State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Detección de mentiras: estado de la cuestión y perspectivas de futuro

[EN]Background: Deception detection has been a longstanding concern throughout human history. It has also interested scientists, who have explored psychological and behavioral differences between liars and truth tellers, as well as ways to improve detection accuracy. Method: In recent years, substantial advances have been made in the fi eld. Some of these advances are briefly reviewed in the current article. Results: A description is provided of (a) research and contemporary theories on how people (try to) detect deception; (b) recent advances on strategic interviewing to detect deception; (c) the integrative findings of recent meta-analyses on systematic verbal lie detection approaches; and (d) several important aspects concerning psychophysiological detection of deception. Also, some emerging trends and research needs for the future are outlined at the end of the article. Conclusions: Deception detection research is a lively and dynamic area of applied psychology that has experienced substantial developments in recent times. Much (though not all) of these research efforts have focused on developing empirically-based lie-detection procedures to be used by practitioners (e.g., the police) in applied settings. A number of new topics are just starting to be examined. These novel research avenues will surely yield interesting new findings in the future.[ES]Antecedentes: la detección de mentiras ha interesado a la humanidad a lo largo de la historia. También a los científicos, quienes han explorado diferencias psicológicas y conductuales al mentir vs. decir la verdad, así como modos de aumentar la precisión de la detección. Método: recientemente se han hecho avances sustanciales en esta área. En el presente artículo se revisan algunos de ellos. Resultados: se describen (a) las investigaciones y teorías contemporáneas sobre cómo la gente (intenta) detecta(r) mentiras; (b) los avances en procedimientos estratégicos de entrevista para detectar mentiras; (c) los hallazgos de meta-análisis recientes sobre aproximaciones sistemáticas para la detección verbal del engaño; y (d) algunos aspectos importantes de la detección psicofisiológica de la mentira. Al final del artículo se esbozan algunas tendencias emergentes y necesidades de investigación de cara al futuro. Conclusiones: el área de investigación de la detección de mentiras ha experimentado grandes desarrollos en tiempos recientes. A menudo (aunque no siempre) se ha centrado en desarrollar procedimientos de detección de mentiras de base empírica para su utilización en contextos aplicados (p. ej., por la policía). Algunas vías de indagación novedosas están empezando a explorar temas nuevos y, seguramente, darán lugar a futuros hallazgos nuevos e interesantes

The Effects of Media Differences and Expertise on Deception Detection Accuracy

Deception is a common part of everyday communication. Most of the research on deception has focused on face-to-face communication, but today most communication is mediated, taking the form of email, texting, and videoconferencing. We have a limited understanding of the relationship between media and deception detection. Computer-mediated communication is also a staple of many business practices, as is the case for modern human resource management (HRM). Deception in HRM can have long-lasting effects in organizations, if recruiting leads to hiring the wrong people. However, people are not very good at detecting deception, regardless of the media used. Further, individual differences, such as expertise, do not seem to matter in detection efforts. Despite their experience and training, experts are no better than novices at detecting deception. So, what is the role played by media in deception detection success, and does that success vary by experience? Comparing HR experts to students on a deception detection task, we found that experts performed no better than novices. Further, all participants were more successful at detection when viewing audiovisual interview segments than when listening to audio only segments

Real-world, high-stakes deceptive speech: Theoretical validation and an examination of its potential for detection automation

The study of deception and the theories which have been developed have relied heavily on laboratory experiments, in controlled environments, utilizing American college students, participating in mock scenarios. The goal of this study was to validate previous deception research in a real-world high-stakes environment. An additional focus of this study was the development of procedures to process data (e.g. video or audio recordings) from real-world environments in such a manner that behavioral measures can be extracted and analyzed. This study utilized previously confirmed speech cues and constructs to deception in an attempt to validate a leading deception theory, Interpersonal Deception Theory (IDT). Several measures and constructs, utilized and validated in existing research, were explored and validated in this study. The data analyzed came from an adjudicated real-world high-stakes criminal case in which the subject was sentenced in federal court to 470 years in prison for creating child pornography, rape, sexual exploitation of children, child sexual assault and kidnapping; a crime spree that spanned over a five years and four states. The results did validate IDT with mixed results on individual measures and their constructs. The exploratory nature of the study, the volume of data, and the numerous methods of analysis used generated many possibilities for future research

Using Data Analytics to Filter Insincere Posts from Online Social Networks A case study: Quora Insincere Questions

The internet in general and Online Social Networks (OSNs) in particular continue to play a significant role in our life where information is massively uploaded and exchanged. With such high importance and attention, abuses of such media of communication for different purposes are common. Driven by goals such as marketing and financial gains, some users use OSNs to post their misleading or insincere content. In this context, we utilized a real-world dataset posted by Quora in Kaggle.com to evaluate different mechanisms and algorithms to filter insincere and spam contents. We evaluated different preprocessing and analysis models. Moreover, we analyzed the cognitive efforts users made in writing their posts and whether that can improve the prediction accuracy. We reported the best models in terms of insincerity prediction accuracy

The Viability of Using Rapid Judgments as a Method of Deception Detection

Rapid Judgments (RJs) are quick assessments based on indirect verbal and nonverbal cues that are known to be associated with deception. RJs are advantageous because they eliminate the need for expensive detection equipment and only require minimal training for coders with relatively accurate judgments. Results of testing on two different datasets showed that trained coders were reliably making RJs after watching both long and short interaction segments but their judgments were not more accurate than the expert interviewers. The RJs did not discriminate between truth and deception as hypothesized. This raises more questions about the conditions under which making RJs from verbal and nonverbal cues achieves accurate detection of veracity.18 month embargo; published online: 25 January 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Cyber-crime Science = Crime Science + Information Security

Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions