Online Incel Speech (Hate Speech/Incivility)

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. Feminist Media Studies, 19(4), 512–524. doi:10.1080/14680777.2018.1467945 D'Souza, T., Griffin, L., Shackelton, N., & Walt, D. (2018). Harming women with words: The failure of Australian law to prohibit gendered hate speech. University of New South Wales Law Journal, 41(3), 939–976. Entman, R. M. 1991. Framing U.S. coverage of international news: contrasts in narratives of the KAL and Iran Air incidents. Journal of Communication, 41(4), 6-7. Glace, A. M., Dover, T. L., & Zatkin, J. G. (2021). Taking the black pill: An empirical analysis of the “Incel”. Psychology of Men & Masculinities, 22(2), 288–297. doi:10.1037/men0000328 Grau Chopite, J. (2022). Framing of Inceldom on incels.is: A content analysis [Master’s thesis, TU Ilmenau]. Psycharchives. doi:10.23668/psycharchives.5626 Groeben, N., Wahl, D., Schlee, J., & Scheele, B. (Eds.). (1988). Das Forschungsprogramm Subjektive Theorien: eine Einführung in die Psychologie des reflexiven Subjekts. Francke. 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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New Formats, New Methods: Computational Approaches as a Way Forward for Media Entertainment Research

The rise of new technologies and platforms, such as mobile devices and streaming services, has substantially changed the media entertainment landscape and continues to do so. Since its subject of study is changing constantly and rapidly, research on media entertainment has to be quick to adapt. This need to quickly react and adapt not only relates to the questions researchers need to ask but also to the methods they need to employ to answer those questions. Over the last few years, the field of computational social science has been developing and using methods for the collection and analysis of data that can be used to study the use, content, and effects of entertainment media. These methods provide ample opportunities for this area of research and can help in overcoming some of the limitations of self-report data and manual content analyses that most of the research on media entertainment is based on. However, they also have their own set of challenges that researchers need to be aware of and address to make (full) use of them. This thematic issue brings together studies employing computational methods to investigate different types and facets of media entertainment. These studies cover a wide range of entertainment media, data types, and analysis methods, and clearly highlight the potential of computational approaches to media entertainment research. At the same time, the articles also include a critical perspective, openly discuss the challenges and limitations of computational methods, and provide useful suggestions for moving this nascent field forward

Virtual Emergency Assistance - The Effect of Virtual Helping, Aggression and Emergency Assistance on Helping and Aggressive Behavior

A recent meta-analysis of Anderson and colleagues (2010) shows that violent behavior in computer games promotes violent behavior in real-life and inhibits prosocial behavior. A couple of studies conducted by Greitemeyer and Osswald (2010) lead to the conclusion that helping behavior in computer games furthers helping behavior in real-life. There exist no studies examining the combined effect of violence and helping in computer games, although this combination is typical for violent video games (Anderson et al., 2010). In violent RPGs, a lot of tasks consist of helping someone by using violence. The present study addresses this issue and bridges the current empirical gap by investigating if violent emergency assistance furthers helping behavior and/or violent behavior in real-life. To accomplish that, the role-playing game “Oblivion” was modified to create four different experimental conditions: (1) violent emergency assistance, (2) killing, (3) helping, and (4) treasure hunting. Comparing these conditions, violent emergency assistance seemingly reduces helping behavior in real-life and at the same time furthers violent behavior. The results are in unison with the moral management model (Hartmann & Vorderer, 2010; Hartmann, in press), which is based on Banduras Theory of Moral Disengagement (Bandura, 2002)

Press CRTT to measure aggressive behavior: The unstandardized use of the competitive reaction time task in aggression research.

The Competitive Reaction Time Task (CRTT) is the measure of aggressive behavior most commonly used in laboratory research. However, the test has been criticized for issues in standardization because there are many different test procedures and at least 13 variants to calculate a score for aggressive behavior. We compared the different published analyses of the CRTT using data from 3 different studies to scrutinize whether it would yield the same results. The comparisons revealed large differences in significance levels and effect sizes between analysis procedures, suggesting that the unstandardized use and analysis of the CRTT have substantial impacts on the results obtained, as well as their interpretations. Based on the outcome of our comparisons, we provide suggestions on how to address some of the issues associated with the CRTT, as well as a guideline for researchers studying aggressive behavior in the laboratory