Community and key player detection for disrupting illicit drug supply networks in social media platforms – especially on Instagram

This thesis focuses on the pressing issue of illicit drug trafficking and its impact on public health and safety at a global level. With the advent of digital technologies and social media platforms, combating drug trafficking has become increasingly challenging for law enforcement and researchers alike. Among these platforms, Instagram, a popular photo and video-sharing social networking platform, has emerged as a prominent hub for drug trafficking activities. In this study, we delve into the effectiveness of community and key player detection algorithms in identifying and disrupting illicit drug supply networks on Instagram. To conduct our research, we collected real Instagram data spanning from June to August 2022. We examined several community detection algorithms, including Louvain, Newman-Girvan, Infomap, Label Propagation, and Hierarchical Clustering. Additionally, we explored key player algorithms, namely CDKPE, TopRank, K-core, and KPEI, the latter being a novel algorithm introduced in this study. Our objective was to assess the performance of these algorithms in accurately identifying and targeting drug trafficking networks. Our findings reveal that the Louvain and Newman-Girvan algorithms outperformed others in terms of community detection, demonstrating their effectiveness in identifying cohesive groups involved in drug trafficking on Instagram. In terms of key player detection, the CDKPE and KPEI algorithms emerged as the most effective, highlighting the individuals who play pivotal roles within these networks. These algorithms offer practical applications for law enforcement agencies seeking to disrupt drug trafficking operations on Instagram. By emphasizing the importance of leveraging advanced analytical tools, our study underscores the significance of combating drug trafficking on social media platforms

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

Unveiling the Potential of Knowledge-Prompted ChatGPT for Enhancing Drug Trafficking Detection on Social Media

Social media platforms such as Instagram and Twitter have emerged as critical channels for drug marketing and illegal sale. Detecting and labeling online illicit drug trafficking activities becomes important in addressing this issue. However, the effectiveness of conventional supervised learning methods in detecting drug trafficking heavily relies on having access to substantial amounts of labeled data, while data annotation is time-consuming and resource-intensive. Furthermore, these models often face challenges in accurately identifying trafficking activities when drug dealers use deceptive language and euphemisms to avoid detection. To overcome this limitation, we conduct the first systematic study on leveraging large language models (LLMs), such as ChatGPT, to detect illicit drug trafficking activities on social media. We propose an analytical framework to compose \emph{knowledge-informed prompts}, which serve as the interface that humans can interact with and use LLMs to perform the detection task. Additionally, we design a Monte Carlo dropout based prompt optimization method to further to improve performance and interpretability. Our experimental findings demonstrate that the proposed framework outperforms other baseline language models in terms of drug trafficking detection accuracy, showing a remarkable improvement of nearly 12\%. By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection. The implications of our research extend to social networks, emphasizing the importance of incorporating prior knowledge and scenario-based prompts into analytical tools to improve online security and public safety

Opioid-Related Content on Twitter and the Impact of COVID-19 Government Stimulus Distribution

The United States has continuously faced an opioid epidemic that has resulted in a severe loss of human life. The coronavirus pandemic began in December 2019 and affected many aspects of daily life. One result of this pandemic was government financial aid in the form of stimulus checks that were directly deposited into peoples’ bank accounts. This study aims to understand better the impact stimulus checks had on opioid overdose rates within America by using content collected from Twitter to gauge public opinion. The sample consisted of a stratified random sample of 600 overall tweets that contained at least one relevant search keyword. Keywords were common drug terms. Content analysis was used to determine emerging themes within the tweets to better understand how people discussed opioids. Results showed that there was no discussion by Twitter users that involved stimulus checks in conjunction with opioids

Impact of Social Media on Psychological Health: Challenges and Opportunities

Background and Aim: During the past few years, social networking has become very popular. Currently, there is a lack of information about the uses, benefits, and limitations of social media for health communication in society.Material and Methods: This paper will review some of challenges and opportunities to use social and their impact on psychological health. In this review we searched all valuable and relevant information considering the social media impact on psychological health. We referred to the information databases of Medline, PubMed, Scopus and Google scholar.Conclusion: Social media brings a new dimension to health care, offering a platform used by the public, patients, and health professionals to communicate about health issues with the possibility of potentially improving health outcomes. More study is required to establish whether social media has impact on mental health in both the short and long terms

Fleshing Out the Bones: Studying the Human Remains Trade with Tensorflow and Inception

There is an active trade in human remains facilitated by social media sites. In this paper we ask: can machine learning detect visual signals in photographs indicating that the human remains depicted are for sale? Do such signals even exist? This paper describes an experiment in using Tensorflow and the Google Inception-v3 model against a corpus of publicly available photographs collected from Instagram. Previous examination of the associated metadata for these photos detected patterns in the connectivity and rhetoric surrounding this ‘bone trade’, including several instances where ‘for sale’ seemed to be implied, though not explicitly stated. The present study looks for signals in the visual rhetoric of the images as detected by the computer and how these signals may intersect with the other data present

Penggunaan Media Sosial terhadap Penyalahgunaan Obat Terlarang pada Remaja

Abstract. There are many factors that can influence teens to abuse drugs and using illegal drugs. In this social media era, it is well known that the Internet has the greatest influence on its users, positively and negatively, and one of them is promoting drug abuse and the use of illegal drugs. The internet is a gateway to information. Open access to information makes it difficult to filter information received both positive and negative. In addition, the internet is also a tool for communication. Thus making communication much easier than before. Open access to information and communication channels can be used negatively by adolescents, especially in relation to drug abuse, research presented in this article has proven that social media such as Twitter, Facebook and others become a place for buying and selling and disseminating information about drugs and abuse drugsAbstract. Ada banyak faktor yang dapat mempengaruhi remaja untuk menyalahgunakan narkoba dan obat-obatan terlarang. Di era media social ini, sudah diketahui bahwa Internet memiliki pengaruh terbesar bagi penggunanya, secara positif dan negatif, dan salah satunya adalah mempromosikan penyalahgunaan narkoba dan pengunaan obat-obatan terlarang. Internet adalah pintu gerbang informasi. Akses terbuka ke informasi mempersulit penyaringan informasi yang diterima baik positive maupun negatif. Selain itu, internet juga merupakan alat untuk komunikasi. Dengan demikian membuat komunikasi jauh lebih mudah daripada sebelumnya. Akses terbuka ke saluran informasi dan komunikasi ini dapat digunakan secara negatif oleh remaja terutama dalam kaitannya dengan penyalahgunaan narkoba, penelitian yang dipaparkan pada artikel ini telah membuktikan bahwa social media seperti twitter, facebook dan lain lain menjadi tempat jual beli dan penyebaran informasi tentang narkoba dan penyalahgunaan obat-obata