An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

This research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10063130, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion), and the 2018 Yeungnam University Research Grant.Peer reviewe

Examining UK drill music through sentiment trajectory analysis

This paper presents how techniques from natural language processing can be used to examine the sentiment trajectories of gang-related drill music in the United Kingdom (UK). This work is important because key public figures are loosely making controversial linkages between drill music and recent escalations in youth violence in London. Thus, this paper examines the dynamic use of sentiment in gang-related drill music lyrics. The findings suggest two distinct sentiment use patterns and statistical analyses revealed that lyrics with a markedly positive tone attract more views and engagement on YouTube than negative ones. Our work provides the first empirical insights into the language use of London drill music, and it can, therefore, be used in future studies and by policymakers to help understand the alleged drill-gang nexus

Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media

Social media offers a unique lens to observe users emotions and subjective feelings toward critical events or topics and has been widely used to investigate public sentiment during crises, e.g., the COVID-19 pandemic. However, social media use varies across demographic groups, with younger people being more inclined to use social media than the older population. This digital divide could lead to biases in data representativeness and analysis results, causing a persistent challenge in research based on social media data. This study aims to tackle this challenge through a case study of estimating the public sentiment about the COVID-19 using social media data. We analyzed the pandemic-related Twitter data in the United States from January 2020 to December 2021. The objectives are: (1) to elucidate the uneven social media usage among various demographic groups and the disparities of their emotions toward COVID-19, (2) to construct an unbiased measurement for public sentiment based on social media data, the Sentiment Adjusted by Demographics (SAD) index, through the post-stratification method, and (3) to evaluate the spatially and temporally evolved public sentiment toward COVID-19 using the SAD index. The results show significant discrepancies among demographic groups in their COVID-19-related emotions. Female and under or equal to 18 years old Twitter users expressed long-term negative sentiment toward COVID-19. The proposed SAD index in this study corrected the underestimation of negative sentiment in 31 states, especially in Vermont. According to the SAD index, Twitter users in Wyoming (Vermont) posted the largest (smallest) percentage of negative tweets toward the pandemic

Do Machines Replicate Humans? Toward a Unified Understanding of Radicalizing Content on the Open Social Web

The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web‐based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding

Postal Delivery Systems and Cryptomarkets

This thesis utilizes quantitative methods to evaluate the relationship between the level of development of state postal delivery systems and the number of drug sales originating on dark web cryptomarkets. I use data from the United Nations Universal Postal Union on state postal system operating expenditures to convey postal system development and compare it to the country-of-origin and acceptable countries-of-destination listed on cryptomarket drug postings. The cryptomarket drug postings being used were collected by previous scholars from a leak on Agora from 2014-2015. This dataset contains 96,286 observations of cryptomarket-based drug transactions. This paper expected to find that moderately developed countries with moderately strong postal systems have the highest proportion of cryptomarket-based drug shipments due to a lapse in developing security measures compared to postal infrastructure. This, however, was not supported by the data which can likely be attributed to the unreliability of the available data. The findings here and potential future iterations have potential implications for drug enforcement policy and contribute to the scholarship on drug trafficking, cryptomarkets, postal delivery systems, cybercrime, and international security

Les actes de langage violents à titre de comportements collectifs radicaux : étude du déploiement du langage haineux au sein des dynamiques de radicalisation dans les milieux numériques de l'extrême-droite

Bien que le concept de radicalisation à titre de sujet d'étude continue d'évoluer, il demeure un sujet de préoccupation et d'actualité. Son traitement dans le milieu de la recherche demeure étroitement lié aux enjeux de sécurité nationale et surtout aux enjeux propres aux actes terroristes. Les conséquences sociales et politiques des processus de radicalisation ne sont pourtant plus à démontrer et attirent de plus en plus le regard des décideurs, des chercheurs et des regroupements citoyens. Parmi ces conséquences, on compte la propagation des discours haineux et violents qui peuvent soutenir des idéologies haineuses, normaliser la discrimination et miner la vie démocratique. La numérisation massive de la société et des interactions sociales au quotidien fourni également des environnements structurés et isolés pour permettre à des communautés qualifiées de radicales de se rassembler. Cette recherche vise à étudier les comportements collectifs qui se déploient au sein des communautés de socialisation radicale, plus précisément dans les milieux d'extrême droite qui s'appuient idéologiquement sur ces discours haineux. Elle s'appuie sur une approche interdisciplinaire et se voulant holistique en considérant à la fois les grandes tendances macroscopiques et les caractéristiques microscopiques observées. Elle s'articule autour des caractéristiques des individus, du discours et de l'environnement de communication ainsi qu'aux dynamiques qui lient ces trois pôles ensembles. Ce mémoire explore également la contribution de disciplines moins présentes dans le champ de la criminologie par le biais des sciences du langage dans cette étude des environnements de socialisation radicale. Il s'agit d'une démarche exploratoire cherchant à mettre en commun les concepts et les principes de la théorie des actes de langage et des modèles de seuils des comportements collectifs au sein de l'étude de la radicalisation.Even though the concept of radicalization continues to evolve as a research topic, it remains up to date as a matter of concerns. The research on radicalization remains closely related to national security issues and especially to terrorist activities. We are aware of some of the social and political consequences of radicalization processes and decision-makers, researchers as well as citizen association keep raising awareness on those consequences. Hate speech and violent discourses are among those consequences and can uphold hateful ideologies, normalize discrimination and threaten the democratic process of our societies. The digitization of society and its social interactions on a daily basis provides many opportunities to bring together so-called radical communities in isolated yet structured environments. This study aims to investigate collective behaviors unfolding within these radical-oriented socialization communities, specifically in the far-right scene heavily relying on hate speech. This research relies on an interdisciplinary and holistic approach, focused on both macroscopic patterns as well as microscopic characteristics. It is organized around the analysis of characteristics from individuals, discourses and the communication environment where they interact as well as the dynamics linking these nodes together. This work also explores the input of less frequent fields of research within criminology by relying on the contributions of many subfields of linguistics in this study of radical-oriented socialization environment. It represents an exploratory approach aiming to bring together key principles and concepts of the speech act theory and threshold model of collective behaviour into radicalization studies

Understanding the collective identity of the radical right online: A mixed-methods approach

Criminologists have generally agreed that the Internet is not only a tool or resource for right-wing extremists to disseminate ideas and products, but also a site of important identity work, accomplished interactively through the exchange of radical ideas. Online discussion forums, amongst other interactive corners of the Web, have become an essential conduit for the radical right to air their grievances and bond around their “common enemy.” Yet overlooked in this discussion has been a macro-level understanding of the radical discussions that contribute to the broader collective identity of the extreme right online, as well as what constitutes “radical posting behaviour” within this context. Drawing from criminal career measures to facilitate this type of analysis, data was extracted from a sub-forum of the most notorious white supremacy forum online, Stormfront, which included 141,763 posts made by 7,014 authors over approximately 15 years. In study one of this dissertation, Sentiment-based Identification of Radical Authors (SIRA), a sentiment analysis-based algorithm that draws from traditional criminal career measures to evaluate authors’ opinions, was used to identify and, by extension, assess forum authors’ radical posting behaviours using a mixed-methods approach. Study two extended on study one by using SIRA to quantify authors’ group-level sentiment about their common enemies: Jews, Blacks, and LGBTQs. Study three further extended on studies one and two by analyzing authors’ radical posting trajectories with semi-parametric group-based modeling. Results highlighted the applicability of criminal career measures to study radical discussions online. Not only did this mixed-methods approach provide theoretical insight into what constitutes radical posting behaviour in a white supremacy forum, it also shed light on the communication patterns that contribute to the broader collective identity of the extreme right online