(1 = yes; 0 = no). The petition motive may buy GDC-0084 influence the amount of online aggression. Using a petition’s title and leading text, two independent coders classify the petitions with regard to their underlying motives by using the classification by Reiss [95]. Five major concerns are identified, namely idealism/fairness (42 ), income/costs (19 ), security/social order (13 ), autonomy/self-determination (14 ), and quality of life/competences (52 ). Multiple assignments of petitions are possible. Idealism/ fairness serves as the reference group in the regression models. Similarly, the petition topic may influence anonymity considerations and the amount of aggression. Depending on the societal area, be it the economy, politics, or culture, accused actors may differ in their thresholds of wanting to sue aggressive online commenters. Commenters may anticipate these thresholds and the related differing costs of being aggressive. This in turn affects commenters’ actual behavior. Using a petition’s title and leading text, two independent coders classify the petitions with regard to their underlying topics using the functional systems of a society [96]. Six major topics are identified, namely society (41 ), arts (20 ), economics (13 ), politics (8 ), media (8 ), and environment and animal protection (8 ). Multiple assignments of petitions are avoided. Society, including topics such as sport or solidarity, is the most general category and serves as reference group in the regression models. For the summary of the descriptive statistics and bivariate correlations of the former variables, see S1 Table.MethodsWe apply random-effects and fixed-effects models to predict online aggression in petitions (for access to data, syntax, and Permission for using data of openpetition.de, see the Data availability statement). In both models the comments are grouped on the petition level. The randomeffects model keeps within- and between-petition variation in the analysis. We assume that petitions vary not only within, but also between, each other, for example because some petitions have many supporters while other petitions have only a few supporters, or because of differences in the underlying goals and motives. We analyze whether online aggression within and between petitions changes when other variables within and between the petitions change. The fixed-effects model keeps only within-petition variation in the analysis. We also analyze whether the aggression within petitions changes when other variables change, for example the anonymity of commenters, the amount of AM152MedChemExpress AM152 intrinsic motivation or the amount of selective incentives within the petitions. Many variables of our dataset are time-invariant, i.e., constant petition features that do not vary on the petition level. In the fixed-effects model these variables are omitted. Both models have advantages as well as disadvantages. The fixed-effects model excludes all random noise between the petitions and is therefore often preferred as the golden standard. However, differences between the petitions, for example the number of supporters, may also be important in explaining negative word-of-mouth behavior within petitions. This speaks in favor of the random-effects model. We therefore apply both models and compare thePLOS ONE | DOI:10.1371/journal.pone.0155923 June 17,10 /Digital Norm Enforcement in Online Firestormsresults. We additionally run alternative conceivable models for the data structure, for example, logistic.(1 = yes; 0 = no). The petition motive may influence the amount of online aggression. Using a petition’s title and leading text, two independent coders classify the petitions with regard to their underlying motives by using the classification by Reiss [95]. Five major concerns are identified, namely idealism/fairness (42 ), income/costs (19 ), security/social order (13 ), autonomy/self-determination (14 ), and quality of life/competences (52 ). Multiple assignments of petitions are possible. Idealism/ fairness serves as the reference group in the regression models. Similarly, the petition topic may influence anonymity considerations and the amount of aggression. Depending on the societal area, be it the economy, politics, or culture, accused actors may differ in their thresholds of wanting to sue aggressive online commenters. Commenters may anticipate these thresholds and the related differing costs of being aggressive. This in turn affects commenters’ actual behavior. Using a petition’s title and leading text, two independent coders classify the petitions with regard to their underlying topics using the functional systems of a society [96]. Six major topics are identified, namely society (41 ), arts (20 ), economics (13 ), politics (8 ), media (8 ), and environment and animal protection (8 ). Multiple assignments of petitions are avoided. Society, including topics such as sport or solidarity, is the most general category and serves as reference group in the regression models. For the summary of the descriptive statistics and bivariate correlations of the former variables, see S1 Table.MethodsWe apply random-effects and fixed-effects models to predict online aggression in petitions (for access to data, syntax, and Permission for using data of openpetition.de, see the Data availability statement). In both models the comments are grouped on the petition level. The randomeffects model keeps within- and between-petition variation in the analysis. We assume that petitions vary not only within, but also between, each other, for example because some petitions have many supporters while other petitions have only a few supporters, or because of differences in the underlying goals and motives. We analyze whether online aggression within and between petitions changes when other variables within and between the petitions change. The fixed-effects model keeps only within-petition variation in the analysis. We also analyze whether the aggression within petitions changes when other variables change, for example the anonymity of commenters, the amount of intrinsic motivation or the amount of selective incentives within the petitions. Many variables of our dataset are time-invariant, i.e., constant petition features that do not vary on the petition level. In the fixed-effects model these variables are omitted. Both models have advantages as well as disadvantages. The fixed-effects model excludes all random noise between the petitions and is therefore often preferred as the golden standard. However, differences between the petitions, for example the number of supporters, may also be important in explaining negative word-of-mouth behavior within petitions. This speaks in favor of the random-effects model. We therefore apply both models and compare thePLOS ONE | DOI:10.1371/journal.pone.0155923 June 17,10 /Digital Norm Enforcement in Online Firestormsresults. We additionally run alternative conceivable models for the data structure, for example, logistic.
