Understanding and preventing the advertisement and sale of illicit drugs to young people through social media: A multidisciplinary scoping review

ISSUES: The sale of illicit drugs online has expanded to mainstream social media apps. These platforms provide access to a wide audience, especially children and adolescents. Research is in its infancy and scattered due to the multidisciplinary aspects of the phenomena. APPROACH: We present a multidisciplinary systematic scoping review on the advertisement and sale of illicit drugs to young people. Peer-reviewed studies written in English, Spanish and French were searched for the period 2015 to 2022. We extracted data on users, drugs studied, rate of posts, terminology used and study methodology. KEY FINDINGS: A total of 56 peer-reviewed papers were included. The analysis of these highlights the variety of drugs advertised and platforms used to do so. Various methodological designs were considered. Approaches to detecting illicit content were the focus of many studies as algorithms move from detecting drug-related keywords to drug selling behaviour. We found that on average, for the studies reviewed, 13 in 100 social media posts advertise illicit drugs. However, popular platforms used by adolescents are rarely studied. IMPLICATIONS: Promotional content is increasing in sophistication to appeal to young people, shifting towards healthy, glamourous and seemingly legal depictions of drugs. Greater inter-disciplinary collaboration between computational and qualitative approaches are needed to comprehensively study the sale and advertisement of illegal drugs on social media across different platforms. This requires coordinated action from researchers, policy makers and service providers

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

Analyzing and Detecting Malicious Activities in Emerging Communication Platforms

Benefiting from innovatory techniques, two communication platforms (online social networking (OSN) platforms and smartphone platforms) have emerged and been widely used in the last few years. However, cybercriminals have also utilized these two emerging platforms to launch malicious activities such as sending spam, spreading malware, hosting botnet command and control (C&C) channels, and performing other illicit activities. All these malicious activities may cause significant economic loss to our society and even threaten national security. Thus, great efforts are indeed needed to mitigate malicious activities on these advanced communication platforms. The goal of this research is to make a deep analysis of malicious activities on OSN and smartphone platforms, and to develop effective and efficient defense approaches against those malicious activities. Firstly, this dissertation performs an empirical analysis of the cyber criminal ecosystem on a large-scale online social networking website space. Secondly, through reverse engineering OSN spammers’ tastes (their preferred targets to spam), this dissertation provides guidelines for building more effective social honeypots on the online social networking platforms, and generates new insights to defend against OSN spammers. Thirdly, this dissertation shows a comprehensive empirical study on analyzing the market-level and network-level behaviors of the Android malware ecosystem. Lastly, by grouping the common program logic among malware families, this dissertation designs an effective system to automatically detect Android malware

Adolescent perceptions of addiction: a mixed-methods exploration of Instagram hashtags and adolescent interviews

Addictive disorders are a public health crisis that affects our society by draining our workforce, health care, judicial, education, and law enforcement, resources. Adolescents are particularly susceptible to social influence - for better and for worse - and addiction. Through social media, today’s youth experience a whole new way of communicating. Not enough is known about adolescent perceptions of addiction, and messages of addiction they are exposed to on social media. Social Learning Theory and the Learning Theory of Addiction provided the framework for understanding how adolescents are at risk for developing unhealthy practices that create numerous psychological, social and physical problems in adulthood. Concurrent, mixed-methods, were used to explore adolescent perceptions of addiction and Instagram hashtags related to addiction. A content analysis of Instagram hashtags related to addiction and interview data from 11 adolescents aged 16-18 from a Students Against Destructive Decisions (SADD) club in New Jersey was collected and analyzed. The sample for phase one of this study was comprised of 819,155 Instagram posts, hashtagged #addiction, #recovery, #alcohol, and #drugs, collected on 5 dates over a month. Phase 2, adolescent interviews, included open-ended and Addiction Belief Survey (ABS) questions. The study’s findings led to the conclusion that the adolescents interviewed have uncertain, and at times prejudicial, understandings of addiction. They see social media as potentially helpful in the fight against addiction and feel protected from negative messages of addiction by a strong circle of friends and family. Addiction related posts on Instagram, though littered with unhealthy messages, reflect the belief that addiction is recoverable and avoidable through social support. Adolescent perceptions of addiction align with those expressed on Instagram in both healthy and unhealthy ways. Beliefs of addiction expressed by adolescents and on Instagram reflect recent findings in the scientific literature on the nature of addiction, stigma, social support, and wellness. Study recommendations include for school and government leadership to take a multi-pronged, community based, approach in supporting adolescents. Future research should focus on social media support for adolescents and adolescent social learning of addiction. Secondary school curricula and interventions that include social media should be created and improved using design-based research because it allows for evidence-based improvement

Making Sense of Online Public Health Debates with Visual Analytics Systems

Online debates occur frequently and on a wide variety of topics. Particularly, online debates about various public health topics (e.g., vaccines, statins, cannabis, dieting plans) are prevalent in today’s society. These debates are important because of the real-world implications they can have on public health. Therefore, it is important for public health stakeholders (i.e., those with a vested interest in public health) and the general public to have the ability to make sense of these debates quickly and effectively. This dissertation investigates ways of enabling sense-making of these debates with the use of visual analytics systems (VASes). VASes are computational tools that integrate data analytics (e.g., webometrics or natural language processing), data visualization, and human-data interaction. This dissertation consists of three stages. In the first stage, I describe the design and development of a novel VAS, called VINCENT (VIsual aNalytiCs systEm for investigating the online vacciNe debaTe), for making sense of the online vaccine debate. VINCENT helps users to make sense of data (i.e., online presence, geographic location, sentiments, and focus) from a collection of vaccine focused websites. In the second stage, I discuss the results of a user study of VINCENT. Participants in the study were asked to complete a set of ten sense-making tasks that required investigating a provided set of websites. Based on the positive outcomes of the study, in stage three of the dissertation I generalize the findings from the first two stages and present a framework called ODIN (Online Debate entIty aNalyzer). This framework consists of various attributes that are important to consider when analyzing online public health debates and provides methods of collecting and analyzing that data. Overall, this dissertation provides visual analytics researchers an in-depth analysis on the considerations and challenges for creating VASes to make sense of online public health debates

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges