Online Trolling, Victim Speech, and Bystander Mood 1

Running head: ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 1

Effect of Online Trolling and Victim Speech on Bystanders’ Mood

Daniël van den Corput

Tilburg University ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 2

Abstract

Despite the growing interest in trolling as an academic subject, research has mainly been theoretical and conducted by second hand data collection. Research in this field challenges with theoretical discrepancies and still requires empirical validation of theoretical data. Moreover, while prevention programs for cyberbullying are gradually coming into existence, it has not yet been applied to trolling, despite the similar negative consequences. Therefore, this study aims to experimentally explore the effects of in-team trolling victimization and the effect of victim speech on mood of bystanders in the competitive online game League of Legends. Congruently with prior non-experimental research, results indicated that participant mood increased by playing a regular game, whereas mood decreased after witnessing trolling victimization. No significant effect of victim speech on bystanders’ mood was found. Theoretical implications in relation to emotional contagion and the general strain theory are discussed. Victim agency is portrayed within a trolling and bystander context. The study is concluded with possible explanations and limitations of the found results and methods. Suggestions for future research regarding bystanders in trolling victimization and victim speech are proposed.

Keywords. Bystanders, Online Gaming, Trolling, Victim Speech

ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 3

Effects of Online in-Team Trolling and Victim Speech on Mood of Bystanders

Bullying is arguably the most extensively-researched form of harassment, with numerous studies regarding different aspects of bullying such as motivations of all concerned parties

(Gasser & Keller, 2009) and intervention programs (Merrell, Gueldner, Ross & Isava, 2008).

Now, since the internet has become ubiquitous as of the start of the 21st century, the emergence of the problem of bullying has repeated itself in an online manner (Patchin & Hinduja, 2006).

The technology that we use enables us to substantially diminish the time-distance barrier that is traditionally faced, while significantly widening the target audience and creating a sense of anonymity (Slonje & Smith, 2008; Hardaker, 2010). This kind of bullying, called cyberbullying, involves using online messaging, websites, emails and social networks to spread a wide variety of content to purposely harm other people (Nicol, 2012). One phenomenon that is similar to cyberbullying is called trolling. People who engage in trolling, i.e. trolls, are characterized as people who communicate online with provocative, offensive or menacing intentions in order to create conflicts and provoke distress for their own enjoyment (March, Grieve, Marrington &

Jonason, 2017). While trolling has been studied by means of surveys, interviews and second data collection, very few experimental studies have been conducted to investigate trolling behavior.

Moreover, experimental studies in which participants experience trolling first hand have not been conducted at all, even though trolls have been identified as disruptive (Hardaker, 2010), transgressive, subversive (Bishop, 2014) and even criminals (Shin, 2008). On top of that, trolling victims, who are typically part of a minority group (Herring, Job-Sluder, Scheckler & Barab,

2002), often times experience harassment and discrimination (Herring, Job-Sluder, Scheckler &

Barab, 2002), calling for support- and intervention programs similar to those of traditional- and cyberbullying. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 4

Bystander research with regards to cyberbullying is prevalent, with research ranging from consequences of witnessing cyberbullying (e.g. Schenk & Fremouw, 2012; Hinduja & Patchin,

2007) to the effect bystanders can have on cyberbullying behavior, such as intervening (e.g.

Bastiaensens, Vandebosch, Poels, van Cleemput, Desmet & De Bourdeaudhuij, 2014) or engaging in hostile behavior (Neff, 2013). Despite this extensive framework of bystander research within a cyberbullying environment, bystander research with respect to trolling is scarce. The present study aims to contribute to the lack of experimental research, in particular with regards to bystanders, by experimentally examining the consequences of witnessing trolling behavior in an online environment. To do so, an online environment in the game that is conceivably frequented by online gaming’s most toxic community, League of Legends (LoL;

Kou & Nardi, 2014), will be used to investigate how in-game team trolling behavior influences the mood of bystanders. Results of this study will contribute to facing these problems by answering the following question: How does in-game team communication affect emotional response of players in a competitive online game?

Theoretical background

Online gaming

Presently, effects of trolling on bystanders will be explored in the scope of the game accompanied with online gaming’s most toxic community: League of Legends (Kou & Nardi,

2014). LoL is a multiplayer online battle arena (MOBA): an online matching game in which players have to compete with each other in teams (Park & Kim, 2014). In LoL, players control different kinds of characters, all with unique abilities to ‘kill’ other players and non-player characters in order to gain virtual money and experience points. Castranova (2008) examined human interactions within massive multiplayer online role-playing games (MMORPG) and ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 5 argued that the boundary between these MMORPG’s and the real world has become ambiguous for many players. Some virtual currencies are worth more than some real currencies, new virtual worlds are arising at the rate of Moore’s Law and numerous lawsuits have been filed (and won) by players who were mistreated in their virtual environment (Castranova, 2008). Castranova

(2008) continues by stating that twenty percent of users claim that the online environment in which they reside is perceived as their real world. While Johnson, Nacke and Wyeth (2015) state that players in MOBA’s are typically more frustrated due to the higher degree of competitiveness than players in MMORPG’s, players still exhibit teamwork-driven interactions similar to those exhibited in MMORPG’s.

In these and most other online games, players can interact with each other via computer mediated communication (CMC). CMC is a text-based type of communication, which is heavily reliant of the system and interface design on which it is broadcasted (Romiszowski, & Mason,

1996). One might think that users are limited in transferring emotions via CMC since it is text based, but Vandergriff (2013) as well as Riordan and Kreuz (2010) found that individuals elicit just as many ‘nonverbal’ cues via CMC as with face to face (F2F) communication. These cues are mainly purported to indicate emotion, disambiguate an utterance, or express contextual cues and implicit meanings. In CMC, individuals achieve the same effect by applying other cues like emoticons, typographic markers and lexical surrogates (Vandergriff, 2013; Riordan & Kreuz,

2010). Derks, Fischer and Bos (2008) even argued that more emotions are conveyed via CMC than via F2F, as people tend to disambiguate their expressions more often via CMC. They also state that negative emotions are communicated more efficiently via CMC, as people sense less negative appraisals of others (Derks, Fischer & Bos, 2008). Individuals feel more anonymous when communicating via CMC and consequently tend to be more direct and explicit, leading to ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 6 more negative and unconcealed expressions (Derks, Fischer & Bos, 2008). Consequently, participants are likely to be affected by witnessing trolling, as virtual environments such as LoL are attended with interpersonal relationships, communities and demeanors similar to those in the real world.

On the other hand, research has shown that playing casual video games can be held responsible for improving mood and decreasing stress (Russoniello, O’Brien & Parks, 2009).

Also of note is the finding that playing a violent videogame resulted in more arousal among children in relation to a nonviolent game (Fleming, Wood & Debra, 2001). Even though LoL is not classified as a casual game according to the conditions provided by Russoniello and colleagues (2009), its popularity implies that it is considered as enjoyable by a vast amount of players from all over the world. Consequently, Mood of participants is possibly affected by the arousal induced by playing a game of LoL.

Trolling

In the present study, trolls are considered to be CMC users with a fake constructed identity that appear to wish to be part of a particular group, but in reality want to cause disruption and/or trigger conflicts for their own amusement (Hardaker, 2010). This definition is by no means exhaustive in all contexts as disagreements regarding different characteristics are still the subject of present studies, but it is exhaustive enough in the scope of this study. The definition of trolling is the subject of great discussion, as many studies differ in certain characteristics that are either included or excluded. Coles and West (2016) dedicated an entire study to the ambiguity of the terms ‘troll’ and ‘trolling’ by conducting an analysis regarding the most common use of both words among users and in online communities. They concluded by stating that neither the words

‘troll’ nor ‘trolling’ can be imposed as a constant. For one, trolls are mostly considered as ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 7 undesirable; they are seen as liars, and take advantage of others and are irrational (Coles & West,

2016), which seems to be in accordance with the general trend in present literature. Trolls are characterized as people who communicate online with provocative, offensive or menacing intentions in order to create conflicts and provoke distress for their own enjoyment (March,

Grieve, Marrington & Jonason, 2017); apply transgressive and subversive behavior at the expense of others (Bishop, 2014); and are considered incisive (Cook, Schaafsma & Antheunis,

2016; Coles & West, 2016; Bishop, 2014).

Another important fact to note is trolling’s undesirability in the community. It must, however, be acknowledged that trolls are not necessarily bad people, and that everyone can engage in trolling behavior. Cheng, Bernstein, Danescu-Niculescu-Mizil and Leskovec (2017) as well as Cook, Schaafsma and Antheunis (2016) affirm that anyone can be a troll, depending on their mood and the presence of other trolls in their environment. Trolling is, in fact, described as something situational and determined by the environment in both studies, as trolling cycles are reinforced by the response that is given to the trolling behavior by bystanders and are triggered by the occurrence and recognition of other trolling behavior (Cheng, et al., 2017; Cook et al.,

2016). According to these studies, trolling behavior can be elicited by the mere presence of trolling messages. Secondly, trolling behavior likely elicits bystander actions. It is difficult to precisely determine whether individuals are likely to take action when witnessing trolling behavior, as this subject has been rarely addressed in the literature. Moreover, research regarding different bystander actions in cyberbullying research is complex and filled with discrepancies.

For example, while Neff (2013) found that bystanders are more likely to engage in hostile behaviors when a hostile in-group environment is established, findings in the studies conducted by Bastiaensens and colleagues (2014) and by Obermaier, Fawzi and Koch (2016) directly ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 8 oppose this. Results of the latter two studies indicated that bystanders are actually more likely to help victims when victims are cyberbullied more severely within a hostile environment. The contradictions in cyberbullying research and the lack of research regarding bystander actions in trolling environments makes it difficult to predict whether participants in the experimental conditions are more likely to engage in trolling behavior, intervene when they witness their teammate being trolled, or do nothing at all. The present study aims to contribute to the lack of knowledge about bystander actions in trolling environments.

It is important to consider the potential effects of witnessing trolling behavior in online environments, since bystanders are likely to be affected by the consequences that victims face when being trolled. It remains unsure whether witnesses of trolling victimization face similar consequences as witnesses of cyberbullying, but given the similarities between the two it is plausible that some effects are true for both. Cyberbullying witnesses alongside with victims have more chance of being depressed (Baker & Tanrikulu, 2010), show more anxious and paranoid symptoms (Schenk & Fremouw, 2012) and experience more school-related problems, substance use, and assaultive behavior (Hinduja & Patchin, 2007) even though the physical well- being of the victims or bystanders is not directly affected. On the other hand, Thacker and

Griffiths (2012) found that bystanders experience an increase in self-esteem when witnessing trolling behavior, making it feasible that participants in the experimental conditions will be affected by witnessing trolling victimization after participating in the experiment. However, self- esteem and mood are not the same constructs, even though they are often investigated in the same studies, especially in subjects like body dissatisfaction (e.g. Paxton, Neumark-Sztainer,

Hannan & Eisenberg, 2006) and stress (e.g. Martyn-Nemeth, Penckofer, Gulanick, Velsor- ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 9

Friedrich & Bryant, 2009). As such, it remains unsure whether this will also positively affect participants’ mood.

Another facet of the gaming experience that may contribute to a change in emotional responses of participants is the effect of mood contagion. This widely acknowledged phenomenon has, for example, been used to explain why listeners automatically adopt emotions when these are expressed by a speaker (Neumann & Strack, 2000). Even when the speaker had no intention to elicit emotions and when no emotional information was provided, emotional contagion still occurred. Furthermore, this effect was also found in large-scale networks like

Facebook (Kramer, Guillory & Hancock, 2014). Kramer and colleagues found that emotions of social network users are altered and influenced by the emotional states that are perceived on these networks. These results indicate that emotional contagion does not even require interpersonal communication and the cues that go along with it to be effective.

Given the negative consequences that cyberbullying behavior has on victims and bystanders and the power of emotional contagion to negatively influence the mood of bystanders after experiencing victimization, all while taking into account the effectiveness of CMC to transfer mood and emotions, it is expected that bystanders’ mood will be negatively influenced when they experience someone in their team being trolled. Therefore:

H1: Participants’ mood will be negatively influenced after witnessing trolling victimization.

Victim speech

Besides the effect that witnessing trolling behavior has on bystanders’ mood, this study will also consider whether this effect differs for bystanders that witness trolling behavior accompanied by victim speech. Experiments regarding the effect of victim speech on mood of ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 10 bystanders have not yet been conducted, but based on two influential theories regarding the relationship between experiencing unpleasant situations and the effect that has on mood, an effect is anticipated. The general strain theory (GST; Agnew, 2006) for one, states that experiencing stressing situations, such as being cyberbullied or trolled, will lead to a decrease in well-being, resulting in unhealthy effects and negative consequences such as an increase of the likelihood to commit a crime. Stressful situations may cause a lower sense of well-being in individuals, making them more prone to misconduct in order for them to cope with the stress

(Agnew, 2006). The theory has been used to explain a wide range of consequences after experiencing stress, in which it is essential to consider the fact that coping mechanisms differ tremendously per individual (Agnew, 2006). Previous research has used the GST to explain the connection between experiencing cyberbullying and future acts of aggression (Hay, Meldrum &

Mann, 2010), to identify the relationship between offline bully victimization and engagement in cyberbullying behavior (Jang, Song & Kim, 2014), and to portray the link between experiencing stress and engagement in bullying behavior (Patchin & Hinduja, 2011). Given this theoretical framework regarding bullying and cyberbullying, effects explained by the GST are conceivably also true within the context of trolling victimization.

While the GST has been applied extensively to explain a broad range of negative consequences in individuals who were victimized, it has not yet been used to explain possible effects on bystanders of victimization. In spite of this lack of bystander literature with respects to the GST, it is still possible that the theory can be applied to bystanders as well as victims.

Cyberbully research that regards consequences that are true for both victims and bystanders rely on the fact that bystanders get influenced just by experiencing someone else being cyberbullied

(e.g. Baker & Tanrikulu, 2010; Schenk & Fremouw, 2012; Hinduja & Patchin, 2007). With this ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 11 in mind, it can be expected that the consequences explained by the GST also have an effect on bystanders, as bystanders will experience the strain that the victim has to deal with. When participants witness trolling victimization along with victim speech, more emotional information about the well-being of the victim is shared in comparison to trolling victimization without victim speech. Consequently, bystanders will be more aware of the strain that victims experience and will therefore be more aware of the stressors. Since cyberbullying bystander literature is based on the fact that bystanders get influenced just by experiencing someone else being cyberbullied, these perceived stressors are then likely to influence the mood of bystanders. The conditions in which victim speech is present will therefore likely have a greater effect on bystanders than the conditions without victim speech due to the perceived stress of victims.

Secondly, the Ripple effect (Barsade, 2002) states that emotional contagion constantly changes people’s moods, judgements and, as a consequence, behavior. As seen before, emotions are subconsciously adopted by listeners (Neumann & Strack, 2000), and emotional contagion occurs on large-scale networks (Kramer, Guillory & Hancock, 2014). The Ripple effect explains the negative consequences that bystanders experience when they witness someone else being trolled. According to the Ripple effect, as victims express their ill-being when they are being victimized, bystanders will adopt that mood and will consequently feel bad for themselves. As mentioned earlier, emotional contagion research suggests that CMC is actually capable of transferring these emotions (Vandergriff, 2013; Riordan & Kreuz, 2010; Derks, Fischer & Bos,

2008). Based on the GST and the Ripple effect, witnessing trolling victimization alongside with victim speech will result in more information about the emotional ill-being of the victim.

Participants that experience victim speech will therefore be more aware of the stressor and are ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 12 consequently affected to a greater extent than participants who experience victimization without victim speech. For these reasons:

H2: Participants in the victim speech condition will experience a decrease in mood posttest.

Methods

Participants

In total, 55 participants started the questionnaire, of which 41 were ultimately included in the analysis. Of the seven participants that were not included in the analysis, three did not consent with the informed consent text (included as Appendix B together with the first questionnaire). The remaining four participants did accept the informed consent text, but either provided no further answers at all or too few answer to be meaningful in the analysis. Of the 41 eventual participants, 39 (95.1%) were male and 3 (4.9%) were female (M age = 21.9, SD =

3.02), with a maximum age of 30 and a minimum age of 17. Participants were mostly recruited by online means, either via social media sites like Facebook and Reddit or by offline gaming events where potential players could sign themselves up for the study. The text used as recruitment text is added as Appendix A. Most participants were Dutch (N = 26), German (N =

5) or from another European country (N = 7). The other three participants were from Egypt,

South Africa and Bahrain. Prior LoL experience and LoL play frequency of the participants can be found in Table 1, in which is seen that most were playing LoL for four to six years and that none were playing for less than 6 months at the time of the experiment. Furthermore, most participants either played daily or several times a week and only six participants did not have an account with a competitive rank. The majority of players also played other online multiplayer games, of which World of Warcraft, Counter-Strike and Hearthstone were the most common. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 13

Table 1

Prior LoL experience and LoL play frequency Prior LoL experience N LoL play frequency N > 1 month 0 > Once a month 5 1-6 months 0 Once a month 2 6 months – 1 year 2 Several times a month 6 1-2 years 2 Once a week 3 2-4 years 8 Several times a week 14 4-6 years 20 Daily 11 6-8 years 8 8+ years 1

Materials

Pre-test. Prior to the experiment, participants were directed through Qualtrics and instructed to complete Questionnaire 1. This questionnaire consisted respectively of demographic questions (age, gender, nationality, English proficiency, level of education, LoL play frequency, LoL experience and skill level and experience with other online multiplayer games), and the PANAS mood scale (Watson, Clark & Tellegen, 1988).

PANAS. Due to the lack of reliable and valid mood scales that are also easily applicable,

Watson, Clark and Tellegen (1988) created this highly internally consistent PANAS mood scale.

The scale aims to measure underlying mood factors by means of ten-item scales for both negative and positive affect. General internal validity of the scale of both positive and negative affect are high, respectively α = .88 and α = .87, and are moreover quasi-independent, with -.17 intercorrelation between the two scales. Retest reliability, measured after an eight-week interval, is moderately high for both positive and negative affect, respectively R = .68 and R = .71.

General convergent and discriminant validity for both scales are also high, respectively .94 and

-.10. The two dimensions account for essentially all common variance in the terms, also resulting in a high item validity. With all of this taken into account, the scale can be considered as reliable, ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 14 valid, and efficient (Watson, Clark & Tellegen, 1988) and was therefore used in the present study. The scale points of the items were labeled from one through five, one being at the left with the additional label not at all and five being at the right with the label extremely. For example, participants were asked to what extent each item represented how they felt at the time of filling in the survey, such as ‘interested’, ‘distressed’, ‘excited’ and ‘upset’. Even though only the first and the last items were labelled, identical results are still expected, as varied descriptors formerly resulted in identical results (Watson, Clark & Tellegen, 1988). The full questionnaire, including the entire PANAS scale, is presented in Appendix B.

League of Legends. Subsequently, the experiment was ran online in the game League of

Legends (Riot Games, 2009). Independent of condition, participants played a custom game on a team with two other players versus three AI-controlled opponents. Whereas the original game mode, called Summoner’s Rift, is played by five human players versus five human players, this custom game, called Twisted Treeline, was played by three players versus three computer controlled bots. The bots always consisted of the champions ‘Alistar’, ‘Annie’ and ‘Ashe’ and were always set to difficulty ‘Beginner’. Unbeknownst to the participant, however, the two other players on their team were always two confederates: one playing as the victim and one playing as the troll, depending on the condition to which the participants were randomly assigned to.

Confederates noted on an online document shared between researchers whether or not the participant spoke and if any remarkable events took place during the match. These matches were observed in ‘spectator mode’, a feature of the game client, by a third user: the primary researcher. This was done to preserve the illusion of three participants instead of one.

Post-test. Finally, participants completed a second questionnaire, which contained an identical copy of the first PANAS scale, questions regarding teammate evaluation, group ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 15 evaluation and user experience. Participants evaluated their teammates and the level of group cohesion on their team using two separate scales, and discussed their experience during the game via open-answer questions. The entire second questionnaire is added as Appendix C. Although the present study focuses only on the results of the PANAS mood scale, the other questions listed above were still part of the larger study in which the present study was a part.

Design

Participants were randomly assigned to one of four conditions, which were based on two manipulations: whether or not troll speech was present and whether or not the victim was speaking. Classification of the four conditions can be found in Table 2. The codes found in Table

2 will be used from this point on.

Table 2 Classification of different conditions No trolling speech Trolling speech No victim speech C1 C3 Victim speech C2 C4

Conditions C1 and C2 are both control conditions. Because there was no trolling content in either of these conditions, bystanders’ reactions to chat alone can be measured by comparing these two conditions. Condition C1 contains information about what effect playing a game of LoL has on participants, condition C2 contains information about what additional effects casual chat has on mood of participants. Conditions C3 and C4 are both experimental conditions. In these conditions, the troll confederate spoke to the victim confederate. In condition C3, the victim did not respond to the tirade of the troll, in condition C4 the victim typed the same script as in condition C2 as a reaction to the troll. Whenever the troll was engaged by the participant, the response ‘whatever, noob’ was expressed. Besides the chat content of both confederates, all ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 16 conditions consisted of the 3v3 custom games as described earlier. The full scripts along with the respective timestamps per condition are presented in Appendix D.

Procedure

After participants signed up for the experiment on Doodle, they received two emails: one e-mail two days before their specified timeslot and another one an hour before that same timeslot. E-mail content is presented in Appendix E. In the first email, participants were reminded of their specified timeslot and it was explained that a second email was going to be sent to them an hour before the beginning of the experiment. The second email contained a link to the Qualtrics questionnaire which the participant had to click in order to start the experiment.

After confirming their consent to participate via online informed consent letter, participants filled out the pre-test (Questionnaire 1). This questionnaire randomly presented the items of the

PANAS mood scale. Once this was completed, the participant was presented with login credentials for a LoL account created by the researchers.

When participants logged into the specified account, they were invited to the custom game created by the researchers. The primary researcher sent a message to the participant stating that he/she should stay until to end of the game to receive a code in order to continue to the second questionnaire. The confederates and participants then played, with chat scripts beginning at the four minute mark (except for condition C1; see Appendix D). After the game, the primary researcher sent the participant a unique code to enter into Qualtrics, allowing them to continue with the post-test (Questionnaire 2). These codes consisted of the participant number, followed by the condition (ex., P30VC). Once the participant entered their corresponding code into

Qualtrics, the second questionnaire as described earlier was presented. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 17

This post-test concluded with a debriefing text, which offered participants the option to receive a leaflet to help dealing with negative feelings, thoughts and emotions elicited by participating in the experiment. Furthermore, participants were informed about the option to ask additional questions by sending an email to the researchers and were asked again if their responses could be used for analysis. Lastly, participants were given the option to leave their email address to be entered in the draw for a 100 euro prize. During the data collection phase, it became evident that it was difficult to recruit enough participants for the study to be valid.

Therefore, to incentivize more people to participate in the study, the draw for a 100 euro prize was replaced by a guaranteed five euros after completing the experiment. This replacement was implemented firstly for participant eight, so the seven participants that had already participated earlier were contacted retrospectively to make sure each participant was rewarded equally for their participation. Participants were also asked to not disclose information about the experiment as the data collection was still running.

Results

Trolling victimization

To test whether participants’ mood was influenced either negatively or positively as an effect of witnessing trolling victimization, results of the PANAS mood scale were compared with each other pre- and posttest. In order to do so, variables were first created for the overall pretest and posttest positive affect, made by adding up the corresponding mood scores from questionnaire 1 and from questionnaire 2. Also, to test if mood of participants was influenced by victim speech, the variable positive mood difference was created by subtracting the positive mood scores posttest from the positive mood scores pretest. The mean and standard deviations of all variables that were necessary for the analyses are stated in Table 1. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 18

Table 1 Range, mean and standard deviations of all created variables in the analyses Variable Range Mean SD Positive mood scores pretest 13 – 41 30.80 5.57 Positive mood scores posttest 12 – 43 30.05 7.44 Negative mood scores pretest 10 – 35 15.62 6.29 Negative mood scores posttest 12 – 43 15.12 5.27 Overall scores pretest 0 – 31 15.21 7.71 Overall scores posttest -1- 29 14.93 7.76 Mood difference -22 – 21 .33 8.71 Negative mood difference -12 – 10 .66 4.69 Positive mood difference -14 – 19 .73 7.17

After the same was done for the pretest and posttest negative mood scores, overall mood scores pre- and posttest were created. Then, a mood difference score was calculated by subtracting the posttest scores form the pretest scores. The difference in negative mood was also calculated by subtracting the negative mood scores posttest from the negative scores pretest. The differences in mood were used throughout the rest of the analysis to see whether mood of participants was influenced by witnessing trolling victimization. Next, two conditions were made based on the condition participants were randomly assigned to: conditions C1 and C2 were named “Control” and conditions C3 and C4 were named “Experimental”, as only participants in the latter two conditions experienced trolling victimization. This dichotomous classification was required as the difference between the two major manipulations was examined. For the victim speech analyses, participants were allocated in either of two conditions: conditions C1 and C3 were names “NVS” (no victim speech) and conditions C2 and C4 were named “VS” (victim speech).

Testing assumptions. Before the effect of witnessing trolling victimization on participants’ mood was examined, tests of normality were performed to determine which analyses would be most appropriate. First of all, histograms of both overall mood variables were ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 19 created, as can be seen in Figure 1. Figure 1 shows that both variables are not perfectly distributed, but still show some degree of normal distribution. Furthermore, scores of both variables are concentrated around a score of 15 to 20, with an additional peak around a score of 0 to 5 for the overall mood scores pretest. To check for any additional departures from normality in both our pre- and posttest overall mood scores, a Shapiro-Wilk test was performed.

Figure 1. Histograms of overall mood scores pre- and posttest.

The data from the overall mood scores pretest was tested for normality, SW = .964, p = .250, supporting the assumption that the data is normally distributed. The same was true for our posttest sample, SW = .980, p = .691. Finally, a Levene’s test, performed using the car package (Fox

& Weisberg, 2011) in R 1.0.153 (RStudio Team, 2016) indicated that the variability in the two conditions were not significantly different, F = .874, p = .356.

Next, test of normality were performed for the difference in negative mood scores pre- and posttest. As can been seen from Figure 2, negative mood scores of participants pre- and posttest are nearly identical, both being rightly skewed and concentrated around a score of 10 to

15. To further check for any changes from normality, a Shapiro-Wilk test was performed for the negative mood scores pretest. The data from the negative mood scores pretest was tested for ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 20 normality, SW = .840, p = < .0001, and the results led us to reject the assumption that the data is normally distributed. The same was analysis done for the negative mood scores posttest provided the same conclusion of non-normality, SW = .837, p < .0001. We then determined if the experimental and the control groups contained equal amounts of variability between scores using a Levene’s test for equality of variances, which indicated that the variability in the two conditions was not significantly different for the difference in negative mood sores pre- and posttest, F(1,37) = .159, p = .693.

Figure 2. Histograms of negative mood scores pre- and posttest.

Analyses. Since overall mood scores pre- and posttest are normally distributed with the assumption that the variance of conditions is equal, the analysis will be performed by means of an ANOVA. The ANOVA was used to see if the differences between pre- and posttest scores differed significantly between the experimental and the control condition. Our analysis revealed that difference in mood pre- and posttest was significantly bigger for participants exposed to trolling (M = 4.06) in comparison to participants not exposed to trolling (M = -2.86), F(1,37) =

7.083, p = .011. A positive difference in mood score indicates that mood scores posttest were lower than mood scores pretest. As such, mood scores of participants in the experimental conditions decreased, where mood scores of participants in the control conditions increased. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 21

Next, to test whether there was an increase in negative affect or a decrease in positive affect, the difference in negative mood scores pretest and posttest between conditions was examined. As both variables regarding the negative mood scores pre- and posttest were non- parametric and because the sample size is relatively small (N = 41), the analysis was conducted by means of a Mann-Whitney-Wilcoxon test. This analysis revealed that participants exposed to trolling significantly report higher levels of negative affect (M = 16.22) after their game than participants not exposed to trolling (M = 14.26), W = 284, p = .007. Together with our ANOVA, these results provide support for hypothesis H1.

Victim speech

To test if mood of participants is influenced by victim speech, results of the PANAS mood scale were compared with each other pre- and posttest. This comparison was done by means of the difference in positive mood scores pre- and posttest. As describer earlier, participants were assigned either to the condition with or without victim speech.

Testing assumptions. Before the effect of witnessing victim speech on participant’s mood was assessed, the same tests of normality were performed. In the first instance, histograms of the positive mood scores pretest and posttest were created, as can be seen in Figure 3.

Figure 3. Histograms of positive mood scores pre- and posttest. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 22

Figure 3 shows that both variables are distributed close to normal. Furthermore, positive mood scores pretest show a lack of responses around the scores of 15 to 20 and both variables are slightly left-skewed. To check for any additional variations from normality in the positive mood scores pretest, a Shapiro-Wilk test was performed. The data from the positive mood scores pretest was checked for normality, SW = .962, p = .203, supporting the assumption that the data is normally distributed. The same was true for our posttest sample, SW = .972, p = .398. Finally, a Levene’s test indicated that the variability in the two conditions was not significantly different,

F(1,38) = 1.924, p = .174.

Analyses. Since positive mood scores pre- and posttest are normally distributed with the assumption that the variance of conditions is equal, the analysis will be performed by means of an ANOVA. First, an ANOVA was employed to see if the differences between pre- and posttest scores differed significantly between participants subjected to victim speech in comparison to participants not subjected to victim speech. Our analysis revealed that, in terms of difference in mood, pre- and posttest scores were not significantly different for participants subjected to victim speech (M = 1.16) in comparison to participants not subjected to victim speech (M = -.45),

F(1,37) = .326, p = .571. Despite the lack of significance, the trend in the data is decreasing for mood scores of participants in the victim speech condition and increasing for mood scores of participants in the non-victim speech conditions.

Next, to check whether the difference in decrease of positive mood scores between conditions was significant, another ANOVA was performed. This analysis indicated that participants exposed to victim chat did not significantly report a bigger decrease in positive mood scores (M = .90) than participants not exposed to victim chat (M = .57), F(1,38) = .02, p

= .889. Finally, to check if difference between negative mood scores pre- and posttest of ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 23 participants in the victim chat condition differed from those of participants in the non-victim chat condition, a final ANOVA was performed. This analysis showed that the difference in negative mood scores pre- and posttest of participants in the victim chat conditions (M = -.26) did not significantly differ from those of participants in the non-victim chat conditions (M = 1.55),

F(1,37) = 1.473, p = .233. Hence, neither positive nor negative mood scores differed significantly between participants in the victim and the non-victim chat conditions. Victim chat did not significantly influence participants’ mood, neither positively nor negatively, providing evidence against both hypothesis H2a and H2b.

Discussion

Conclusions

Trolling victimization. Hypothesis H1, which specified that mood of participants subjected to trolling victimization will be negatively influenced, was confirmed. The difference in overall mood scores pre- and posttest was significantly disparate for participants in the experimental and the control conditions. Participants subjected to trolling behavior experienced a decrease in overall mood, while participants not subjected to trolling behavior experienced an increase in overall mood. The decrease in overall mood scores of participants in the experimental conditions was greater than the increase in overall mood scores of participants in the control conditions. Beside overall mood scores, difference in negative mood scores was also measured.

These analyses revealed that participants exposed to trolling behavior reported significantly higher levels of negative affect after their game than participants not exposed to trolling behavior.

Victim speech. Hypotheses H2, which stated that participants in the victim speech condition will experience a decrease in mood scores posttest, remains unconfirmed. However, ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 24 overall mood scores of participants in the victim speech condition marginally decreased, whereas overall mood scores of participants in the non-victim speech conditions marginally increased.

Our analyses showed that the difference in negative mood scores pre- and posttest also did not differ significantly between these two conditions. Even though these results display an absence of conclusive support for hypothesis H2, mood scores still changed as predicted. The small population that participated in the study most likely caused the dearth of evidence for either hypothesis. More meaningful results are expected when the experiment is reproduced with a larger sample size, as the right trend in the data was achieved with the current population.

Theoretical implications

Gaming effect on mood. When assessing the control conditions in relation to trolling victimization and victim speech, participants in both groups experienced an increase in overall mood scores posttest compared to their overall mood scores pretest. In our questionnaire, participants were asked to describe their experience during their game. Even though the answers to these questions were not analyzed qualitatively, they still were available to us as part of the questionnaire and as such yielded some support for why mood was increased by playing the game. Many participants indicated that they enjoyed playing the game, with various comments like “I was enjoying the game still” (P6, male), “Good! It has been 15 months since I played so I was very excited to play” (P25, male) and a couple of instances where players indicated that they felt relaxed after their game (P4, male; P5, male; P21, male). These comments are congruent with the prediction that mood would increase after participating in our experiment. This prediction was based on the study conducted by Fleming, Wood and Debra (2001) in which was stated that playing violent videogames result in arousal, which can lead to an increase in mood. It cannot be concluded that participants experienced an increase in arousal in the present study as ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 25 arousal was not specifically measured in our study, but the prediction that participating in our experiment will result in an increase in mood is supported. This was true even though we can assume that a game verses three easy bots was not challenging for most participants, judging by the skill levels of most of them (Table 1). This is further indicated by participants’ post-game comments: “… but bored to be playing a simple bot game.” (P22, male), “… however it also wasn’t that exciting compared to a game vs real players” (P27, male) and “A bit unfocussed

[sic.], as playing against bots is very easy compared to playing against others.” (P19, male). To substantiate the claim that participants experienced an increase in mood due to an increase in arousal despite the fact that the played games were not challenging for most participants, future research is required in which arousal is measured explicitly.

Trolling victimization. The positive effect that gaming had on mood was nullified by the negative feelings emitted by witnessing trolling victimization. Not only was this effect cancelled, participants’ mood scores were negatively influenced by the occurrence of trolling. Even though the present study did not analyze the explicit repercussions of this negative affect, these results are in line with a vast amount of prior research regarding the adverse consequences of witnessing trolling- and cyberbullying victimization, and thus have the potential to result in problems like substance use, school-related problems, assaultive behavior (Hinduja & Patchin, 2007), depressions (Baker & Tanrikulu, 2010) and anxious and paranoid symptoms (Schenk &

Fremouw, 2012) in the long term. Future research is required to investigate whether these specific consequences of witnessing trolling victimization are true for trolling-study participants.

Emotional contagion. It remains unclear whether or not emotional contagion explained the negative mood scores after witnessing trolling victimization because it cannot be concluded unconditionally that emotional contagion caused all the negative affect. Even though participants ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 26 in the victim speech conditions experienced a smaller decrease in negative mood scores than participants in the trolling conditions, both conditions still experienced a decrease instead of an increase of negative mood scores. Still, this decrease can be due to other effects that caused negative mood scores to decrease, like the induced arousal when playing a violent game

(Fleming, Wood & Debra, 2001), while the difference in decrease between the conditions was caused by emotional contagion. As our manipulation does not provide enough information to conclude that emotional contagion solely caused the negative mood scores, future research must actively manipulate emotional contagion to find a definitive answer.

The present study also lacks evidence to support hypothesis H2, as neither a positive nor a negative effect of victim speech on mood was found. This shortcoming is either due to the low sample size used in this study or due to the operationalization of victim speech. It is important to note that the emotional contagion theory operates from the assumption that a certain type of emotion is conveyed by a sender, which is then adopted by a receiver (Barsade, 2002). Research regarding emotional contagion within other domains also operates from this standpoint (e.g.

Kramer, Guillory & Hancock, 2014). Since the victim speech script used in our experiment did not actually convey any emotions, but rather neutral statements (except for the first statement, see Appendix D), this may explain the lack of effect on mood in the victim speech conditions.

Research that focuses on emotional content in victim speech within a trolling environment would likely produce different results.

General strain theory. The GST, used as an explanation for the link between being cyberbullied and acts of aggression (e.g. Hay, Meldrum & Mann, 2010), is validated partly as a causal mechanism in our study. When assessing the results in relation to victim agency, mood scores of participants in the victim speech conditions only decreased slightly and insignificantly ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 27 in relation to mood scores of participants in the non-victim speech conditions. Assessing the temporal restrictions of our experiment, it is suggested that GST’s predictions are invalid in the short term within the gaming context. GST’s predictions with respect to victim agency are therefore contradicted in the present study. Otherwise, the occurrence of victim speech would have resulted in a significant decrease in mood scores between the victim and non-victim speech conditions. On the other hand, given the overall decrease in mood upon exposure to trolling as a bystander, participants’ mood was affected negatively. Therefore, the present study affirms

GST’s predictions in overall mood scores and contradicts GST’s predictions in relation to victim agency.

Limitations and future directions

First of all, because the study was conducted by means of an online questionnaire, results were susceptible to recall errors, framing effects and social desirability (Chan, 2009). Where recall errors are not applicable to questions regarding demographics and current mood, they could have occurred when participants were asked to evaluate their team players and their experience regarding their played game. However, these errors should be reduced to a minimum as participants were instructed to fill in the questionnaire directly after their game, and it is thus not expected that these effects have significantly altered the results of the present study. That said, future research could consider using various presentations of the questions to check for potential validity threats.

Secondly, since this was an online study and participants were not playing from a fixed location provided by the researchers, it was impossible to achieve complete control over the experimental environment. Unbeknownst to the researchers, every participant could have been influenced by factors outside of the experiment. There could have been cases where participants ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 28 were interrupted by something or someone during their experiment. It is impossible to confirm if and to what extent participants were hindered in the present experiment, as participants were playing from a location of their choice. Future research could consider using a fixed environment in which participants conduct the experiment; however, this involves a tradeoff with ease of participating. In the present study, it was very easy to participate as every individual was able to choose their own timeslot and play from a location of choice. Nevertheless, it was still very difficult to sample enough participants. Thus, when future research is conducted with a fixed environment, it can be expected that it is even harder to sample enough participants, as ease of participation is further reduced by using a fixed environment.

Eventually, the data of 41 participants was used in the analyses. Hertzog (2008) states that sample sizes ranging between10 to 40 per group are big enough in order to achieve adequate results, indicating that our sample size was big enough in order to achieve considerable results.

When assessing research regarding the related construct of cyberbullying though, the number of participants is usually substantially higher than is the case here (e.g. Gasser & Keller, 2004;

Hinduja & Patchin, 2007; Schenk & Fremouw, 2012), ranging from 212 to 1388 participants in the cited articles. Given the limited amount of time and resources available to the research team, while also considering the time-consuming task each participant had to fulfill, it was not reasonable to aim for similar amounts of participants in the present study. Since the results of the present study regarding victim speech were in the predicted direction for both conditions, despite being insignificant, it is expected that the predictions are confirmed when more individuals participate in the study.

In conclusion, this study experimentally validated the theoretically-described negative consequences of witnessing trolling victimization in extant literature. Trolling caused bystanders ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 29 to experience negative affect, whereas a lack of trolling caused an increase of overall mood. The present study did not find a significant effect of victim speech on bystanders’ mood, but results suggested that this effect would occur with a larger sample size. This study provides experimental support for prior known theoretical knowledge, encouraging future research to experimentally investigate different aspects of trolling effects and consequences. Moreover, the present study provides a groundwork of theories regarding trolling and its consequences, which can act as an outset for experimental research regarding the underlying factors of trolling behavior.

ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 30

References

Agnew, R. (2006). General strain theory: Current status and directions for further

research. Taking stock: The status of criminological theory, 15, 101-123.

Baker, Ö. E., & Tanrıkulu, İ. (2010). Psychological consequences of cyber bullying experiences

among Turkish secondary school children. Procedia-Social and Behavioral

Sciences, 2(2), 2771-2776.

Barsade, S. G. (2002). The ripple effect: Emotional contagion and its influence on group

behavior. Administrative Science Quarterly, 47(4), 644-675.

Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., Desmet, A., & De

Bourdeaudhuij, I. (2014). Cyberbullying on social network sites. An experimental study

into bystanders’ behavioural intentions to help the victim or reinforce the

bully. Computers in Human Behavior, 31, 259-271.

Bishop, J. (2014). Representations of ‘trolls’ in mass media communication: a review of media-

texts and moral panics relating to ‘internet trolling’. International Journal of Web Based

Communities, 10(1), 7-24.

Castranova, E. (2008). Synthetic worlds: The business and culture of online games. University of

Chicago press.

Chan, D. (2009). So why ask me? Are self-report data really that bad. Statistical and

methodological myths and urban legends: Doctrine, verity and fable in the

organizational and social sciences, 309-336.

Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., & Leskovec, J. (2017). Anyone can

become a troll: Causes of trolling behavior in online discussions. arXiv preprint

arXiv:1702.01119. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 31

Coles, B. A., & West, M. (2016). Trolling the trolls: Online forum users constructions of the

nature and properties of trolling. Computers in Human Behavior, 60, 233-244.

Cook, C., Schaafsma, J., & Antheunis, M.L. (2016). Under the bridge: An in-depth exploration

of trolling in online games. Manuscript submitted for publication.

Derks, D., Fischer, A. H., & Bos, A. E. (2008). The role of emotion in computer-mediated

communication: A review. Computers in Human Behavior, 24(3), 766-785.

Fleming, M. J., Wood, R., & Debra, J. (2001). Effects of violent versus nonviolent video games

on children's arousal, aggressive mood, and positive mood. Journal of Applied Social

Psychology, 31(10), 2047-2071.

Fox, J., & Weisberg, S. (2011). An {R} Companion to Applied Regression, Second Edition.

Thousand Oaks CA: Sage. URL:

http://socserv.socsci.mcmaster.ca/jfox/Books/Companion

Gasser, L., & Keller, M. (2009). Are the competent the morally good? Perspective taking and moral motivation of children involved in bullying. Social Development, 18(4), 798-816.

Hardaker, C. (2010). Trolling in asynchronous computer-mediated communication: From user

discussions to academic definitions.

Hay, C., Meldrum, R., & Mann, K. (2010). Traditional bullying, cyber bullying, and

deviance: A general strain theory approach. Journal of Contemporary Criminal

Justice, 26(2), 130-147.

Herring, S., Job-Sluder, K., Scheckler, R., & Barab, S. (2002). Searching for safety online:

Managing" trolling" in a feminist forum. The Information Society, 18(5), 371-384.

Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in

nursing & health, 31(2), 180-191. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 32

Hinduja, S., & Patchin, J. W. (2007). Offline consequences of online victimization: School

violence and delinquency. Journal of school violence, 6(3), 89-112.

Jang, H., Song, J., & Kim, R. (2014). Does the offline bully-victimization influence

cyberbullying behavior among youths? Application of general strain theory. Computers

in Human Behavior, 31, 85-93.

Johnson, D., Nacke, L. E., & Wyeth, P. (2015). All about that base: differing player

experiences in video game genres and the unique case of moba games. In Proceedings of

the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 2265-

2274). ACM.

Kou, Y., & Nardi, B. A. (2014). Governance in League of Legends: A hybrid system. In FDG.

Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale

emotional contagion through social networks. Proceedings of the National Academy of

Sciences, 111(24), 8788-8790.

March, E., Grieve, R., Marrington, J., & Jonason, P. K. (2017). Trolling on Tinder® (and other

dating apps): Examining the role of the Dark Tetrad and impulsivity. Personality and

Individual Differences, 110, 139-143.

Martyn‐Nemeth, P., Penckofer, S., Gulanick, M., Velsor‐Friedrich, B., & Bryant, F. B. (2009).

The relationships among self‐esteem, stress, coping, eating behavior, and depressive

mood in adolescents. Research in nursing & health, 32(1), 96-109.

Merrell, K. W., Gueldner, B. A., Ross, S. W., & Isava, D. M. (2008). How effective are school

bullying intervention programs? A meta-analysis of intervention research. School

Psychology Quarterly, 23(1), 26-42. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 33

Neumann, R., & Strack, F. (2000). "Mood contagion": the automatic transfer of mood between

persons. Journal of personality and social psychology, 79(2), 211.

Nicol, S. (2012). Cyber-bullying and trolling. Youth Studies Australia, 31(4), 3.

Obermaier, M., Fawzi, N., & Koch, T. (2016). Bystanding or standing by? How the number of

bystanders affects the intention to intervene in cyberbullying. New media &

society, 18(8), 1491-1507.

Park, H., & Kim, K. J. (2014, November). Social network analysis of high-level players in

multiplayer online battle arena game. In International Conference on Social

Informatics (pp. 223-226). Springer, Cham.

Patchin, J. W., & Hinduja, S. (2006). Bullies move beyond the schoolyard: A preliminary look at

cyberbullying. Youth violence and juvenile justice, 4(2), 148-169.

Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A

test of general strain theory. Youth & Society, 43(2), 727-751.

Paxton, S. J., Neumark-Sztainer, D., Hannan, P. J., & Eisenberg, M. E. (2006). Body

dissatisfaction prospectively predicts depressive mood and low self-esteem in adolescent

girls and boys. Journal of clinical child and adolescent psychology, 35(4), 539-549.

Riordan, M. A., & Kreuz, R. J. (2010). Cues in computer-mediated communication: A corpus

analysis. Computers in Human Behavior, 26(6), 1806-1817.

Riot Games (2009). League of Legends [PC game]. Riot Games. Retrieved from

https://euw.leagueoflegends.com/en/

Romiszowski, A., & Mason, R. (1996). Computer-mediated communication. Handbook of

research for educational communications and technology, 2, 397-431. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 34

RStudio Team (2016). Rstudio: Integrated Development for R. Rstudio, Inc., Boston, MA. URL:

http://www.rstudio.com/

Russoniello, C. V., O’Brien, K., & Parks, J. M. (2009). The effectiveness of casual video games

in improving mood and decreasing stress. Journal of CyberTherapy &

Rehabilitation, 2(1), 53-66.

Schenk, A. M., & Fremouw, W. J. (2012). Prevalence, psychological impact, and coping of

cyberbully victims among college students. Journal of School Violence, 11(1), 21-37.

Shin, J. (2008). Morality and Internet Behavior: A study of the Internet Troll and its

relation with morality on the Internet. In Society for information technology & teacher

education international conference (pp. 2834-2840). Association for the Advancement of

Computing in Education (AACE).

Slonje, R., & Smith, P. K. (2008). Cyberbullying: Another main type of bullying? Scandinavian

journal of psychology, 49(2), 147-154.

Thacker, S., & Griffiths, M. D. (2012). An exploratory study of trolling in online video

gaming. International Journal of Cyber Behavior, Psychology and Learning

(IJCBPL), 2(4), 17-33.

Vandergriff, I. (2013). Emotive communication online: A contextual analysis of computer-

mediated communication (CMC) cues. Journal of Pragmatics, 51, 1-12.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures

of positive and negative affect: the PANAS scales. Journal of personality and social

psychology, 54(6), 1063.

ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 35

Appendix A Recruitment Text` Have you ever played League of Legends before? Are you 18 or older? Do you want to contribute to science by playing a little game? If so, then you could qualify to participate in an online teamwork experiment going on right now through Tilburg University! We are currently looking for participants to play League of Legends for science. If you qualify and would be interested in participating, please click the following link: https://doodle.com/poll/esx3s4cyq58aysuz ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 36

Appendix B

Informed consent text and questionnaire 1

Informed Consent Text

Thank you again for your interest in participating in the present study, conducted by researchers at Tilburg University. In this study, we are interested in looking at teamwork among strangers in an online environment.

Procedure: Participation in this study will consist of a questionnaire about your mood and gaming experiences, a game of League of Legends lasting approximately 30 minutes, and another questionnaire discussing the game and your mood.

Duration of participation: Your participation will take no more than an hour. At the end of the final questionnaire, you have the option to be entered into a draw to receive €100.00 euros.

Risks: You may experience fatigue and moments of emotional discomfort throughout the course of the study, but otherwise the risks are no greater than those encountered in ordinary life. As with any laboratory study in which data are collected, there is a potential risk of breach of confidentiality. Safeguards to minimize this risk are discussed in the “Confidentiality” section below.

Privacy and confidentiality: During the game, the researcher will be in spectator mode recording the proceedings. No other audio or video recording will take place throughout the procedure. Your personal information will not be shared with or given to anyone – everything is anonymized by number, and no personally identifiable information will be available as experimental data. Only the researchers will know who you are; anything personally identifiable is destroyed at the end of the data collection phase. Your anonymized data will be stored in a central repository for a period of at least ten years. These data may be reviewed by other researchers. Completed results of the study will be available in scientific article and dissertation format.

Voluntary Nature of Participation: Your participation is completely voluntary, and you are free to withdraw from the study at any time with no penalty. Any questions you may find objectionable, you are not required to answer. Should you choose to discontinue your participation, all information you provide will be destroyed.

Questions: This project has been approved by a school-level ethics committee (the TSH REC), but if you have any questions or concerns, you can contact the researchers or the ethics committee directly at Snail mail can be sent to CL Cook, Kamer D302, Postbus 90153, 5000 LE, Tilburg, The Netherlands.

By clicking "I accept", you consent to participating in the experiment. If you click "I do not consent", your participation will be terminated. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 37

Questionnaire 1

Please answer the following questions either by filling in the blanks or circling the appropriate response provided.

1. What is your age? ______2. With which gender do you identify? Male Female Other 3. What is your nationality? ______4. What is your country of residence? ______5. What is your native language? ______6. What is your estimated level of English proficiency? a. Beginner b. Intermediate c. Advanced d. Fluent e. Native 7. What is your highest completed level of education? a. Primary education b. Lower secondary education c. Upper secondary education d. Bachelor’s or equivalent level e. Master’s or equivalent level f. PhD or equivalent level 8. How long have you been playing League of Legends? a. > 1 month b. 1-6 months c. 6 months – 1 year d. 1-2 years e. 2-4 years f. 4-6 years g. 6-8 years h. 8+ years 9. Which best describes your League of Legends play frequency? a. > Once a month b. Once a month c. Several times a month d. Once a week e. Several times a week f. Daily 10. Do you have a League of Legends account with a competitive rank? Yes No 11. If yes, which rank are you currently? Please select both a rank and division (if applicable). a. Bronze a. I b. Silver b. II ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 38

c. Gold c. III d. Platinum d. IV e. Diamond e. V f. Master g. Challenger 12. Do you have experience playing other online multiplayer games? If so, please list a few: ______

Below, you will find a number of words that describe different feelings and emotions. Read each word and indicate to what extent you feel this way right now, that is, at the present moment. You can mark your appropriate answer in the space next to that word, using the following scale:

1 2 3 4 5 Not at all Extremely

_____ Interested _____ Irritable _____ Distressed _____ Alert _____ Excited _____ Ashamed _____ Upset _____ Inspired _____ Strong _____ Nervous _____ Guilty _____ Determined _____ Scared _____ Attentive _____ Hostile _____ Jittery _____ Enthusiastic _____ Active _____ Proud _____ Afraid ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 39

Appendix C

Questionnaire 2

This scale consists of a number of words that describe different feelings and emotions. Read each item and then mark the appropriate answer in the space next to that word. Indicate to what extent you felt ______during the game; use the following scale to record your answers.

1 2 3 4 5 Not at all Extremely

Below, you find a number of statements about the other players in the game you just played. Please rate the statements below using the following scale:

1 2 3 4 5 Strongly disagree Disagree Neutral Agree Strongly agree

Based on the game we just played, I think that Player 1 is …

___ Friendly___ Kind

___ Intelligent ___ Knowledgeable

___ Creative ___ Incompetent

___ Mean ___ Trustworthy

Based on the game we just played, I think that Player 2 is …

___ Friendly___ Kind

___ Intelligent ___ Knowledgeable

___ Creative ___ Incompetent

___ Mean ___ Trustworthy ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 40

Read each statement carefully and as you answer the questions think of your team during the game as a whole. For each statement fill in the box under the MOST APPROPRIATE heading that best describes the group during the game. Please mark only ONE box for each statement.

Not at all Not bit little A Moderately a bit Quite Extremely There was a sense of team spirit. The team got along well together. There was friction and anger between the members. The members affronted each other. The members rejected each other. The members distrusted each other. The members felt comfortable with one another. The members appeared tense.

Please answer the following questions as completely and truthfully as possible.

1. Please rate your team’s level of teamwork on a scale of 1 to 10, with 1 being non-existent and 10 being a well-oiled machine. Justify your answer below.

Rating: 1 2 3 4 5 6 7 8 9 10

Justification: ______

2. How did you feel during the game? Describe your experience.

______

3. Would you have reported (i.e., sent a complaint and request for punishment to the game authorities) any other players were this a real game? Why or why not?

______ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 41

______

Debriefing Text

Thank you for your participation!

As you might have guessed, this study is not actually focused on teamwork directly. You may have noticed that during the game, one person was being harassed, or trolled. Both the troll and the victim are actually in on this experiment – everything they said was scripted. What we are really measuring is how you, the real participants, react to that situation of victimization. You were in one of four conditions: silence, chatting, or one of two flaming conditions (with or without victim chat). We are looking at how different trolling situations might influence bystanders in games or on the net to respond to these situations and in what way. In this way, we hope to better understand what is happening when someone trolls, and how it affects everyone involved, directly or indirectly.

If at any point this brought up feelings you're uncomfortable with, a leaflet is available to you offering resources in the Netherlands to help in dealing with these thoughts and emotions. There is also some extra info on antisocial online behavior if you want to learn more. If you would like this leaflet, or if you have any additional questions about the research, please feel free to contact the researchers at

Please confirm that we may use your responses for our research by clicking yes below. If you choose to discontinue your participation, please click no and your data will be deleted.

If you choose ‘yes’, you may also enter your e-mail below if you would like to be entered in the draw for the 100 euro prize. We would also ask that you not talk about the nature of the study to anyone until the data collection phase is over (February 2018) so as not to reveal the experiment’s goals to others. ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 42

Appendix D

Trolling Scripts

1. Silence Condition – Neither confederate will speak for the duration of the match. 2. Chat Condition – There will be no troll present – one confederate will say ”gl hf” (good luck, have fun) at the beginning of the match, and will proceed to chat as follows, beginning at the 4 minute mark; all messages are time-stamped):

- I really like ___ as a champ. (The blank will be filled with the character name of the character they are currently playing.) [4:00]

- Let’s push top. [5:30]

- What’s going on bot? [7:45]

- Can I get help top? [9:15]

3. Flaming Condition 1– In the pre-game lobby, the troll will immediately say “I call bot.” and choose a champion (character) immediately. At the 4 minute and 45 second mark, the following script begins (if the troll is engaged by the naïve participant, he says ‘whatever, noob’ and continues his tirade against the other confederate; messages are timestamped below):

- Srsly? ___ is such a noob champ. (The blank will be filled with the character name of the character the other confederate is currently playing.) [4:45]

- Have you ever even played this game before ___? Fucking retard. (The blank will be filled with the character name of the character the other confederate is currently playing.) [6:15]

- ____, uninstall. Fucking easy bots are better than you. (The blank will be filled with the character name of the character the other confederate is currently playing.) [7:00]

- Even in fucking experiments I have to carry these noob-ass teams. [8:30]

4. Flaming Condition 2 – In the pre-game lobby, the troll will immediately say “I call bot.” and choose a champion (character) immediately. At the 4 minute mark, the following script begins (script messages are time-stamped below; if the troll is engaged by the naïve participant, he says ‘whatever, noob’ and continues his tirade against the other confederate):

V: I really like ___ as a champ. (The blank will be filled with the character name of the character they are currently playing.) [4:00]

T: Srsly? ___ is such a noob champ. (The blank will be filled with the character name of the character the other confederate is currently playing.) [4:45]

V: Let’s push top. [5:30]

T: Have you ever even played this game before ___? Fucking retard. (The blank will be filled with the character name of the character the other confederate is currently playing.) [6:15] ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 43

T: ____, uninstall. Fucking easy bots are better than you. (The blank will be filled with the character name of the character the other confederate is currently playing.) [7:00]

V: What’s going on bot? [7:45]

T: Even in fucking experiments I have to carry these noob-ass teams. [8:30]

V: Can I get help top? [9:15] ONLINE TROLLING, VICTIM SPEECH, AND BYSTANDER MOOD 44

Appendix E

Study email 1 (sent two days before specified timeslot)

Dear participant,

You have scheduled your participation in our online teamwork study on Friday, September 15th at 11:00*. One hour before your participation you will receive a link, which you must click to start the experiment.

Our thanks again for your participation.

- Tilburg Online Behaviour Research Team

Study email 2 (sent one hour before specified timeslot)

Dear participant,

You have scheduled your participation in our online teamwork study at 11:00*, in one hour. At that time, please click the following link to begin:

Our thanks again for your participation.

- Tilburg Online Behaviour Research Team

*Note. The date and time were variables for each email, tailored to the timeslot of the participant in dispute.