Hate speech (Hate Speech/Incivility)

The variable hate speech is an indicator used to describe communication that expresses and/or promotes hatred towards others (Erjavec & Kovačič, 2012; Rosenfeld, 2012; Ziegele, Koehler, & Weber, 2018). A second element is that hate speech is directed against others on the basis of their ethnic or national origin, religion, gender, disability, sexual orientation or political conviction (Erjavec & Kovačič, 2012; Rosenfeld, 2012; Waseem & Hovy, 2016) and typically uses terms to denigrate, degrade and threaten others (Döring & Mohseni, 2020; Gagliardone, Gal, Alves, & Martínez, 2015). Hate speech and incivility are often used synonymously as hateful speech is considered part of incivility (Ziegele et al., 2018). Field of application/theoretical foundation: Hate speech (see also incivility) has become an issue of growing concern both in public and academic discourses on user-generated online communication. References/combination with other methods of data collection: Hate speech is examined through content analysis and can be combined with comparative or experimental designs (Muddiman, 2017; Oz, Zheng, & Chen, 2017; Rowe, 2015). In addition, content analyses can be accompanied by interviews or surveys, for example to validate the results of the content analysis (Erjavec & Kovačič, 2012). Example studies: Research question/research interest: Previous studies have been interested in the extent of hate speech in online communication (e.g. in one specific online discussion, in discussions on a specific topic or discussions on a specific platform or different platforms in comparatively) (Döring & Mohseni, 2020; Poole, Giraud, & Quincey, 2020; Waseem & Hovy, 2016). Object of analysis: Previous studies have investigated hate speech in user comments for example on news websites, social media platforms (e.g. Twitter) and social live streaming services (e.g. YouTube, YouNow). Level of analysis: Most manual content analysis studies measure hate speech on the level of a message, for example on the level of user comments. On a higher level of analysis, the level of hate speech for a whole discussion thread or online platform could be measured or estimated. On a lower level of analysis hate speech can be measured on the level of utterances, sentences or words which are the preferred levels of analysis in automated content analyses. Table 1. Previous manual and automated content analysis studies and measures of hate speech Example study (type of content analysis) Construct Dimensions/variables Explanation/example Reliability Waseem & Hovy (2016) (automated content analysis) hate speech sexist or racial slur - - attack of a minority - - silencing of a minority   - criticizing of a minority without argument or straw man argument - - promotion of hate speech or violent crime - - misrepresentation of truth or seeking to distort views on a minority - - problematic hash tags. e.g. “#BanIslam”, “#whoriental”, “#whitegenocide” - - negative stereotypes of a minority - - defending xenophobia or sexism - - user name that is offensive, as per the previous criteria - -     hate speech - κ = .84 Döring & Mohseni (2020) (manual content analysis) hate speech explicitly or aggressively sexual hate e. g. “are you single, and can I lick you?” κ = .74; PA = .99 racist or sexist hate e.g. “this is why ignorant whores like you belong in the fucking kitchen”, “oh my god that accent sounds like crappy American” κ = .66; PA = .99     hate speech   κ = .70 Note: Previous studies used different inter-coder reliability statistics; κ = Cohen’s Kappa; PA = percentage agreement.   More coded variables with definitions used in the study Döring & Mohseni (2020) are available under: https://osf.io/da8tw/   References Döring, N., & Mohseni, M. R. (2020). Gendered hate speech in YouTube and YouNow comments: Results of two content analyses. SCM Studies in Communication and Media, 9(1), 62–88. https://doi.org/10.5771/2192-4007-2020-1-62 Erjavec, K., & Kovačič, M. P. (2012). “You Don't Understand, This is a New War! ” Analysis of Hate Speech in News Web Sites' Comments. Mass Communication and Society, 15(6), 899–920. https://doi.org/10.1080/15205436.2011.619679 Gagliardone, I., Gal, D., Alves, T., & Martínez, G. (2015). Countering online hate speech. UNESCO Series on Internet Freedom. Retrieved from http://unesdoc.unesco.org/images/0023/002332/233231e.pdf Muddiman, A. (2017). : Personal and public levels of political incivility. International Journal of Communication, 11, 3182–3202. Oz, M., Zheng, P., & Chen, G. M. (2017). Twitter versus Facebook: Comparing incivility, impoliteness, and deliberative attributes. New Media & Society, 20(9), 3400–3419. https://doi.org/10.1177/1461444817749516 Poole, E., Giraud, E. H., & Quincey, E. de (2020). Tactical interventions in online hate speech: The case of #stopIslam. New Media & Society, 146144482090331. https://doi.org/10.1177/1461444820903319 Rosenfeld, M. (2012). Hate Speech in Constitutional Jurisprudence. In M. Herz & P. Molnar (Eds.), The Content and Context of Hate Speech (pp. 242–289). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139042871.018 Rowe, I. (2015). Civility 2.0: A comparative analysis of incivility in online political discussion. Information, Communication & Society, 18(2), 121–138. https://doi.org/10.1080/1369118X.2014.940365 Waseem, Z., & Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In J. Andreas, E. Choi, & A. Lazaridou (Chairs), Proceedings of the NAACL Student Research Workshop. Ziegele, M., Koehler, C., & Weber, M. (2018). Socially Destructive? Effects of Negative and Hateful User Comments on Readers’ Donation Behavior toward Refugees and Homeless Persons. Journal of Broadcasting & Electronic Media, 62(4), 636–653. https://doi.org/10.1080/08838151.2018.153243

Incivility (Hate Speech/Incivility)

The variable incivility is an indicator used to describe violations of communication norms. These norms can be social norms established within a society, a culture or parts of a society (e.g. a social class, milieu or group) or democratic norms established within a democratic society. In this sense incivility is associated with behaviors that threaten a collective face or a democratic society, deny people their personal freedoms, and stereotype individuals or social groups. Furthermore, some scholars include impoliteness into the concept of incivility and argue that the two concepts have no clear boundaries (e.g. Seely, 2017). They therefore describe incivility as aggressive, offensive or derogatory communication expressed directly or indirectly to other individuals or parties. In many studies a message is classified as uncivil if the message contains at least one instance of incivility (e.g. one violent threat). The direction of an uncivil statement is coded as ‘interpersonal’/‘personal’ or ‘other-oriented’/‘impersonal’ or sometimes also as ‘neutral’, meaning it is not directed at any group or individual. Field of application/theoretical foundation: One unifying element to communication that is labelled as incivility is that it has to be a violation of an existing norm. Which norms are seen as violated depends on the theoretical tradition. Incivility research is related to theories on social norms of communication and conversation: conversational-maxims (Grice, 1975), face-saving concepts (Brown & Levinson, 1987; Goffman, 1989) or conversational-contract theories (Fraser, 1990). Further, incivility research has ties to theories that view public communication as part of democratic opinion formation and decision-making processes, e.g. theories on deliberative democracy and deliberation (Dryzek, 2000; Gutmann & Thompson, 1996; Habermas, 1994). References/combination with other methods of data collection: Incivility is examined through content analysis and sometimes combined with comparative designs (e.g., Rowe, 2015) or experimental designs (Muddiman, 2017; Oz, Zheng, & Chen, 2017). In addition, content analyses can be accompanied by interviews or surveys, for example to validate the results of the content analysis (Erjavec & Kovačič, 2012). Example studies: Research question/research interest: Previous studies have been interested in the extent, levels and direction of incivility in online communication (e.g. in one specific online discussion, in discussions on a specific topic, in discussions on a specific platform or on different platforms comparatively). Object of analysis: Previous studies have investigated incivility in user comments on political newsgroups, news websites, social media platforms (e.g. Twitter, Facebook), political blogs, science blogs or online consultation platforms. Timeframe of analysis: Many studies investigate incivility in user comments focusing on periods between 2 months and 1 year. It is common to use constructed weeks. Level of analysis: Most manual content analyses measure incivility on the level of a message, for example on the level of user comments. On a higher level of analysis, the level of incivility for a whole discussion thread or online platform can be measured or estimated. On a lower level of analysis incivility can be measured on the level of utterances, sentences or words which are the preferred levels of analysis in automated content analyses. Table 1. Previous manual content analysis studies and measures of incivility Example study Construct Dimensions/Variables Explanation/example Reliability Papacharissi (2004) incivility (separate from impoliteness) threat to democracy e.g. propose to overthrow a democratic government by force Ir = .89 stereotype e.g. association of a person with a group by using labels, whether those are mild – “liberal”, or more offensive – “faggot”)? Ir = .91 threat to other individuals’ rights e.g. personal freedom, freedom to speak Ir = .86 incivility Ir = .89 Coe, Kenski, and Rains (2014) incivility (impoliteness is included) name-calling mean-spirited or disparaging words directed at a person or group of people K-α = .67 aspersion mean-spirited or disparaging words directed at an idea, plan, policy, or behavior K-α = .61 reference to lying stating or implying that an idea, plan, or policy was disingenuous K-α = .73 vulgarity using profanity or language that would not be considered proper (e.g., “pissed”, “screw”) in professional discourse K-α = .91 pejorative for speech disparaging remark about the way in which a person communicates K-α = .74 incivility / impoliteness K-α = .73 Rowe (2015) incivility (separate from impoliteness) threat to democracy proposes to overthrow the government (e.g. proposes a revolution) or advocates an armed struggle in opposition to the government (e.g. threatens the use of violence against the government) κ = .66 threat to individual rights advocates restricting the rights or freedoms of certain members of society or certain individuals κ = .86 stereotype asserts a widely held but fixed and oversimplified image or idea of a particular type of person κ = .80 incivility κ = .77 Seely (2017) incivility(impoliteness is included) insulting language name calling and other derogatory remarks often seen in pejorative speech and aspersions K-α = .84 vulgarity e.g. “lazy f**kers”, “a**holes” K-α = 1 stereotyping of political party/ideology e.g. “typical lying lefties” K-α = .88 stereotyping using “isms”/discriminatory language e.g. “if we don’t get rid of idiotic Muslim theologies, we will have growing problems” K-α = 1 other stereotyping language e.g. “GENERALS LIKE TO HAVE A MALE SOLDIER ON THEIR LAP AT ALL TIMES.” K-α = .78 sarcasm e.g. “betrayed again by the Repub leadership . . . what a shock” K-α = .79 accusations of lying e.g. “typical lying lefties” K-α = .80 shouting excessive capitalization and/or exclamation points K-α = .83 incivility / impoliteness K-α = .81 Note: Previous studies used different inter-coder reliability statistics; Ir = reliability index by Perreault and Leigh (1989); K-α = Krippendorff’s-α; κ = Cohen’s Kappa   Codebook used in the study Rowe (2015) is available under: https://www.tandfonline.com/doi/full/10.1080/1369118X.2014.940365   References Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language usage. Cambridge: Cambridge University Press. Coe, K., Kenski, K., & Rains, S. A. (2014). Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments. Journal of Communication, 64(4), 658–679. https://doi.org/10.1111/jcom.12104 Dryzek, J. S. (2000). Deliberative democracy and beyond: Liberals, Critics, Contestations. Oxford political theory. Oxford, New York: Oxford University Press. Erjavec, K., & Kovačič, M. P. (2012). “You Don't Understand, This is a New War! ” Analysis of Hate Speech in News Web Sites' Comments. Mass Communication and Society, 15(6), 899–920. https://doi.org/10.1080/15205436.2011.619679 Fraser, B. (1990). Perspectives on politeness. Journal of Pragmatics, 14(2), 219–236. https://doi.org/10.1016/0378-2166(90)90081-n Goffman, E. (1989). Interaction ritual: Essays on face-to-face behavior. New York: Pantheon Books. Grice, P. H. (1975). Logic and conversation. In P. Cole (Ed.), Syntax and Semantics: Speech acts (pp. 41–58). New York: Academic Press. Gutmann, A., & Thompson, D. F. (1996). Democracy and disagreement. Cambridge, Massachusetts: Belknap Press of Harvard University Press. Habermas, J. (1994). Three Normative Models of Democracy. Constellations, 1(1), 1–10. Muddiman, A. (2017). : Personal and public levels of political incivility. International Journal of Communication, 11, 3182–3202. Oz, M., Zheng, P., & Chen, G. M. (2017). Twitter versus Facebook: Comparing incivility, impoliteness, and deliberative attributes. New Media & Society, 20(9), 3400–3419. https://doi.org/10.1177/1461444817749516 Papacharissi, Z. (2004). Democracy online: Civility, politeness, and the democratic potential of online political discussion groups. New Media & Society, 6(2), 259–283. https://doi.org/10.1177/1461444804041444 Rowe, I. (2015). Civility 2.0: A comparative analysis of incivility in online political discussion. Information, Communication & Society, 18(2), 121–138. https://doi.org/10.1080/1369118X.2014.940365 Seely, N. (2017). Virtual Vitriol: A Comparative Analysis of Incivility Within Political News Discussion Forums. Electronic News, 12(1), 42–61. https://doi.org/10.1177/193124311773906

Impoliteness (Hate Speech/Incivility)

The variable impoliteness is an indicator used to describe violations of communication norms. These norms can be social norms established within a society, a culture or parts of a society (e.g. a social class, milieu or group). In this sense impoliteness is associated with, among other things, aggressive, offensive or derogatory communication expressed directly or indirectly to other individuals or parties. More specifically name calling, vulgar expressions or aspersions are classified as examples of impolite statements  (e.g. Papacharissi, 2004; Seely, 2017). While some scholars distinguish between impoliteness and incivility and argue that impoliteness is more spontaneous, unintentional and more frequently regretted than incivility (e.g. Papacharissi, 2004; Rowe, 2015), other scholars include impoliteness into the concept of incivility and argue that the two concepts have no clear boundaries (Coe, Kenski, & Rains, 2014; e.g. Seely, 2017). In many studies a message is classified as impolite if the message contains at least one instance of impoliteness (e.g. a swear word). The direction of an impolite statement is coded as ‘interpersonal’/‘personal’ or ‘other-oriented’/‘impersonal’ or sometimes also as ‘neutral’, meaning it is not directed at any group or individual. Field of application/theoretical foundation: Impoliteness is a broader concept of violations of norms in communication that, in digital communication research, is often referred to in studies on incivility. Politeness can be related to theories on social norms of communication and conversation, for example conversational-maxims (Grice, 1975), face-saving concepts (Brown & Levinson, 1987; Goffman, 1989) or conversational-contract theories (Fraser, 1990). References/combination with other methods of data collection: Impoliteness is examined through content analysis and is sometimes combined with comparative designs (e.g., Rowe, 2015) or experimental designs (Muddiman, 2017; Oz, Zheng, & Chen, 2017). In addition, content analyses can be accompanied by interviews or surveys, for example to validate the results of the content analysis (Erjavec & Kovačič, 2012). Example studies: Research question/research interest: Previous studies have been interested in the extent, levels and direction of impoliteness in online communication (e.g. in one specific online discussion, in discussions on a specific topic, in discussions on a specific platform or on different platforms comparatively). Object of analysis: Previous studies have investigated impoliteness in user comments on political newsgroups, news websites, social media platforms (e.g. Twitter, Facebook), political blogs, science blogs or online consultation platforms. Timeframe of analysis: Content analysis studies investigate impoliteness in user comments focusing on periods between 2 months and 1 year (Coe et al., 2014; Rowe, 2015; Seely, 2017). It is common to use constructed weeks. Level of analysis: Most manual content analysis studies measure impoliteness on the level of a message, for example on the level of user comments. On a higher level of analysis, the level of impoliteness for a whole discussion thread or online platform could be measured or estimated. On a lower level of analysis impoliteness can be measured on the level of utterances, sentences or words which are the preferred levels of analysis in automated content analyses. Table 1. Previous manual content analysis studies and measures of impoliteness Example study Construct Dimensions/Variables Explanation/example Reliability Papacharissi (2004) impoliteness (separate from incivility) name-calling e.g. “weirdo”, “traitor”, “crackpot” Ir = .91 aspersion e.g. “reckless”, “irrational”, “un-American” Ir = .91 synonyms for liar e.g. “hoax”, “farce” N/A hyperboles e.g. “outrageous”, “heinous” N/A non-cooperation - N/A pejorative speak - N/A vulgarity e.g. ”shit”, “damn”, “hell” Ir = .89 sarcasm - N/A all-capital letters used online to reflect shouting N/A impoliteness Ir = .90 Coe et al. (2014) impoliteness (included in incivility) name-calling mean-spirited or disparaging words directed at a person or group of people K-α = .67 aspersion mean-spirited or disparaging words directed at an idea, plan, policy, or behavior K-α = .61 reference to lying stating or implying that an idea, plan, or policy was disingenuous K-α = .73 vulgarity using profanity or language that would not be considered proper (e.g., “pissed”, “screw”) in professional discourse K-α = .91 pejorative for speech disparaging remark about the way in which a person communicates K-α = .74 impoliteness/incivility K-α = .73 Rowe (2015) impoliteness (separate from incivility) name-calling e.g., “gun-nut”, “idiot”, “fool” κ = .82 aspersion comments containing an attack on the reputation or integrity of someone or something κ = .72 lying   comments implying disingenuousness N/A vulgarity e.g., “crap”, “shit”, any swear-words/cursing, sexual innuendo κ = 1 pejorative comments containing language which disparage the manner in which someone communicates (e.g., blather, crying, moaning) κ = 1 hyperbole a massive overstatement (e.g., makes pulling teeth with pliers look easy) κ = .75 non-cooperation a situation in a discussion in terms of a stalemate κ = .66 sarcasm - κ = .71 other impoliteness any other type of impoliteness κ = .72 impoliteness κ = .78 Seely (2017) impoliteness (included in incivility) insulting language name calling and other derogatory remarks often seen in pejorative speech and aspersions K-α = .84 vulgarity e.g. “lazy f**kers”, “a**holes” K-α = 1 stereotyping of political party/ideology e.g. “typical lying lefties” K-α = .88 stereotyping using “isms”/discriminatory language e.g. “if we don’t get rid of idiotic Muslim theologies, we will have growing problems” K-α = 1 other stereotyping language e.g. “GENERALS LIKE TO HAVE A MALE SOLDIER ON THEIR LAP AT ALL TIMES.” K-α = .78 sarcasm e.g. “betrayed again by the Repub leadership . . . what a shock” K-α = .79 accusations of lying e.g. “typical lying lefties” K-α = .80 shouting excessive capitalization and/or exclamation points K-α = .83 impoliteness/incivility K-α = .81 Note: Previous studies used different inter-coder reliability statistics: Ir = reliability index by Perreault and Leigh (1989); K-α = Krippendorff’s-α; κ = Cohen’s Kappa   Codebook used in the study Rowe (2015) is available under: https://www.tandfonline.com/doi/full/10.1080/1369118X.2014.940365   References Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language usage. Cambridge: Cambridge University Press. Coe, K., Kenski, K., & Rains, S. A. (2014). Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments. Journal of Communication, 64(4), 658–679. https://doi.org/10.1111/jcom.12104 Erjavec, K., & Kovačič, M. P. (2012). “You Don't Understand, This is a New War! ” Analysis of Hate Speech in News Web Sites' Comments. Mass Communication and Society, 15(6), 899–920. https://doi.org/10.1080/15205436.2011.619679 Fraser, B. (1990). Perspectives on politeness. Journal of Pragmatics, 14(2), 219–236. https://doi.org/10.1016/0378-2166(90)90081-n Goffman, E. (1989). Interaction ritual: Essays on face-to-face behavior. New York: Pantheon Books. Grice, P. H. (1975). Logic and conversation. In P. Cole (Ed.), Syntax and Semantics: Speech acts (pp. 41–58). New York: Academic Press. Muddiman, A. (2017). : Personal and public levels of political incivility. International Journal of Communication, 11, 3182–3202. Oz, M., Zheng, P., & Chen, G. M. (2017). Twitter versus Facebook: Comparing incivility, impoliteness, and deliberative attributes. New Media & Society, 20(9), 3400–3419. https://doi.org/10.1177/1461444817749516 Papacharissi, Z. (2004). Democracy online: Civility, politeness, and the democratic potential of online political discussion groups. New Media & Society, 6(2), 259–283. https://doi.org/10.1177/1461444804041444 Rowe, I. (2015). Civility 2.0: A comparative analysis of incivility in online political discussion. Information, Communication & Society, 18(2), 121–138. https://doi.org/10.1080/1369118X.2014.940365 Seely, N. (2017). Virtual Vitriol: A Comparative Analysis of Incivility Within Political News Discussion Forums. Electronic News, 12(1), 42–61. https://doi.org/10.1177/193124311773906

Online-Partizipation jenseits klassischer Deliberation: Eine Analyse zum Verhältnis unterschiedlicher Deliberationskonzepte in Nutzerkommentaren auf Facebook-Nachrichtenseiten und Beteiligungsplattformen

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Online Deliberation in Academia: Evaluating the Quality and Legitimacy of Cooperatively Developed University Regulations

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Content Analysis in the Research Field of Incivility and Hate Speech in Online Communication

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Capturing Citizens’ Values: On the Role of Narratives and Emotions in Digital Participation

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Crisis Talks: The Framing of the Ukraine Crisis on German Talk Show Debates

Following the controversial incorporation of Crimea into Russia in spring 2014 andthe subsequent economic sanctions imposed by the US and the EU, the Ukrainecrisis marks a sea change in international relations between Russia and the EU(Bacon, 2015). The international tensions surrounding the crisis Impact the globaleconomy, complicate cooperation between EU countries and Russia in relation tomilitary actions in Syria and demonstrate the fragility of peace in Europe. Warningof the danger of further escalation, many commentators draw parallels with theCold War (Bovt, 2015; Legvold, 2014) and the outbreak of the First World Warin 1914, when a local conflict turned into a bot war between the major powers onthe r:uropean continent (Clark, 2013)

Das Netz der Andersdenkenden? Eine Inhaltsanalyse der Online-Diskussionen zur Wulff-Affäre auf Nachrichtenseiten, in Weblogs und bei Facebook

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Kommunikationsformen und Deliberationsdynamik: Eine relationale Inhalts- und Sequenzanalyse politischer Online-Diskussionen auf Beteiligungsplattformen [Forms of communication and deliberation dynamics: A relational content and sequence analysis of online political discussions on participation platforms]

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