Articles de revues sur le sujet « Computational Criminology »

In the context of intense and accelerating transformations of social reality, science acts as one of the adaptive mechanisms to understand the nature of what is happening and the prospects for future change. However, in the face of rapid and substantial social change, the social empirical sciences, including criminology, also need to be transformed to take account of the digitalisation of relations, their exponentially increasing complexity and the growth of universal connectivity. These properties of a transforming reality require not only a move away from traditional sources of information on criminologically relevant phenomena, not only the application (expansion) of an interdisciplinary approach, but also the establishment of a 'quantitative criminology paradigm', a 'computational criminology' that enables the tracking of both criminal and potential criminogenic phenomena as well as background crime phenomena in real time through monitoring and using both preset and self-generated algorithms.

Active offender research relies on the collection of data from noninstitutionalized criminals and has made significant contributions to our understanding of the etiology of serious crime. This review covers its history as well as its methodological, scientific, and ethical pitfalls and advantages. Because study subjects are currently and freely engaging in crime at the time of data collection, their memories, attitudes, and feelings about their criminality and specific criminal events are rich, detailed, and accurate. Contemporary approaches to active offender research employ systematized formats for data collection and analysis that improve the validity of findings and help illuminate the foreground of crime. Although active offender research has traditionally relied on qualitative techniques, we outline the potential for it to make contributions via mixed methods, experiments, and emerging computational and technological approaches, such as virtual reality simulation studies and agent-based modeling.

The current level of scientifi c knowledge and understanding of the genome, as well as the level of human computational capabilities, form new, so-called «post-genomic technologies» for genome research. The results of such studies can be used in diff erent areas of human life and society, for example in criminology, including solving crimes. And there may be both positive and negative legal consequences. Genetic research is an extremely useful tool for crime investigation. But it is still unclear to what extent law enforcement agencies should be able to obtain genetic data stored in public and private databases, and how this may aff ect human rights in the future.

In some applications, such as bioinformatics, social network analysis, and computational criminology, it is desirable to find compact clusters formed by a (very) small portion of objects in a large data set. Since such clusters are comprised of a small number of objects, they are extraordinary and anomalous with respect to the entire data set. This specific type of clustering task cannot be solved well by the conventional clustering methods since generally those methods try to assign most of the data objects into clusters. In this paper, we model this novel and application-inspired task as the problem of mining cohesive anomalies. We propose a general framework and a principled approach to tackle the problem. The experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our approach.

Abstract In the past, various traditional methods used experiments and statistical data to examine and solve the occurred problem and social-environmental issue. However, the traditional method is not suitable for expressing or solving the complex dynamics of human environmental crisis (such as the spread of diseases, natural disaster management, social problems, etc.). Therefore, the implementation of computational modelling methods such as Agent-Based Models (ABM) has become an effective technology for solving complex problems arising from the interpretation of human behaviour such as human society, environment, and biological systems. Overall, this article will outline the ABM model properties and its applications in the criminology, flood management, and the COVID-19 pandemic fields. In addition, this article will review the limitations that occurred to be overcome in the further development of the ABM model.

The obvious need for using modern computer networking capabilities to enable the effective sharing of information has resulted in data-sharing systems, which store, and manage large amounts of data. These data need to be effectively searched and analyzed. More specifically, in the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information, as required in homeland security, criminology, and medical applications, amongst others. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. These techniques have demonstrated varying levels of success, but there is a pressing need for name matching approaches that provide high levels of accuracy in matching names, while at the same time maintaining low computational complexity. In this paper, such a technique, called ANSWER, is proposed and its characteristics are discussed. Our results demonstrate that ANSWER possesses high accuracy, as well as high speed and is superior to other techniques of retrieving fuzzy name matches in large databases.

The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based on Subject-Matter Experts (SME) to investigate new techniques for acquiring higher-order thinking, the perception, the use of new computational explanatory techniques, support decision-making, and the judge’s cognition and behavior. Thus, a new spectrum is covered and promises to be a new area of study called Interpretation-Assessment/Assessment-Interpretation (IA-AI), consisting of explaining machine inferences and the interpretation and assessment from a human. It allows expressing a semantic, ontological, and hermeneutical meaning related to the psyche of a human (judge). The system has an interpretative and explanatory nature, and in the future, could be used in other domains of discourse. More than 33 experts in Law and Artificial Intelligence validated the functional design. More than 26 judges, most of them specializing in psychology and criminology from Colombia, Ecuador, Panama, Spain, Argentina, and Costa Rica, participated in the experiments. The results of the experimentation have been very positive. As a challenge, this research represents a paradigm shift in legal data processing.

The paper is devoted to the study, measurement and assessment of organized crime in Russia, and to researching the possibility of using «big data» to analyze crime. As the task of counteracting crime is getting more and more complicated, it becomes necessary to reconsider both the traditional methods of researching crime, and the possibilities of going beyond the boundaries of such methods. The existing forms of state statistical reporting reflect only certain crimes committed by organized criminal groups and, in exceptional cases — by criminal communities. The use of official state statistics to measure the level of organized crime leads to numerous distortions in its evaluation. In some cases, all it can do is indicate the trend. There is no assessment of the actual condition of this criminological problem due to a high complexity of measurements, of choosing the technique of information collection and analysis, as well as the theoretical model of predicting organized crime, forecasting its future trends and regularities. In the last decade, the countries where digital technologies are developing actively have witnessed an intensified interest to the use of computing and intellectual methods of analyzing organized crime. The main achievement of science is that there is a widespread understanding among criminologists involved in research that the existing instruments, methods and programs for the intellectual analysis of structured and unstructured data are sufficient for the transition to computational criminology. «Big data» have a special place in the structure of information technologies. The development of data collection and analysis technologies makes it possible to measure criminality at a conceptually new level. A significant growth in the volume of data, the sources and dissemination tools of which are social objects, leads to the establishment of new technologies of processing the information on crimes. The presented article shows the necessity of using a large volume of structured and unstructured data to study and evaluate qualitative parameters of organized crime. This presents considerable opportunities for using «big data» to predict the trends of future changes in organized crime, as well as for applying new technologies to the theory and practice of preventing organized crime. The prediction and prevention of organized crime are considered to be rather difficult, and sometimes impossible when it concerns finding empirical proof and, moreover, obtaining prediction verification of some or other concept. Consequently, up to now traditional research approaches have not been effective in the sphere of researching organized crime. Meanwhile, law enforcement bodies need knowledge on the structure of crime, on the emergence of new forms of organizing criminal groups, on changes in criminal behavior connected with the technologies of the new technological revolution, etc.

Human faces play a central role in our lives. Thanks to our behavioural capacity to perceive faces, how a face looks in a painting, a movie, or an advertisement can dramatically influence what we feel about them and what emotions are elicited. Facial information is processed by our brain in such a way that we immediately make judgements like attractiveness or masculinity or interpret personality traits or moods of other people. Due to the importance of appearance-driven judgements of faces, this has become a major focus not only for psychological research, but for neuroscientists, artists, engineers, and software developers. New technologies are now able to create realistic looking synthetic faces that are used in arts, online activities, advertisement, or movies. However, there is not a method to generate virtual faces that convey the desired sensations to the observers. In this work, we present a genetic algorithm based procedure to create realistic faces combining facial features in the adequate relative positions. A model of how observers will perceive a face based on its features’ appearances and relative positions was developed and used as the fitness function of the algorithm. The model is able to predict 15 facial social traits related to aesthetic, moods, and personality. The proposed procedure was validated comparing its results with the opinion of human observers. This procedure is useful not only for creating characters with artistic purposes, but also for online activities, advertising, surgery, or criminology.

Academics and policymakers recognize the absence of empirically grounded research to support the suppositions on which terrorist focused policies are based. (Sageman, Marc. 2014. “The Stagnation in Terrorism Research”. Terrorism and Political Violence 26 (4): 565–80) We developed our project, Mapping Networks and Narratives of Online Right-Wing Extremists in New South Wales, (Department of Security Studies and Criminology. 2020. Mapping Networks and Narratives of Online Right-Wing Extremists in New South Wales. https://doi.org/10.5281/zenodo.4071472) to illuminate this space. Using the analysis of large-scale online data to generate evidence-based insights into online Right-Wing Extremism (RWE) across the state, our research focused on four key questions: (1) What is the nature of the online RWE environment in New South Wales, Australia (NSW)? (2) How is this movement distributed across NSW? (3) How are themes and narratives framed in different online contexts to mobilize support? (4) What level of risk does the online right-wing environment pose? These questions were left purposely broad to facilitate an exploratory project into what was, in 2018–2019, still a relatively little studied milieu. We combined expertise from computational science, security studies, and behavioral science. We were funded by the Department of Communities and Justice, NSW. We identified two distinct—yet connected—levels of risk. The first was a creeping threat to democracy fueled by networks and content that challenged the fundamental principles of pluralistic liberal democracy. The second was a risk of violence perpetrated by individuals and/or groups that advocate and/or support the use of violence as a tactic to achieve an ideological end. The communities we examined were primarily characterized by networks of individuals as opposed to formal groups. The role played by individual influencers has important ramifications for policy communities: attention should be paid to issues of proscription and moderation. While this milieu engaged with Australian issues and events, it was notably far more obsessed with American issues: particularly those focused on populist narratives and Trumpism. Despite being hateful and extreme, online RWE communities are, firstly, spaces of sociability for users, where social networks are maintained by shared values and norms. For those involved, these spaces engender positive experiences: individuals might share an image of their dinner cooking in their kitchen interspersed with “shitposting” and virulent hate speech. While we identified a variety of narratives that focused on the delegitimization of government and dehumanization of others, the central theme was that of “white identity under threat”. We observed five distinct stages of moderation approach and echo chamber strength. A series of issues for future consideration were identified from the analysis: (1) Awareness raising for key stakeholders across different levels of government and civil society about the revolutionary and anti-social agenda of RWE communities. (2) Building awareness about the civic underpinnings of representative liberal democracy and the threat that RWE poses. (3) Expanding current Countering Violent Extremism infrastructure provided by the NSW government to individuals and communities vulnerable to right wing extremism. (4) The local government is well positioned to deliver programs in rural communities impacted by RWE. (5) Upskilling front-line workers to recognize the risks associated with RWE, and providing pathways into CVE intervention programs for individuals identified as being at-risk.

Network K -function has been the de facto operation for analyzing point patterns in spatial networks, which is widely used in many communities, including geography, ecology, transportation science, social science, and criminology. To analyze a location dataset, domain experts need to generate a network K -function plot that involves computing multiple network K -functions. However, network K -function is a computationally expensive operation that is not feasible to support large-scale datasets, let alone to generate a network K -function plot. To handle this issue, we develop two efficient algorithms, namely count augmentation (CA) and neighbor sharing (NS), which can reduce the worst-case time complexity for computing network K -functions. In addition, we incorporate the advanced shortest path sharing (ASPS) approach into these two methods to further lower the worst-case time complexity for generating network K -function plots. Experiment results on four large-scale location datasets (up to 7.33 million data points) show that our methods can achieve up to 165.85x speedup compared with the state-of-the-art methods.

Abstract Many types of crime are difficult to study because they are hard to operationalize, hidden from the public, or both. With communication increasingly moving to online domains, recent work has begun to examine whether the online domain contains traces of such hard-to-observe crimes. This study explores the online linguistic contours of hard-to-observe crimes through a rigorous mixed-methods approach that combines interviews and computational text analysis. Using human trafficking in illicit massage businesses as a proof-of-concept, we show how this approach, which we call semi-inductive triangulation, meets the empirical contextuality and relationality of crime traces in the online domain. The findings contribute to an emerging field of computational criminology and call for an integration of linguistic approaches in criminology.

This study examines to what extent new and emerging data sources or big data have been empirically used to measure key theoretical concepts within environmental criminology. By means of a scoping review, aimed at studies published between 2005 and 2018, insight is provided into the characteristics of studies that used big data sources within environmental criminology. The type and extent of big data sources used, as well as the strengths and weaknesses of these data sources, are synthesized. After the selection procedure, 84 studies were included for further analysis. Although the number of studies increased each year, there has been a remarkable increase in the number of studies since 2014. The findings suggest that most studies used administrative data or user-generated content as one type of research data. However, innovative data sources (automated and volunteered data) have gained in importance in recent years. Also, most studies are of a descriptive or predictive nature, predominantly conducted by computational (social) scientists. Since these approaches pay little to no attention to mechanisms that bring about social outcomes, an alternative philosophical framework is proposed. We put forward a scientific realist approach as a solution to integrate data-driven and theory-driven research. This approach responds to recent calls to move towards an ‘analytical criminology’. The results are discussed within this framework, and translated into avenues for future research.

Abstract National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent of ‘trigger’ events. Using Computational Criminology that draws on data science methods, we link police crime, census and Twitter data to establish a temporal and spatial association between online hate speech that targets race and religion, and offline racially and religiously aggravated crimes in London over an eight-month period. The findings renew our understanding of hate crime as a process, rather than as a discrete event, for the digital age.

AbstractCorruption, bribery, and white-collar crime are inherently relational phenomena as actors involved in them exchange information, resources, and favours. These exchanges give rise to a network in which the actors are embedded. While this has been often emphasized in the literature, there is a lack of studies actually empirically examining the structural properties of corruption networks. We aim to fill this gap with this exploratory study analysing the networks of corporate and public sector bribery. We theoretically ground the network analysis in two compatible criminological frameworks: routine activity theory and analytical criminology. We extract information about relations and interactions among involved actors in three cases from Statements of Facts from Deferred Prosecution Agreements obtained from United Kingdom’s Serious Fraud Office. Our findings indicate that the bribery networks resemble core-periphery structure networks with ties predominantly concentrated between a few very central actors (core), who ad hoc engage with actors from the periphery. Moreover, we also see a strong tendency of actors to repeat interactions within certain dyads. In terms of temporal dynamics, we observe periods of relative inaction alternating with periods of frequent and repeated contacts triggered by the presence of contracts susceptible to corruption. We discuss these findings in terms of their policy implications for designing evidence-based intervention and prevention measures.

AbstractLongitudinal clustering techniques are widely deployed in computational social science to delineate groupings of subjects characterized by meaningful developmental trends. In criminology, such methods have been utilized to examine the extent to which micro places (such as streets) experience macro-level police-recorded crime trends in unison. This has largely been driven by a theoretical interest in the longitudinal stability of crime concentrations, a topic that has become particularly pertinent amidst a widespread decline in recorded crime. Recent studies have tended to rely on a generic implementation k-means to unpick this stability, with little consideration for its theoretical suitability. This study makes two methodological contributions. First, it demonstrates the application of k-medoids to study longitudinal crime concentrations, and second, it develops a novel ‘anchored k-medoids’ (ak-medoids), a bespoke clustering method specifically designed to meet the theoretical requirements of micro-place investigations into long-term stability. Using both simulated data and 15-years of police-recorded crime data from Birmingham, England, we compare the performances of k-medoids against ak-medoids. We find that both methods highlight instability in the exposure to crime over time, but the consistency and contribution of cluster solutions determined by ak-medoids provide insight overlooked by k-medoids, which is sensitive to short-term fluctuations and subject starting points. This has important implications for the theories said to explain longitudinal crime concentrations, and the law enforcement agencies seeking to offer an effective and equitable service to the public.

Involuntarily celibate men (Incels) form online communities in which they “often bemoan their lack of a loving relationship with a woman while simultaneously dehumanizing women and calling for misogynistic violence” (Glace et al., 2021, p. 288). Several studies investigate this dehumanization and misogyny including (gendered) hate speech in online comments from Incels (e.g., Glace et al., 2021). However, not all online comments from Incels contain misogyny or gendered hate speech. To get a better understanding of the phenomenon of Incels, it would be better to not only focus on these problematic comments. Thus, we propose a new construct called “Online Incel speech”, which is defined as the sum of all online comments from Incels that are related to Inceldom, that is, being or becoming an Incel. In an approach to provide an extensive system of categorization, Grau Chopite (2022) synthesized codebooks from several studies on Incels (see example studies table note) and put it to an empirical test. She found that most Incel comments found online can be categorized into three subdimensions. The first two subdimensions cover framing by Incels, namely how Incels frame the subjective causes of becoming an Incel and how they frame the subjective emotional consequences of being an Incel. Both subdimensions can also be interpreted as part of a subjective theory (sensu Groeben et al., 1988) of Inceldom. In contrast to this, the third subdimension does not consist of framing, but of observable verbal behaviors, which are often linked to gendered hate speech. When trying to categorize online comments from Incels, former studies often applied the construct “Hybrid Masculinities” (e.g., Glace et al, 2021). This construct from Bridge and Pascoe (2014) suggests that “some men develop masculinities which appear to subvert, but actually reaffirm, White hegemonic masculinities” (Glace et al., 2021, p. 289). Glace et al. (2021) structure the construct into three subdimensions, namely (1) discursive distancing (claiming distance from hegemonic masculine roles without actually relinquishing masculine power), (2) strategic borrowing (appropriating the cultures of nondominant groups of men), and (3) fortifying boundaries (continually using hegemonic standards to constrain masculinity and demeaning men who fail to meet them). However, the construct only covers a part of Inceldom, which Glace et al. (2021) indirectly acknowledge by adding two inductive categories, that is, hostile sexism (shaming and degrading women) and suicidality (reporting suicidal thoughts, feelings, and intentions). Field of application/theoretical foundation: The construct “Online Incel speech” was coined by Grau Chopite (2022), and there are currently no other studies making use of it. However, there are studies (e.g., Vu & Lynn, 2020; also see the entry “Frames (Automated Content Analysis”) based on the framing theory by Entman (1991) where the subdimension “subjective causes” would correspond to Entman’s “causal interpretation frame”, while the “subjective emotional consequences” would correspond to Entman’s “problem definition frame”. The “subjective causes” also correspond to the “discursive distancing” and the “emotional consequences” to “suicidality” in the construct of Hybrid Masculinities. The third subdimension “verbal behavior” corresponds to gendered online hate speech (e.g., Döring & Mohseni, 2019), but also to “hostile sexism” and “fortifying boundaries” in the construct of Hybrid Masculinities. References/combination with other methods: The study by Grau Chopite (2022) employs a quantitative manual content analysis using a deductive approach. Studies based on the construct of Hybrid Masculinities also employ manual online content analyses or manual thematic analyses, but those are often qualitative in nature (e.g., Glace et al., 2021). Framing is also often assessed with manual content analyses (e.g., Nitsch & Lichtenstein, 2019), but newer studies try to assess it computationally (e.g., Vu & Lynn, 2020). Hate speech is often assessed with manual content analyses (e.g., Döring & Mohseni, 2019) and surveys (e.g., Oksanen et al., 2014), but some newer studies try to assess it computationally (e.g., Al-Hassan & Al-Dossari, 2019). As Online Incel Speech is related to framing and gendered hate speech, it seems plausible that manual content analyses of Online Incel Speech could be combined with computational analyses, too, to enable the investigation of large samples. However, computational analyses of subtle forms of verbal behavior can be challenging because the number of wrong categorizations increases (e.g., for sexism detection see Samory et al., 2021; for hate speech detection see Ruiter et al., 2022). Example studies: Example study Construct Dimensions Explanation Reliability Online Incel speech Grau Chopite (2022) Subjective Causes of Inceldom Race/Ethnicity having certain racial features and/or belonging to a certain ethnic κ = .55;AC1 = .80 Mental Health suffering from any mental health issue κ = .58;AC1 = .90 Employment difficulties with getting and/or maintaining employment; experiencing dissatisfaction in the workplace κ = .85;AC1 = .98 Family having family issues (e.g., an abusive family member) κ = .66;AC1 = .98 Subjective Emotional Consequences of Inceldom Hopelessness expressing hopelessness κ = .37;AC1 = .89 Sadness expressing sadness κ = .26;AC1 = .91 Suicidality expressing suicidality κ = .24;AC1 = .95 Anger expressing anger κ = .44;AC1 = .87 Hatred expressing hatred κ = .40;AC1 = .83 Verbal Behavior of Incels Using Gendered Hate Speech Against Women hostile sexism against women and misogynistic speech κ = .80;AC1 = .87 Adopting Social Justice Language claiming unfairness/ injustice of being discriminated by society or groups (e.g., other men, other races) κ = .48;AC1 = .82 Claiming Lack of Masculine Traits lacking masculine traits (e.g., muscles, a big penis) κ = .62;AC1 = .86 Shaming Other Men shaming of other men directly by calling them terms related to being “effeminate” or “unmanly” κ = .71;AC1 = .91 Claiming Lack of Female Interest being unable to attract women or being rejected by women κ = .61;AC1 = .87 Hybrid Masculinities Glace et al. (2021) Discursive Distancing Lack of Female Interest claiming a lack of ability to attract female romantic companionship and sexual interest n/a Lack of Masculine Traits claiming a lack of traditionally attractive masculine physical traits n/a Strategic Borrowing Race and Racism appropriating the culture of racial and ethnic minority men n/a Social Justice Language using the language of the marginalized to diminish one’s own position of power n/a Fortifying Boundaries Soyboys deriding non-Incel men as weak and desperate n/a Cucks deriding non-Incel men as being cheated or exploited by women n/a Hostile Sexism Women are Ugly deriding women for being unattractive n/a Slut-Shaming deriding women for having sex n/a False Rape Claims claiming that women make false rape claims (e.g., when approached by an Incel) n/a Women’s Only Value is Sex claiming that women’s only value is their sexuality n/a Women are Subhuman dehumanizing women n/a Suicidality Due to Incel Experience attributing suicidal thoughts, feelings, and intentions to Incel status n/a The “Clown World” claiming that the world is meaningless and nonsensical n/a Note: The codebook from Grau Chopite (2022) is based on the codebook and findings of Glace et al. (2021) and other studies (Baele et al., 2019; Bou-Franch & Garcés-Conejos Blitvich, 2021; Bridges & Pascoe, 2014; Cottee, 2020; Döring & Mohseni, 2019; D’Souza et al., 2018; Marwick & Caplan, 2018; Mattheis & Waltman, 2021; Maxwell et al., 2020; Rogers et al., 2015; Rouda & Siegel, 2020; Scaptura & Boyle, 2019; Williams & Arntfield, 2020; Williams et al., 2021). Gwet’s AC1 was calculated in addition to Cohen’s Kappa because some categories were rarely coded, which biases Cohen’s Kappa. The codebook is available at http://doi.org/10.23668/psycharchives.5626 References Al-Hassan, A., & Al-Dossari, Hmood (2019). Detection of hate speech in social networks: A survey on multilingual corpus. In D. Nagamalai & D. C. Wyld (Eds.), Computer Science & Information Technology. Proceedings of the 6th International Conference on Computer Science and Information Technology (pp. 83–100). AIRCC Publishing. doi:10.5121/csit.2019.90208 Baele, S. J., Brace, L., & Coan, T. G. (2019). From “Incel” to “Saint”: Analyzing the violent worldview behind the 2018 Toronto attack. Terrorism and Political Violence, 1–25. doi:10.1080/09546553.2019.1638256 Bou-Franch, P., & Garcés-Conejos Blitvich, P. (2021). Gender ideology and social identity processes in online language aggression against women. In R. M. DeKeyser (Ed.), Benjamins Current Topics: Vol. 116. Aptitude-Treatment Interaction in Second Language Learning (Vol. 86, pp. 59–81). John Benjamins Publishing Company. doi:10.1075/bct.86.03bou Bridges, T., & Pascoe, C. J. (2014). Hybrid masculinities: New directions in the sociology of men and masculinities. Sociology Compass, 8(3), 246–258. doi:10.1111/soc4.12134 Cottee, S. (2021). Incel (e)motives: Resentment, shame and revenge. Studies in Conflict & Terrorism, 44(2), 93–114. doi:10.1080/1057610X.2020.1822589 Döring, N., & Mohseni, M. R. (2018). Male dominance and sexism on YouTube: Results of three content analyses. 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Retrieved from https://nbn-resolving.org/urn:nbn:de:0168-ssoar-27658 Marwick, A. E., & Caplan, R. (2018). Drinking male tears: language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543–559. doi:10.1080/14680777.2018.1450568 Mattheis, A. A., & Waltman, M. S. (2021). Gendered hate online. In K. Ross & I. Bachmann (Eds.), The Wiley Blackwell-ICA international encyclopedias of communication. The international encyclopedia of gender, media, and communication (pp. 1–5). John Wiley & Sons Inc. doi:10.1002/9781119429128.iegmc019 Maxwell, D., Robinson, S. R., Williams, J. R., & Keaton, C. (2020). “A short story of a lonely guy”: A qualitative thematic analysis of involuntary celibacy using Reddit. Sexuality & Culture, 24(6), 1852–1874. doi:10.1007/s12119-020-09724-6 Nitsch, C. & Lichtenstein, D. (2019). Satirizing international crises. The depiction of the Ukraine, Greek debt and migration crises in political satire. Studies in Communication Science (SComS), 19(1), 85-103. doi:10.24434/j.scoms.2019.01.007 Oksanen, A., Hawdon, J., Holkeri, E., Näsi, M., & Räsänen, P. (2014). Exposure to online hate among young social media users. In N. Warehime (Ed.), Soul of Society: A focus on the lives of children & youth (p. 253-273). doi:10.1108/S1537-466120140000018021 Rogers, D. L., Cervantes, E., & Espinosa, J. C. (2015). Development and validation of the belief in female sexual deceptiveness scale. Journal of Interpersonal Violence, 30(5), 744–761. doi:10.1177/0886260514536282 Rouda, B., & Siegel, A. (2020). I’d kill for a girl like that”: The black pill and the Incel uprising. International Multidisciplinary Program in the Humanities, Tel Aviv University. Retrieved from https://www.academia.edu/43663741/_Id_kill_for_a_girl_like_that_The_Black_Pill_and_the_Incel_Uprising Ruiter, D., Reiners, L., Geet D’Sa, A., Kleinbauer, Th., Fohr, D., Illina, I., Klakow. D., Schemer, Ch., & Monnier, A. (2022). Placing m-phasis on the plurality of hate. A feature-based corpus of hate online. Preprint. Retrieved from https://doi.org/10.48550/arXiv.2204.13400 Samory, M., Sen, I., Kohne, J., Flöck, F., & Wagner, C. (2021). “Call me sexist, but...”: Revisiting sexism detection using psychological scales and adversarial samples. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 573-584. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18085 Scaptura, M. N., & Boyle, K. M. (2019). Masculinity threat, “Incel” traits, and violent fantasies among heterosexual men in the United States. Feminist Criminology, 15(3), 278–298. doi:10.1177/1557085119896415 Vu, H. T., & Lynn, N. (2020). When the news takes sides: Automated framing analysis of news coverage of the Rohingya crisis by the elite press from three countries. Journalism Studies. Online first publication. doi:10.1080/1461670X.2020.1745665 Williams, D. J., & Arntfield, M. (2020). 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Pornography is a fictional media genre that depicts sexual fantasies and explicitly presents naked bodies and sexual activities for the purpose of sexual arousal (Williams, 1989; McKee et al., 2020). Regarding media ethics and media effects, pornography has traditionally been viewed as highly problematic. Pornographic material has been accused of portraying sexuality in unhealthy, morally questionable and often sexist ways, thereby harming performers, audiences, and society at large. In the age of the Internet, pornography has become more diverse, accessible, and widespread than ever (Döring, 2009; Miller et al., 2020). Consequently, the depiction of sexuality in pornography is the focus of a growing number of content analyses of both mass media (e.g., erotic and pornographic novels and movies) and social media (e.g., erotic and pornographic stories, photos and videos shared via online platforms). Typically, pornography’s portrayals of sexuality are examined by measuring the prevalence and frequency of sexual practices and related gender roles via quantitative content analysis (for research reviews see Carrotte et al., 2020; Miller & McBain, 2022). This DOCA entry focuses on the representation of violence as one of eight important dimensions of the portrayals of sexuality in pornography. Field of application/theoretical foundation: In the field of pornographic media content research, different theories are used, mainly 1) general media effects theories, 2) sexual media effects theories, 3) gender role, feminist and queer theories, 4) sexual fantasy and desire theories, and different 5) mold theories versus mirror theories. The DOCA entry “Conceptual Overview (Portrayals of Sexuality in Pornography)” introduces all these theories and explains their application to pornography. The respective theories are applicable to the analysis of the depiction of violence as one dimension of the portrayals of sexuality in pornography. References/combination with other methods of data collection: Manual quantitative content analyses of pornographic material can be combined with qualitative (e.g., Keft-Kennedy, 2008) as well as computational (e.g., Seehuus et al., 2019) content analyses. Furthermore, content analyses can be complemented with qualitative interviews and quantitative surveys to investigate perceptions and evaluations of the portrayals of sexuality in pornography among pornography’s creators and performers (e.g., West, 2019) and audiences (e.g., Cowan & Dunn, 1994; Hardy et al., 2022; Paasoonen, 2021; Shor, 2022). Additionally, experimental studies are helpful to measure directly how different dimensions of pornographic portrayals of sexuality are perceived and evaluated by recipients, and if and how these portrayals can affect audiences’ sexuality-related thoughts, feelings, and behaviors (e.g., Kohut & Fisher, 2013; Miller et al., 2019). Example studies for manual quantitative content analyses: Common research hypotheses state that pornography depicts sexuality as violent and shows violent acts such as verbal aggression, physical aggression or image-based abuse being predominately perpetrated by men targeting women. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “violence” and use valid and reliable measures for different types of violence. In addition, it is necessary to code the sex/gender of the person depicted as the source and/or the target of the respective violent act (e.g., in the context of verbal sexual aggression, the target of verbal aggression is coded as female or male and the source of verbal aggression is coded as female or male). It is important to note that in the context of pornographic content research, researchers conceptualize violence differently. Also, it should be noted, that there is some overlap between the variable violence and the variable degradation in the context of pornographic portrayals of sexuality. For example, the depiction of “name calling” in a pornographic scene can be understood as an indicator of “violence” (namely verbal aggression) or of “degradation”. Name calling is covered here as verbal aggression (following Fritz et al., 2020), hence, it is not covered again as degradation, even though some authors do so (such as Gorman et al., 2010; see DOCA entry “Degradation (Portrayals of Sexuality in Pornography)”). In general, one can argue that all violent acts – apart from being potentially painful and harmful – have a component of degradation because they put the target of violence in a subordinate role. However, not all degrading acts are violent (e.g., degradation by systematic lack of sexual reciprocity does not entail overt aggression). Coding Material Measure Operationalization (excerpt) Reliability Source Violence: Usually, violence is defined as behavior directed toward the goal of harm or injury of another living being, who is motivated to avoid such treatment (McKee, 2015). However, in content analyses of pornography, violent behavior is often coded regardless of intention to harm or actual harm done. Instead, violence is coded with reference to the presence of prespecified behaviors (e.g., spanking, slapping, choking), even if these behaviors are presented as consensual and sexually arousing (Miller & McBain, 2022). Technology-facilitated sexual violence (image-based sexual abuse) addresses the illegal recording and dissemination of intimate imagery without consent, such as revenge porn, upskirting or spy cams (Henry & Powell, 2018). Mainstream pornography platforms partly disseminate illegal material and partly market some of their legal commercial pornography under these respective labels, hence pretending to provide non-consensual pornography (Vera-Gray et al., 2021). Apart from issues of performer health protection, violent acts are also regarded as relevant in terms of modelling behaviors for audiences. N=4,009 heterosexual scenes from 3,767 pornographic videos sampled from PornHub.com (574 scenes) and and Xvideos. com (3,435 scenes) Verbal aggression “An action that clearly does or could reasonably be expected to cause psychological harm to oneself or another person through name calling or insulting”. Binary coding (1: present; 2: not present). Percentage Agreement: 97.5% (PornHub) / 88.9% (Xvideos) Fritz et al. (2020) Physical aggression “Any action that clearly did or could reasonably be expected to cause physical harm to oneself or another person, regardless of the perpetrator’s intent and the target’s response”. Binary coding (1: present; 2: not present). Percentage Agreement: 98.8% (Pornhub) / 97.6% (Xvideos) - Spanking (type of physical aggression) “Striking on the buttocks with an open hand”. Binary coding (1: present; 2: not present). Percentage Agreement: 94.2% (Pornhub) / 96.9% (Xvideos) - Slapping (type of physical aggression) “Striking oneself or another with an entirely unclosed hand, group of fingers, or palm”. Binary coding (1: present; 2: not present). Percentage Agreement: 99.2% (Pornhub) / 98.1% (Xvideos) - Gagging (type of physical aggression) “Any instance in which an object (including the genitals) is inserted into a person’s mouth, such that it appears to cut off their ability to breathe freely and/or causes them to experience a throat spasm”. Binary coding (1: present; 2: not present). Percentage Agreement: 99.2% (Pornhub) / 96.7% (Xvideos) - Pulling hair (type of physical aggression) “Any instance where the hair on a person’s head is grasped or pulled on, such that the person’s head is pulled (even slightly) in a particular direction”. Binary coding (1: present; 2: not present). Percentage Agreement: 100.0% (Pornhub) / 98.9% (Xvideos) - Choking (type of physical aggression) “To cause another to stop breathing, if only for a moment, by grabbing the throat”. Binary coding (1: present; 2: not present). Percentage Agreement: 98.3% (Pornhub) / 98.8% (Xvideos) - Pushing (type of physical aggression) ‘‘Use of one’s hands, arms, or other body parts to force another person’s body or part of their body to move in a particular manner or direction”. Binary coding (1: present; 2: not present). Percentage Agreement: 95.0% (Pornhub) / 97.9% (Xvideos) N=131,738 titles of pornographic videos presented on the landing pages of the three leading mainstream pornography video platforms in the UK: PornHub.com, Xhamster.com, Xvideos.com Image-based sexual abuse Pornographic video title includes keywords indicating image-based sexual abuse such as “spy”, “hidden”, “upskirting”, “leak” or “revenge”. Binary coding (1: present; 2: not present). Not available Vera-Gray et al. 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