" under the EU Justice Action Grant: JUST-2015-JCOO-AG-72318

A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era

Journal pre proofBig data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from more than 400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio

Modeling the Process of School Shooters Radicalization (Russian Case)

Research on radicalization became relevant to the study of terrorism and violent extremism just two decades ago. The accumulated empirical data on terrorism have led researchers and experts to understand that radicalization is a predictor of violent actions by terrorists, violent extremists, and lone actors. Violent incidents committed by school shooters are not terrorist crimes, but there is good reasons for inclusion as terrorist crimes since they have similar mechanisms. The article aims to create a conceptual model of school-shooter radicalization and determine the distinguishing features of the process. The paper presents a theoretical and methodological base of content analysis concepts, political models, and terrorist radicalization on the different levels of study. Based on the content analysis results, we identify the significant gaps in the research field, consider the radicalization phenomenon in detail, substantiated the qualitative aspects of the school shooters radicalization, and propose a conceptual scheme. Psychological, behavioral, cognitive aspects of the school shooters radicalization reflect a holistic picture of the relationship between the process phases and changes in the parameters of the object’s state. The aspects of radicalization and this phenomenon's qualitative properties are interpreted as the determinants of the conceptual model. The model includes five stages, each of which is considered to be one of the components for the formation and acceptance of the idea of a violent way to solve a problem, but certainly do not act individually as the only component that leads to the actual implementation of the incident of a school shooting. An in-depth study of online social connections and warning signs, mobilization factors, behavioral trajectories, and imitation mechanisms can help scientists understand why school shooters are increasingly motivated to use violent means to achieve personal goals. We have outlined the possibilities and prospects of the model's application and directions for future research

Graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors, A

2017 Summer.Includes bibliographical references.The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms

Method for Detecting Far-Right Extremist Communities on Social Media

Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the subject area of far-right extremist communities, we identified three groups of factors that influence the effectiveness of the research work. These are a group of theoretical, methodological, and instrumental factors. We developed and implemented a unique algorithm of calendar-correlation analysis (CCA) to search for specific online communities. We based CCA on a hybrid calendar correlation approach identifying potential far-right communities by characteristic changes in group activity around key dates of events that are historically crucial to those communities. The developed software module includes several functions designed to automatically search, process, and analyze social media data. In the current paper we present a process diagram showing CCA’s mechanism of operation and its relationship to elements of automated search software. Furthermore, we outline the limiting factors of the developed algorithm. The algorithm was tested on data from the Russian social network VKontakte. Two experimental data sets were formed: 259 far-right communities and the 49 most popular (not far-right) communities. In both cases, we calculated the type II error for two mutually exclusive hypotheses—far-right affiliation and no affiliation. Accordingly, for the first sample, Я = 0.81. For the second sample, Я = 0.02. The presented CCA algorithm was more effective at identifying far-right communities belonging to the alt-right and Nazi ideologies compared to the neo-pagan or manosphere communities. We expect that the CCA algorithm can be effectively used to identify other movements within far-right extremist communities when an appropriate foundation of expert knowledge is provided to the algorithm

Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups

The identity-extremism nexus in virtual groups: the impact of online group alignment on radicalisation towards violence

Can would-be perpetrators of extreme violence be reliably identified by the linguistic traces they unintentionally leave behind in their online communications? Previous research has shown that identity fusion, a visceral feeling of oneness with the group, can motivate violent self-sacrifice when the in-group is threatened. This thesis tests the hypothesis that a higher degree of identify fusion in online groups, coupled with a range of mediating and moderating variables, increases the likelihood of individuals carrying out violent pro-group action. To date, there has been no systematic analysis of the relationship between identity dynamics within virtual communities and radicalisation towards violence. This research project seeks to fill this gap by investigating the relationship between identity fusion in the digital arena and violent extremist activities in the real world. Using a mixed methods approach, the project combines quantitative natural language processing (NLP) analysis with quantitative text analysis and digital ethnographic research. The first part of the thesis presents a new coding scheme that traces the narratives and linguistic markers found across the written statements published by terrorists prior to launching an attack. A total of 4,000 pages by 15 authors, on a spectrum from violent terrorist to non-violent political manifestos, were analysed in a comparative manifesto analysis and tested via intercoder reliability (ICR) analysis. The statistical and ethnographic findings indicate that linguistic proxies for identity fusion and other relevant variables, such as violence-condoning group norms and dehumanising vocabulary, can be reliably identified and are significantly more prevalent in the documents of would-be terrorists. A weighted score, the so-called “Violence Risk Index”, was created, drawing on the statistical findings of the manifesto analysis. This new language-based violence risk assessment framework was then applied to eight online groups varying in their degree of extreme ideologies, verbal commitment to violence, and real-world links to terrorist activities. A total of over one million messages were collected from online forums and messaging apps and investigated with a view to determining the scale and nature of violence-predicting narratives and language in each of the groups. The calculated Violence Risk Index for each group mirrored the level of real-world engagement in extreme violence and terrorism, thus providing preliminary evidence that the assessment framework offers an accurate estimation of violence risk associated with online groups. This thesis argues that psychologically grounded linguistic markers are a more reliable predictor of extreme violence than taking violent threats at face value. While this project’s findings should be treated with caution, they may contribute to complementing and improving existing early warning systems used by security and intelligence services

A social network of crime : A review of the use of social networks for crime and the detection of crime

Social media is used to commit and detect crimes. With automated methods, it is possible to scale both crime and detection of crime to a large number of people. The ability of criminals to reach large numbers of people has made this area subject to frequent study, and consequently, there have been several surveys that have reviewed specific crimes committed on social platforms. Until now, there has not been a review article that considers all types of crimes on social media, their similarity as well as their detection. The demonstration of similarity between crimes and their detection methods allows for the transfer of techniques and data between domains. This survey, therefore, seeks to document the crimes that have been committed on social media, and demonstrate their similarity through a taxonomy of crimes. Also, this survey documents publicly available datasets. Finally, this survey provides suggestions for further research in this field

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks