Introduction

Toxic behaviors in online multiplayer games have become a prevalent phenomenon. Players frequently encounter toxic behaviors when playing popular competitive online multiplayer games, such as League of Legends, Counter Strike: Global Offensive, and Defense of the Ancients 2 (see for example Cary et al., 2020; Kordyaka et al., 2020; Kou, 2020; Tang & Fox, 2016). The prevalence of toxic behaviors has raised concerns among numerous scholars as exposure to toxicity has shown to have negative consequences—both in the immediate situation as well as in the long run. According to the Anti-Defamation League, one in ten player report having depressive or suicidal thoughts due to experiences with toxicity, such as hate and harassment (ADL, 2019). As for diversity and inclusive play, other studies have found that experiences with toxic behaviors, such as sexual harassment, have resulted in players withdrawing from playing online multiplayer games (Fox et al., 2018; Fox & Tang, 2017a; McLean & Griffiths, 2019). This is problematic in terms of representation and equal access to online space. Furthermore, toxic behaviors have shown to be counterproductive for teammate performance. Thus, teammates exposed to toxic behaviors tend to be more negative affected than their opponents (Monge & O’Brien, 2021; Neto et al., 2017; Neto & Becker, 2018).

To this end, current research on toxic behaviors in online games has been attentive to conceptualizing and classifying different forms of toxic behaviors and its negative consequences (see for example Kou, 2020; Kowert, 2020 for taxonomies of toxic behavior). Although existing definitions of toxic behavior remain ambiguous, research has classified and examined behaviors encompassing actions such as offensive language, griefing, trash-talking, trolling, sexual harassment, and racist slurs (Blackburn et al., 2014; Brehm, 2013; Cook et al., 2019; Cote, 2017; Irwin et al., 2020; Nakamura, 2012; Ortiz, 2019; Thacker & Griffiths, 2012). For example, griefing is defined as behaviors that disrupts the game experience and enjoyment of other players, such as deception or stealing kills (Achterbosch et al., 2017; Foo & Koivisto, 2004).

In addition to conceptualizing the components of toxic behaviors, current research has provided a range of explanations for why toxic behaviors occur. Studies have focused on individual-oriented explanations such as linking personality traits (e.g., machiavellianism, psychopathy, gamer identification) to performance of toxic behaviors in online games (Lemercier-Dugarin et al., 2021; Tang et al., 2020). Likewise, player motivations for engaging in toxic behaviors, such as revenge, personal enjoyment, thrill seeking, and egocentrism, have been identified (Cook et al., 2018; Liu & Agur, 2022). Besides individual explanations, cultural explanations covering toxic gaming culture (Consalvo, 2012), geek masculinity (Braithwaite, 2016), and structural sexism have been linked to why toxicity occur when players play online. By comparison, only a handful of studies have investigated the interactional dynamics involved in encounters with toxic behavior in online games. Among the key exceptions is Cook et al. (2019) who based on analyzing 10,025 community-reported trolling incidents in League of Legends identified striking similarities between messages sent by trolls and their teammates as both displayed negative traits. Similarly, by analyzing the “r/leagueoflegends” subreddit, Kou (2020) find that five contextual factors (competitiveness, in-team conflict, perceived loss, powerlessness, and toxic behavior) are involved in the emergence of toxicity. Furthermore, in detecting toxicity in online multiplayer games, Märtens et al. (2015) identified kill events as being frequently involved in the proceeding of toxic remarks. That is, events in which a player is killed in battle by the enemy team. This novel line of research exemplifies the need for advancing knowledge on how interactional dynamics are involved in encounters with toxicity arising from death events.

While in the existing literature, the importance of integrating situational and interactional dynamics in coherent understandings of toxicity in online gaming is recognized, there is a lack of studies utilizing coherent theoretical frameworks to account for such dynamics. Specifically, there seem to be a gap in the literature exploring and explaining the conflicts that arise in online gaming. Against this background, we suggest such a situational framework based on the works of Erving Goffman to explore the emergence of toxic encounters from death events in League of Legends. In the following, we outline the main conceptual components in Goffman’s analytical scheme and apply this approach to preliminary findings from an on-going study of toxicity in online gaming as well as to findings in the existing research literature. In so doing, we aim to (1) explore, demonstrate, and validate the applicability of Goffman’s situational approach to online situations of conflict and confrontation and (2) advance the conceptual understanding of toxicity in online gaming. As such, then, this study offers a theoretically informed account of the interactional dynamics of conflicts in online gaming.

The Patterned Regularities of Interactional Micro-worlds

Goffman’s most significant concern was the establishment of the so-called interaction order “as a substantive domain in its own right” (Goffman, 1983, p. 2). Thus, an interaction order is the special type of order that characterizes everyday life face-to-face interaction among participants in one another’s response presence and mutually engaged in displaying and monitoring information about each other. Interaction orders derive from social situations defined “as an environment of mutual monitoring possibilities, anywhere within which an individual will find himself accessible to the naked senses of all others who are “present,” and similarly find them accessible to him” (Goffman, 1964, p. 135). As such, situations constitute “a reality sui generis” and therefore “warrant analysis in their own right” (Goffman, 1964, p. 134). When in the presence of others, participants are vulnerable to violation of their psychic preserves or personal territories (Goffman, 1971) by way of the actions and utterances of others. Therefore, interaction orders are regulated by situational rules or “situational proprieties”(Goffman, 1963, p. 24) that may be viewed as a special situational code distinct from other explicit moral codes such as laws regulating economic affairs. A special set of situational norms relates to the “engrossment and involvement” (Goffman, 1983, p. 3) of participants. Participants are, for example, obliged to “come into play,” to “stay in play,” and to contribute to maintaining a form of “interaction tonus” (Goffman, 1963, p. 25), and in conveying their continued engagement in the situation at hand, participants may display certain interaction rituals that serve a vehicles in forming the patterns of deference characteristic of social situations (Goffman, 1967). To Goffman, then, an interaction order is a socially ordered micro-social phenomena with distinct processes, structures, and regulation.

As it appears, Goffman’s framework directs attention to the situational aspects of situated social activity; to “what could only occur in face-to-face assemblies” (Goffman, 1983, p. 3) and that thus to activity that is “intrinsically dependent on the conditions that prevail in therein” (Goffman, 1963, p. 22). This notion will serve as a guiding principle through our subsequent application of Goffman’s framework to the study of toxicity in online gaming.

Besides offering a micro sociological approach to account for the orderliness of interaction in situations of co-presence, Goffman (1961) offered a situational scheme to explain why social actors engage in games. This scheme, which has particular relevance to this present study, served as part of his overarching aim to study focused interactions as they appear in the encounters or “situated activity system[s]” (Goffman, 196, p. 8). Elaborating the works of Riezler (1941) work on play and seriousness, Goffman (Goffman, 1961, p. 25) conceptualized games, and thus social situations in general, as “world-building activities” that “generate a world of meanings that is exclusive to it” (Goffman, 1961, p. 26). Games, including online games, may be seen as examples of such world-building activities, as places of action, in which participants can engage in experiences of risk and fatefulness that are no longer available in everyday life. Such activities, then, provide access to fateful events allowing them to demonstrate character (Goffman, 1967, p. 217) or perhaps even to engage in “character contests” (Goffman, 1967, p. 240). Further, games allow for experiences of euphoric interaction. When participant’s spontaneous involvement in the game/situation is well-aligned with the situational expectations, participants may experience to be “at ease” or that the interaction is euphoric. On the other hand, when there is an unalignment of involvement and expectation, participants may experience a state of unease, tension, or dysphoria (Deterding, 2019; Goffman, 1961, pp. 38-39).

In characterizing these micro-worlds of focused interaction, Goffman found that the interactional material for establishing the roles and events is available for the participants in the situation (Goffman, 1961, p. 26) and defined the specific interaction order to be observed in encounters as “a locally realized world of roles and events [that] cuts the participants off from many externally based matters that might have been given relevance, but allows a few of these external matters to enter the interaction as an official part of it” (Goffman, 1961, p. 29). Thus, according to Goffman, there is no one-to-one relationship between interaction orders and external matters or structures, but rather a “loose coupling,” a “set of transformation rules,” (that is subdivide into inhibitory rules and facilitating rules) or a “membrane” (Goffman, 1983, p. 11) emphasizing that focused interactions have unique internal logics while at the same time they are related to extra-situational factors.

In the concluding sections of his PhD dissertation, in which Goffman coined the concept of the interaction order, he suggested further research to explore the regulations of this order. Specifically, he pointed to studies of (board) gaming as a form of interplay activity with its own rules and conventions, and where studies of moves and countermoves of participants may reveal significant interactional regularities that may not be easi observable in ordinary communication conduct (Goffman, 1953, p. 362). Following Goffman’s theoretical scheme and his call for studies of game situations, we intend to explore the potential social or situational orderliness of toxic behaviors in online gaming conceptualizing game situations as locally realized worlds of meaning maintained by interpersonal rituals and loosely coupled to externally based resource attributes.

While seemingly, Goffman’s conceptual scheme holds merits in terms of teasing out social regularities in gaming situations, a note on its applicability in online encounters warrants attention. Goffman’s framework on situational interactions has received critique for its emphasis on physical immediate co-presence as fundamental component for initiating social interactions (see for example Bullingham & Vasconcelos, 2013; Campos-Castillo & Hitlin, 2013). Seemingly, the concern with embodied (and thus not disembodied) information and the implication of physical co-presence renders Goffman’s framework on social interactions irrelevant for studies on online situations and interactions where people are physically separated and unable to see each other. However, Bullingham and Vasconcelos (2013) demonstrate that people actively interact and construct identities (e.g., by managing expressions given to an audience) by using online representations available as substitutes for behavioral and physical cues. Similarly, researchers such as Boyns and Loprieno (2013) argue that individuals can become emotionally immersed in virtual worlds with social interactions creating simulations of co-presence transcending the limitations of physical presence. Collectively, as they indicate the significant social experience of virtual worlds, these studies expand the application of the micro sociology and Goffman’s framework into relevance for studying online interactions and situations. Taken as a whole, they support the notion that in online gaming, participants maintain a shared situated order that defines identities and various lines of actions. Finally, in online gaming, participants act towards each other, and these actions hold real (and sometimes fateful) consequences for all other game participants. However, little work has been done in terms of applying Goffman’s framework to an online context characterized by conflicts and toxicity.

League of Legends

Toxic Behaviors in League of Legends

For this study, toxic encounters arising from death events within temporary teams in League of Legends constitute the unit of the analysis. Specifically, we focus solely on encounters with toxic behaviors within teams. Toxic behaviors occurring between teams are thus excluded from the analysis as it appears from existing research that many forms of toxic behaviors occur within teams. Studies have identified textual, non-textual, and voiced forms of toxic behaviors attempting to negatively affect teammates rather than opponents. For example, leaking information or intentionally feeding the enemy team (Kou, 2020). Furthermore, the analysis exclusively focuses on exploring the interactional dynamics involved in toxic behaviors arising from death events of a teammate. As illustrated by Märtens et al. (2015), the events of death are frequently involved in the proceeding of toxic behavior.

League of Legends constitute an excellent case for studying encounters with toxic behaviors as they unfold. First, this online multiplayer game is one of the most played videogame in the World. It was first released in October 2009, and today, more than 100 million unique players log in to play League of Legends each month (activeplayer.io/league-of-legends/, 2022). It is a synchronous game with players competing real-time across national borders as they log on online servers. Its situatedness allows us to explore the reactions and behaviors of the players as situations unfold. Second, League of Legends constitutes one of the most studied online multiplayer game in terms of toxicity. The majority of studies has focused on text-based forms of toxic behaviors such as trash-talking, offensive language, and flaming (see for example Blackburn et al., 2014; Cook et al., 2019; Kwak & Blackburn, 2015; Neto & Becker, 2018). For example, by analyzing the linguistic patterns of toxic and non-toxic players in League of Legends, Kwak and Blackburn (2015) identify difference in chat engagement among players. Toxic players tend to never apologize and praise teammates during matches, which are otherwise common behaviors displayed by non-toxic players. Nevertheless, while these studies have examined textual forms of toxic interaction and communication practices, knowledge of the interactional dynamics involved in encounters with toxicity is still limited.

What Is League of Legends?

In League of Legends, a team-based strategy game, one team consists of five individual players. Each player takes on a champion (an already-created avatar) to be controlled for the entire match. Figure 1 depicts Shaco the Demon Jester which is one of the more than 150 different champions to select from. Every champion has its own unique combination of abilities, strength, and weaknesses. Therefore, the selection of champions is an important decision for the game.

Fig. 1
figure 1

Shaco the Demon Jester

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To win the game, a team must destroy the enemy team’s base, called Nexus. This battle takes place on the map, Summoners Rift (see Fig. 2), with players strategically defeating the enemy team, their minions, and turrets, while taking over their Nexus. The Summoners Rift is divided into three lanes. These are called the top lane, the middle lane, and the bottom lane. When selecting a champion, each player is ascribed a lane position to defend. Throughout the battle, players are killed and respawn. That is, killed players resurrect from their home base while the enemy player(s) achieve experience points and gold. Hence, killing enemy players make players, and thus their team, stronger. The typical duration of a battle is 20–45 min.

Fig. 2
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Summoner’s Rift

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Method

Video Observation of Naturalistic Behaviors

Data for this study comprise a sample of naturalistic observations of toxic gaming behavior captured by League of Legends players screen-recording their gameplay. Video recordings provide an unique yet underutilized approach for studying behavior in its natural context (Pallante et al., 2022). By capturing real-life encounters of toxicity in online gaming, screen-recordings offer fine-grained details and unobtrusive data for systematic naturalistic observations on actual behavior and interactional dynamics of the encounters (Jerolmack & Khan, 2014; Lindegaard & Bernasco, 2018; Reiss, 1971). With video recordings, the same events of toxic behaviors can be observed multiple times, pausing, zooming in on specific actions or encounters, and playing the scenes in slow motion. Such options offer a high level of reliability and precision when examining encounters of toxic behaviors in online gaming.

Recruitment

Video data was collected in January–February 2022. For this study, participants were recruited from a Danish League of Legends Facebook group. Participants were informed that the research focus was on aggregated behaviors in situations with toxicity, not on the individual player. As the analytical unit of this study is on situational interactions, we did not gather information on the individual participant, such as background factors, personality traits, or motivation. Only individual information concerning nametags, gaming level, and played champion is represented in the data.

In terms of gender, only male League of Legends players volunteered to participate. This is a clear limitation of our study, and the skewness in gender representation should not be taken lightly upon. Existing research on gender and toxic gaming illustrate that female players often are targeted and experiencing toxic gaming behaviors (see for example Fox & Tang, 2017b; Kuznekoff & Rose, 2013; McLean & Griffiths, 2019). As a result, this skewness in gender representation may have implications for the situations of toxicity we are observing.

For participating in this study, participants had to be above the age of 18 years and have gaming experience with League of Legends. A participant’s level of gaming experience was not essential for participation. Participants were instructed to screen record entire gameplays while playing League of Legends. We purposefully applied a broad definition of “toxicity” using field-sensitizing concepts like griefing, flaming, and feeding as examples on what toxicity could entail (Blumer, 1954). This allowed for an explorative approach using the judgments of the players to identify situations of toxic behaviors, and thus a high level of ecological validity.

Video Sampling

For this study, a total of three videos of gameplay were included, containing 119 minutes of gameplay, and 131 identified deaths of teammates. The shortest video was 38:21 minutes and the longest was 42:27 minutes. To be included, the video had to conform to the following inclusion criteria: The video should have a certain technical quality (e.g., high resolution) that enables behavioral coding with no breaks in the gameplay sequence. The video should encompass entire lengths of matches to include contextual information prior and after events of toxic behavior. The video should record gameplay behaviors in temporary teams consisting of strangers playing together. Based on autoethnographic fieldwork in League of Legends, temporary group formations appear to feature many forms of toxic behaviors (Karhulahti, 2020). Hence, all three gameplay videos were recorded within the Ranked Solo/Duo battle mode in League of Legends. However, it should be noted that the battle mode Ranked Solo/Duo does not prohibit players from playing with one friend. As we did not identify cues of friendship throughout the videos, we are not able to determine if each player on the team and enemy team is playing exclusively with strangers.

Anonymity procedures were taken to protect the identities of the players appearing in the data. Players were anonymized with potentially identifiable information removed. Unique nametags concealing personal information are widely used in online gaming, however, nametags remain unique and potentially recognizable for fellow community members in the online gaming environment. Hence, unique nametags only provide pseudo-anonymity, not full anonymity. As a consequence, players are referred to by the name of their played League of Legends champion, such as Shaco or Nunu. In League of Legends, there are more than 150 different champions for the players to select from. These champions are all pre-constructed by game designers. Therefore, individual players cannot be identified based on the played champion. Furthermore, we have decided to include the name of the played champion as (a) the individual champion has different abilities, strength, and weaknesses that inform possible gameplay actions, and (b) because teammates’ interactions and interpretations of gameplay actions may be informed as well by the selection of specific champions. In addition, alteration procedures of chat messages were carried out to reduce searchable data. More specifically, we altered self-written text messages to blur the style and wording of the sender. Since pings are inbuild prior-defined macrostructures of the gaming communication, we have not altered these. This reconstruction of searchable visual online data is already practiced in criminological research as a mean to anonymize and protect research participants (see for example Bakken & Demant, 2019) Data management and processing were performed using the secured storage facilities provided by Aalborg University.

Analytical Strategy

Each of the 131 death incidents was assessed by the first author multiple times to become familiar with the interactional dynamics involved in each encounter. Based on this, the first author grouped the 131 death incidents into 86 death events. That is, if two or more teammates die at the same time or with only few seconds apart but in the same location, then this is grouped as one death event. The analyses then proceeded in two steps. First, each death event was categorized as whether it escalated into toxic encounters or not. To identify and categorize the encounters, we applied existing empirical research on toxic behaviors in online games. By reviewing existing research, we had a general categorization of already-identified types of toxic behaviors, encompassing, e.g., offensive language, ping-spamming, trash-talking, intentional feeding, threatening players, and leaking information to enemy players. The death events were categorized as if they escalated into one or more of these types of toxicity. Second, an iterative and explorative coding process searching for interactional and situational dynamics involved in death events escalating into toxic encounters was conducted. As part of this coding procedure, the transcription software program, Dote, was used to systematically transcribe each encounter (McIlvenny et al., 2022). This process was performed in parallel with creating coding categories for the interactional dynamics involved. The death events escalating into toxic encounters were studied step-by-step, analyzing the interactional and situational properties involved, and creating comprehensive storylines (Nassauer & Legewie, 2021).

Results

Based on our observational data on naturalistic toxic encounters emerging from death events, an overview of teammate death events was generated. It should be noticed that this overview does not offer a comprehensive list encompassing any type of death events in League of Legends or other online multiplayer online games. Table 1 is exclusively generated based on identified death events from our observational data. Therefore, it does not contain toxic death events such as Intentional Feeding or Teammate Killings despite such behaviors have proven to be prevalent in online games.

Table 1 Teammate death events in League of Legends
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Across the 86 identified death events, only 20 escalated into toxic encounters. Most death events proceeded unnoticed or uncommented by teammates. That is, none of the teammates reacted with toxic confrontation nor comforting gestures, e.g., cheering up the killed teammate. While this finding is tentative, it strongly implies that death and killing events are experienced as an integral part of online competitive games. Furthermore, it underscores that although toxic remarks frequently proceed from death events (Martens et al., 2015), far from all death events lead to toxicity. Rather, whether these events escalate into toxic encounters tend to be highly context sensitive as to the situational and interactional dynamics at play.

For example, we identified instances of death events in which teammates subsequently would apologize for getting killed. The killed teammate would write “mb” (my bad) or “sry” (sorry), which tended to de-escalate the emergence of toxic confrontations from teammates. This interactional pattern of de-escalation appeared even in encounters with teammates engaging in minor expressions of toxic confrontation, such as marking the death event with a question mark in the team chat as shown in the following encounter:

Excerpt from team chat:

[31:18] Zelian is killed by Nunu and Tristana

[31:20] Malzahar.chat: ?

[31:21] Zelian.chat: mb [my bad]

[31:22] Garen.pings: is on the way

[31:22] Garen.pings: is on the way

The ritualized work of Zelian in gesturing an apology appear to de-escalate a potential conflict between Malzahar and Zelian. This situation exemplifies how even minimal expressions of apologies (i.e., writing “mb”) hold the potential of altering situational outcomes (Heritage et al., 2019). The interchange of apologizing rituals and acceptance from teammates implies how apologies play an important role in re-establishing an expressive order as part of the social activity in online competitive games (Goffman, 1967, 1971). While apologizing rituals only appeared in a small handful of death events and should therefore be investigated more in-depth and with a larger sample in future studies, we did not observe any encounters escalating into toxicity if an apology for death was provided.

From Corrective Work Prior to Death Event to Character Contest

In death events characterized by corrective work prior to the death of a teammate, we observe how interactional and situational properties are at play in escalating the event into toxic encounters. An escalation from death event into a toxic encounter is illustrated in the following situation:

Situation 1:

Three enemy players, Garen, Nami, and Fate, are running towards the middle lane where Shaco and Yasuo are located. Two of their teammates, Ezreal and Pantheon, are already killed. The fifth teammate, Sion, is located in the top lane fighting against Diana. Shaco and Yasuo are thus facing three enemy players with no possible assistance from teammates. As the enemy players are getting closer, Shaco withdraws from the situation while pinging “signals to be careful.” Shaco’s movement away from the situation and use of pings may be signals to Yasuo on not getting into battle. However, despite the warnings from Shaco, Yasuo enters the fight and a 3 v 1 battle arises with a fourth enemy player, Samira, running down mid lane to join (see Fig. 3). Subsequently, Yasuo is killed a few seconds later.

Fig. 3
figure 3

Shaco and Yasuo fighting in mid lane against three enemies [15:50]

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Excerpt from team chat

[15:52] Shaco.pings:signals to be careful

[15:54] Shaco.pings:signals to be careful

[15:57] Samira has shut down Yasuo! (Fig. 4)

Fig. 4
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Yasuo has been killed (15:57)

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[15:59] Ezreal.pings: signals that enemies are missing

[16:00] Shaco.pings:Yasuo – Respawning in 33s

[16:00] Shaco.pings:Yasuo – Respawning in 33s

[16:01] Yasuo.chat:shacoo?

[16:03] Yasuo.chat:r u awake?

[16:06] Ezreal.chat:they were four

[16:12] Ezreal.chat: so no, he won’t follow you

[16:13] Yasuo.chat:u r ad, do ur job [ad: attack damage]

[16:14] Ezreal.chat:lol

Prior to the death of Yasuo, we observe how the two teammates, Shaco and Ezreal, take on the responsibility of gesturing and signaling appropriate behavior to Yasuo using pings and movement. By calling attention to Yasuo, Shaco and Ezreal imply that the actions of Yasuo are a “threat that deserves direct official attention” (Goffman, 1967 p. 19). Responding to this potential crisis, movement and pings are exchanged to alert Yasuo that he—because of the direction he is taken—risks jeopardizing the game and engages in actions that are expressively incompatible with expected lines of action. The movement and pinging gestures of Shaco and Ezreal appear to establish a form of “corrective process” by which Yasuo has a chance to correct his behavior in accordance with what is considered as appropriate actions in this situation. Nevertheless, Yasuo does not—intentionally or non-intentionally—follow the acting instructions from Shaco and Ezreal and is, subsequently, killed by the enemy team.

Arising from this situation, Shaco and Ezreal perform a small series of minor yet significant interpersonal rituals of toxic actions as response to the death of Yasuo. These actions are carried through by the use of pings. In League of Legends, pings are effective player alerts that provide gameplay information to the entire team and occur as sound and notification in the team chat. As isolated gameplay actions, utilizing pings does not convey toxicity in itself. However, these inbuild game mechanics can be misused by players during matches. Known as ping-spamming, players may utilize pings to disrupt the attention of teammates, for example by repeatedly sending pings characterized by low relevance or utility for the gameplay (Kordyaka et al., 2020; Kou & Gui, 2014; Monge & O’Brien, 2021). The disruption of gameplay attention, thus, constitutes an interactional cue for toxicity as the disruptive ping-spamming or misuse of pings may violate the situational rules for interaction.

By utilizing “signals that enemies are missing” and “Respawning”-ping functions in the situation, Ezreal and Shaco are directing attention towards the death of Yasuo. For Shaco, this behavior is repeated twice in less than one second. Though, because of inbuild gaming features making announcement whenever a player is killed, visually as well as with sound, everyone has already witnessed or been informed of the death of Yasuo. Directing attention towards his death is therefore not necessary in terms of sharing gameplay information. Thus, Shaco’s and Ezreal’s utilization of pings appears to convey symbolic expressions that insinuate dissatisfaction with the performance of Yasuo, and consequently, threaten the face of Yasuo. In Shaco’s and Ezreal’s view of the situation, Yasuo should not have made the decision of getting into the battle. This is the heart of the dispute.

In comparison to other types of toxic behaviors, such as hate speech or rape threats, the misuse of pings or ping-spamming may constitute rather minor interpersonal toxic actions. Nevertheless, just as with many other types of toxic behaviors, these little rituals of toxic interpersonal actions convey evidence of judgment of social worth available to be witnessed by an audience (Goffman, 1967). This resembles similar findings on social interactions in temporary League of Legends teams. According to Kou and Gui (2014), players tend to blame others for their own mistakes. Furthermore, interpersonal actions of blaming teammates are found to be a conversational trait of toxic players (Neto & Becker, 2018).

As illustrated in the excerpt, the situation escalates further. In taking action against the actions of Shaco, Yasuo performs face-defensing rituals that challenge Shaco’s definition of the situation and transform the encounter into a confrontation. Attributes of character are brought into the dispute as something that can be gained and lost, and by characterizing Shaco’s actions as an expression of poor understanding of the situation (“r u awake?”), Yasuo is defending his own status and respect by challenging the character of Shaco (Goffman, 1967). However, while Yasuo attempts to structure the dispute around the poor judgment of Shaco for not engaging in the battle, Ezreal makes the claim that Yasuo himself is responsible for his own death:

“They were four. So no, he [Shaco] won’t follow you”

These conflicting accounts of a shared event become a fateful moment of truth. The ritualized contest between Shaco, Ezreal, and Yasuo expresses a special kind of moral combat. From the experience of Ezreal and Shaco, a moral rule regarding gameplay has been violated by Yasuo’s poor judgment in the situation as disregarding team instructions. Ezreal and Shaco call attention to the offense, emphasizing that an offense has been committed. Though, Yasuo refuses to accept the framework presented by Ezreal and Shaco, neither apologizing nor backing down but responds with his own moral claim that Shaco is responsible for the outcome of the situation. The counter-response of Ezreal does not offer Yasuo any satisfaction or restoring face-work on the offense Yasuo feels Shaco has committed, thus transforming the encounter into a character contest (Goffman, 1967).

A Request for Help

Death events characterized by help requests tend to escalate into toxic encounters as the killed teammate counterattack his or her teammates—either by attacking certain teammates or the entire team. Here, this is exemplified by interaction strategies employed by Malzahar in establishing a definition of the situation:

Situation 2:

Malzahar is fleeing from Nunu in midlane (Fig. 5). Shortly before this scene, Malzahar has pinged his teammates “is asking for assistance.” The teammate, Garen, responds and pings that he “is on the way” while another teammate, Kai’Sa, attacks Nunu from behind. Nunu keeps attacking Malzahar. Two other enemy players, Graves and Rell, enters the battle. It is now a 3 v 2 battle with Malzahar low on health points. The three enemy players surround Malzahar. Kai’Sa keeps shooting at Nunu, but the enemy players manage to kill Malzahar anyway (Fig. 6). Kai’Sa tries to escape the scene but is killed a few seconds later by Tristana and Rell.

Fig. 5:
figure 5

Malzahar fleeing from Nunu (11:11)

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Fig. 6
figure 6

Malzahar getting killed by Nunu (11:17)

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Excerpt from team chat:

[11:18] Malzahar is killed by Nunu, Graves, and Rell

[11:20] Malzahar.chat:okk

[11:22] Malzahar.chat:ff XD [forfeit]

[11:25] Malzahar.chat:u guys sck

[11:26] Kai’Sa is killed by Tristana and Rell

[11:27] Garen.pings:Malzahar – Respawning in 20s

[11:28] Malzahar: well it is [level of game removed]

[11:29] Malzahar.chat:so np [no problem]

[11:31] Malzahar.chat:just ff [forfeit]

In League of Legends, writing “ff” is a reference to forfeit indicating that the team should surrender. As players cannot vote for surrendering before 15 min of gameplay, we observe across data how players utilize writing “ff” in negotiating if the team should surrender or not. By writing “ff” in the team chat as well as “(yo)u guys s(u)ck”, Malzahar is actively projecting the outcome of the death event as a consequence of his teammates’ poor performances. These interpersonal actions of Malzahar resemble recent research on trolling behavior as trolls tend to blame others for mistakes made, repeating words, and positioning themselves as the dominating interactant in team chat (Cook et al., 2019; Neto et al., 2017; Neto & Becker, 2018; Shachaf & Hara, 2010).

As a result, a counter-interactional strategy emerges during the situation with the teammates displaying deference avoidance rituals (Goffman, 1967). The only teammate that does engage is Garen and he only engages with a minimum using just one “Respawning”-ping. The silence treatment suggests an interactional strategy on not making the situation escalate into a more serious character contest. Such actions of actively ignoring trolls are often labeled “Don’t feed the troll”-strategies and have previously been reported as a common counter-strategy towards trolling behaviors in online games (Cary et al., 2020; Cook et al., 2018; Hilvert-Bruce & Neill, 2020). This suggests how the teammates may be actively ignoring Malzahar as an attempt to structure and manage the situation. That is, they tacitly collaborate on sustaining a definition of the situation and the interaction order.

Finally, it is worth noting that, according to Goffman, requests as remedial interchanges constitute a special kind of ritual work (Goffman, 1971:114). To Goffman, a request serves as permission to potentially violating the territories of the other’s self (ibid.). Thus, requests constitute remedial interchanging work for managing normative structures prior to questionable situations. However, the failure to accommodate a teammate’s request for help appears to be involved in escalating the situation. While the very act of requesting for help in an online gaming context in itself is neither toxic nor violating the territories of the other’s self, this ritualized work prior to death events appears to be involved in escalating and transforming the situation into toxic encounters.

As we have seen, these two situations illustrate how interactional and situational properties are at play in escalating death events into toxic encounters—as well as de-escalating the situations, i.e., through apologizing rituals. Especially, different forms of situational rules and interactional ritualized work appear to structure the encounters and the situational outcomes. Death events are thus not isolated events but situated within specific contexts. On the contrary, it appears that situational rules and interaction rituals are structuring the behaviors of the players, prior to, during, and after death events. Hence, whether the situations escalate into toxicity appear to be highly sensitive to micro-sociological processes. For example, the performance of interaction rituals prior to death events, such as failures in requesting help or correcting player behaviors, seem to structure the emergence and expressions of toxic behaviors. Likewise, interaction rituals performed subsequently death events show potential in both escalating and de-escalating the situations, for example by performing apologizing gestures. Furthermore, the interpersonal rituals of toxic actions performed by teammates demonstrate a difficulty in identifying just one single player as responsible for the toxic encounters as players transitions from different role positions throughout the encounter. These tentative findings on interactional properties aligns with existing studies on the interactional dynamics involved in trolling interactions. As identified by Cook et al. (2019), the transition from victim to perpetrator can occur extremely rapidly in trolling interactions.

Discussion and Conclusion

In this study, we addressed Goffman’s call for studies of game situations to explore regularities of interaction orders and explored the analytical potential of applying his micro-sociological and situational framework to the study of toxic communication- and interaction practices in online gaming. In doing so, and by applying Goffman’s own theoretical framework, we have offered a new conceptual understanding of the situational and social structuring of player behaviors in online gaming focusing specifically regarding situations of conflict. When entering the battlefield, players enter focused encounter units organized by acts and gestures. For the players, these focused encounters involve being in each other’s immediate presence, mutual attention, sharing activities, and in this co-presence of others, being vulnerable to gestures and verbal actions penetrating the territories of the self, and the breaching of a social order expected to be maintained (Goffman, 1983). Evidently, death events are common in League of Legends and other multiplayer battle games, but our analysis indicates that under certain situational conditions, these events result in toxic behaviors of which we have identified certain, tentative forms. Through transgressing the territories of the self of other(s), displaying disrespect and disregard towards the social gathering, these social encounters transform into situations of conflict and confrontations. As a result, the gaming encounter becomes a field of fateful dramatic action through the violation of interactional rules in the situational gaming order. As such, our study extends previous research identifying and cataloging toxic online behavior (Kou, 2020; Kowert, 2020; Kwak et al., 2015) by suggesting a micro-sociological theoretical understanding that renders these behaviors socially patterned and structured by micro-social properties.

However, with this study, we have, of course, not provided a universal theoretical explanation to the problem of toxic behavior in online gaming, rather we have taken a first, exploratory step to be followed by further studies in other gaming contexts as well as by replications in the League of Legends-context studied here. Also, we have pointed to a new level of analysis, the sociological micro-analysis, in studies of toxic behaviors in online gaming that hopefully will inspire future research in this field. Micro-analyses as the one provided here may not only provide insights into the social patterning of toxic behavior, but they may also shed important light on fundamental social phenomena such as collective behavior, social exclusion, deviance, and bystander behavior. Furthermore, this study points towards the methodological advantage of using video data to explore behaviors, interactions, and situational dynamics in situations of toxicity in online gaming. An important implication of this exploratory study is that toxic behaviors in online gaming cannot be separated from the social situation in which they occur and thus that the normative structuring of online gaming encounters should be taken into consideration when accounting for the specific group processes and dynamics. Further, this implication warrants caution in terms of generalizing specific types of online gaming behaviors from one online context to another until further studies in different context have been reported.

Our findings and the proposed micro sociological framework hold implications in terms of policy and interventions aiming at mitigating toxic behavior in online gaming and thus promoting more socially inclusive and sustainable gaming cultures. First, our analysis directs attention from individual factors among players such as psychological, personality, or cognitive factors to micro sociological drivers of toxicity. This is not, of course, to downplay the potential of interventions based on individual level factors, but to suggest complementary forms of interventions based on the social mechanisms at play. Similarly, our approach points to interventions that may supplement boycotting or banning interventions and the range of AI-based detection systems (see for example Canossa et al., 2021; Stoop et al., 2019; Weld et al., 2021) aiming at predicting when severe forms of toxic and deviant behaviors may occur in game situations. Supplementing such measures, our framework underscores the potential for interventions to target the micro-social interactions and the socially ordered micro-worlds contextualizing and framing player behaviors.

A tangible avenue for such interventions may be the application of social-norm interventions which have proven useful in providing social change in a range of different fields (Sunstein, 2020). While aligning with our suggested theoretical framework, such an approach also falls in line with recent research suggesting that direct confrontation of harassment from participants in online games is effective in mitigating such behaviors (Tang et al., 2020). Social norms interventions, which take different forms, are based on the idea that social norms are significant forces in group processes and that behavior change can be facilitated by affecting these norms using detailed knowledge of the social group in question. Prentice and Paluck (2020) have delineated two specific strategies that might prove useful in mitigating toxic behaviors in online gaming and align with the framework suggested in this study. One strategy would imply a targeting of social referents (the most influential players) to which other players look to decode group rituals and norms (Prentice & Paluck, 2020, p. 139). Social referents in online gaming groups could be approached and instructed in mitigating deviant and toxic behaviors by explicitly counteracting such behaviors. Similar interventions of micro-targeting specific individuals with the potential to trigger deviant behaviors have been suggested in recent studies of spread of cheating behavior in online gaming (Kim & Tsvetkova, 2022). Another form of intervention would be directed at weakening the norms (Prentice & Paluck, 2020, p. 140) allowing for toxic behaviors to take place. As some gamer cultures have developed into toxic spheres in which non-conformers face the risk of exclusion (Kowert, 2020), interventions that aim at weakening the beliefs supporting aggression in online games (Hilvert-Bruce & Neill, 2020) may facilitate change in the direction of a more inclusive and non-toxic game environment. As recent studies suggest that while large proportions of gamers think that negative behavior should be confronted in online and only a minority stand up to harassment when experiencing it (Cary et al., 2020), it is plausible that deviant norms have only limited support in the online game environment and that reality-testing interventions aiming to puncture ideas of universal toxic norms may reduce toxic behaviors. While interventions of the kind suggested above need further refinement and operationalization as well as the active involvement of game providers and the gamers themselves, it is likely that such measures would nurture the development of more inclusive online game cultures. Finally, our study points to the creation of game design that allows players better to recognize the impact that their game behavior has on the overall game situation. Such measures could take the form of feedback systems informing players of their gameplay impact which in turn may nurture a sense of shared responsibility (Williams & Wall, 2007).

This study has demonstrated the applicability of a specific micro-analytic approach to online situations of conflict and thus advanced the conceptual understanding of toxicity in online gaming. A notable strength of the study lies in its relatively high level of ecological validity. Gamers who provided video recordings may be seen as “co-researchers” pointing to what they perceive as toxic behaviors (e.g., by moving and pointing with around the area in which perceived toxicity occurred) and providing video recordings that they themselves categorize as involving examples of toxic behavior. However, in addition to these strengths, several limitations should be noted. First, given the design of the study, we had no information on already established relations (e.g., offline friendships) among the players involved in the video recording. Evidently, such relations may impact and structure the interactions that were in the center of our analysis; however, the extent and scope of such potential impact were not accessible. Second, the video recordings suffer from perspectivism as the camera angle follows one participant (the recording one) as does not allow for a 360 degree-perception of the situations at hand. This shortcoming could be avoided in replica studies using more sophisticated video-recording systems. Third, in our use of Goffman’s situational framework, we have underappreciated the impact of wider social structures, roles, and norms on the observed online behavior. While this follows naturally from our research design and the nature of our data material, we would advise future studies to take advantage of Goffman’s notion of a louse coupling between micro- and macro level factors and attempt to investigate the situational impact from meso- and macro-level factors. Finally, the gender distribution was not clear. As our primary data were unsupervised video recordings with no coding of gender, we were not able to determine whether or to what extent female players were present in game situations. We did not observe many examples of sexism in our data, and it is likely that we would have found other forms of sexistic harassment with a larger proportion of female players in our data.