Investigating the emotional experiences in eSports spectatorship: The case of League of Legends
《调查电子竞技观众的情感体验：英雄联盟》案例

Dataset link: https://github.com/finalfire/emot ional-experience-esports
数据集链接：https://github.com/finalfire/emot ional-experience-esports

eSports 电子竞技
Emotional experience 情感体验
Spectatorship 观众身份
Reddit
Social network analysis 社会网络分析
Thematic analysis 专题分析

Electronic sports (eSports) is "competitive (pro and amateur) video gaming that is often coordinated by different leagues, ladders and tournaments, and where players customarily belong to teams or other ‘sporting’ organizations who are sponsored by various business organizations " (Hamari & Sjöblom, 2017). eSports enacts and legitimizes video games as spectator-driven sports in ways that are typical in the traditional sport worlds of soccer and basketball, such as live tournament casts, interviews with players, and broadcasting of replays, highlights, and documentaries (Taylor, 2016). Large eSports tournaments such as League of Legends’ World Championship enjoy a massive-scale viewer base around the globe (Kelly, 2020).
电子竞技（eSports）是 "竞技性（职业和业余）视频游戏，通常由不同的联赛、梯队和锦标赛协调，玩家通常属于由各种商业组织赞助的团队或其他'体育'组织"（Hamari & Sjöblom，2017）。电子竞技以足球和篮球等传统体育世界中的典型方式，将视频游戏作为观众驱动的体育运动，并使之合法化，例如赛事直播、玩家访谈、重播、集锦和纪录片（Taylor，2016）。大型电子竞技比赛，如英雄联盟世界锦标赛，在全球拥有大规模的观众群（Kelly，2020）。

Irrespective of their familiarity with or personal involvement in video gaming, eSports viewers can engage in the digitally mediated, interactive experience of spectating for a variety of motivations and interests. Some who have a limited understanding of the game could learn from the pros and get inspired; some seek entertainment from watching eSports just like watching a TV show; and some enjoy the feeling of belonging to a crowd of spectators (Cheung & Huang, 2011). As such, scholars have been able to draw many parallels between eSports and traditional sports spectatorship in terms of social support, fanship, and knowledge acquisition (Brown, Billings, Murphy, & Puesan, 2018). Some scholars have also investigated how to design technologies to enhance such spectator experiences (Kriglstein et al., 2020).
电子竞技观众无论对电子游戏是否熟悉或参与程度如何，都可以出于各种动机和兴趣参与以数字为媒介的互动式观赛体验。一些对游戏了解有限的人可以向专业选手学习，从中得到启发；一些人则像看电视节目一样，从观看电子竞技中寻求娱乐；还有一些人则享受属于一群观众的感觉（Cheung & Huang，2011）。因此，学者们能够从社会支持、粉丝精神和知识获取等方面得出电子竞技与传统体育观众之间的许多相似之处（Brown、Billings、Murphy 和 Puesan，2018）。一些学者还研究了如何设计技术来增强这种观众体验（Kriglstein et al.）

https://doi.org/10.1016/j.ipm.2023.103516
Received 3 May 2023; Received in revised form 20 September 2023; Accepted 25 September 2023
2023 年 5 月 3 日收到；2023 年 9 月 20 日收到修订稿；2023 年 9 月 25 日接受
Available online 5 October 2023
可于 2023 年 10 月 5 日在线查阅
0306-4573/© 2023 Elsevier Ltd. All rights reserved.
0306-4573/© 2023 爱思唯尔有限公司。保留所有权利。

Importantly, while viewers of traditional sports undergo a broad spectrum of emotions (Chang, 2019; Oshimi, 2015), evidence has been accumulating that rich emotional experiences are also profound in eSports spectatorship. eSports spectators have emotional investments in the players, and are able to share emotional experiences within the spectator ecosystem (Cheung & Huang, 2011). Live streamers put up significant emotional labor to engage the audience (Woodcock & Johnson, 2019). eSports commentators actively develop an emotional connection with their spectators beyond providing information and content (Li, Uttarapong, Freeman, & Wohn, 2020). Chat rooms, considered a distinctive socio-technical mechanism for eSports spectatorship (Qian, Zhang, Wang, & Hulland, 2020), help foster a unique communicative culture where large crowds of spectators engage in intense emotional communication at a rapid pace (Ford et al., 2017). These prior works have pointed to the emotional facet of eSports spectatorship, indicating the significance of emotions in the eSports spectating experience. However, little work has been centered on the emotional experiences of eSports spectators. Thus, as an early investigation into eSports spectating experience, we ask the following exploratory question: “How to characterize eSports spectators’ emotional experiences?”.
重要的是，虽然传统体育观众经历了广泛的情感体验（Chang, 2019; Oshimi, 2015），但越来越多的证据表明，丰富的情感体验在电子竞技观众中同样深刻。电子竞技评论员除了提供信息和内容外，还积极与观众建立情感联系（Li, Uttarapong, Freeman, & Wohn, 2020）。聊天室被认为是电子竞技观众身份的一种独特的社会技术机制（Qian，Zhang，Wang，& Hulland，2020），有助于培养一种独特的交流文化，在这种文化中，大批观众以极快的速度进行激烈的情感交流（Ford 等人，2017）。这些先前的研究指出了电子竞技观赛的情感层面，表明了情感在电子竞技观赛体验中的重要性。然而，很少有研究以电子竞技观众的情感体验为中心。因此，作为对电子竞技观赛体验的早期调查，我们提出了以下探索性问题："如何描述电子竞技观众的情感体验？

In this paper, we explore eSports spectators’ emotional experiences, focusing on one eSports event which is League of Legends (LoL)‘s 2020 World Championship. Inspired by previous studies investigating how traditional sports spectators share emotional experiences (Delia & Armstrong, 2015; Stavros, Meng, Westberg, & Farrelly, 2014), and eSports’ digitally mediated nature, we analyze the social media speeches of eSports onlookers during the event. In particular, we specifically collected the discussions that took place among spectators during the event on the social media platform Reddit. Through a combination of computational and qualitative analyses on this data, we show that LoL spectators actively expressed their team identifications, and engaged in emotional communication with fellow spectators. Their emotional expressions are patterned, pertaining to both the paths of their teams as well as episodes in the eSports event. In our computational analysis, we identify the expressed support given to a team by spectators, and we study their interactions borrowing social network analysis techniques; also, we define breakpoints, i.e., particularly important event matches, and we study spectators’ experience before and after them. In the qualitative analysis, we investigate several emotional dimensions via qualitative methods, observing how spectators express themselves on the social platform. Also, we study these dimensions through the lens of six major emotive factors, such as fan groups’ interactions and gameplay quality. Our investigation connects both ends, forming a unified knot in the form of a mixed-methods, empirical exploration of the emotional experiences of eSports spectators. This not only showcases the inherent dynamics of emotional communication facilitated by social media platforms but also prompts reflections on how eSports spectating systems can be enhanced to provide a more engaging viewing experience. Our approach provides a meticulous investigation whose nature is heterogeneous, i.e., it can also be comfortably applied in other similar contexts. Thanks to the proposed methodology, we are able to generate several key findings. We observe that distinctive features of eSports spectator behavior, such as breakpoints and teams’ recognition, have a considerable impact on spectators’ presence and interaction in the discussion. For instance, spectators’ interactions tend to vary when the supported team suffers a loss. Also, we note that spectators of different teams exhibiting rivalry tend to interact more. Lastly, we identify and characterize six primary emotive factors that can trigger spectators’ emotions, highlighting the affective dimension of the eSports spectatorship. These emotive factors pertain to various aspects of the eSports spectating experience, and collectively indicate a profound emotional underpinning for the spectator ecosystem to mobilize.
在本文中，我们将以英雄联盟（LoL）2020 年世界锦标赛这一电子竞技赛事为重点，探讨电子竞技观众的情感体验。受之前调查传统体育观众如何分享情感体验的研究（Delia & Armstrong, 2015; Stavros, Meng, Westberg, & Farrelly, 2014）以及电子竞技的数字媒介性质的启发，我们分析了电子竞技围观者在赛事期间在社交媒体上的发言。特别是，我们专门收集了赛事期间观众在社交媒体平台 Reddit 上的讨论。通过对这些数据进行计算分析和定性分析，我们发现 LoL 观众积极表达了他们的团队认同，并与其他观众进行了情感交流。他们的情感表达是有规律的，既与他们的队伍的发展轨迹有关，也与电子竞技赛事中的情节有关。在计算分析中，我们确定了观众对战队表达的支持，并借用社交网络分析技术研究了他们之间的互动；此外，我们还定义了断点，即特别重要的赛事，并研究了观众在这些赛事前后的体验。在定性分析中，我们通过定性方法研究了几个情感维度，观察观众如何在社交平台上表达自己。此外，我们还通过六大情感因素（如球迷群体的互动和游戏质量）来研究这些维度。我们的调查将两端连接起来，形成了一个统一的结，即对电子竞技观众情感体验的混合方法实证探索。 这不仅展示了社交媒体平台促进情感交流的内在动力，还引发了对如何增强电子竞技观赛系统以提供更具吸引力的观赛体验的思考。我们的方法提供了一种细致的调查，其性质是异质的，也就是说，它也可以轻松地应用于其他类似的环境中。得益于所提出的方法，我们能够得出一些重要发现。我们观察到，电子竞技观众行为的独特特征，如断点和战队识别，对观众在讨论中的存在和互动有相当大的影响。例如，当支持的队伍输掉比赛时，观众的互动往往会有所不同。此外，我们还注意到，表现出竞争关系的不同球队的观众往往会有更多的互动。最后，我们识别并描述了能够引发观众情绪的六个主要情感因素，突出了电子竞技观众身份的情感维度。这些情感因素涉及电子竞技观赛体验的各个方面，共同表明了观赛生态系统可以调动的深厚情感基础。

The outline of this paper is as follows. Section 2 is devoted to providing the reader with essential background on LoL, the Worlds event, and Reddit. Section 3 illustrates the research objectives of this study, while Section 4 lays out an analysis of the related literature. Section 5 provides an overview of the data collected in this work and the adopted methodology used to analyze it. In Section 6 , the results of the proposed investigation are presented from both computational and qualitative perspectives. Section 7 offers a discussion on the context and its theoretical and practical implications. Finally, in Section 8 we conclude and introduce future works.
本文大纲如下。第 2 节主要向读者介绍有关 LoL、世界赛事和 Reddit 的基本背景。第 3 节阐述了本研究的研究目标，第 4 节对相关文献进行了分析。第 5 节概述了本研究中收集的数据以及采用的分析方法。第 6 部分从计算和定性角度介绍了建议的调查结果。第 7 节讨论了背景及其理论和实践意义。最后，在第 8 节中，我们总结并介绍了未来的工作。

We now describe the background of our proposed investigation. In particular, we first introduce the Lol game in Section 2.1. Subsequently, in Section 2.2, we provide information on the social media platform Reddit, elucidating how both the game and the related spectatorship experience are represented in it.
现在，我们来介绍一下拟议调查的背景。在第 2.1 节中，我们首先介绍了 Lol 游戏。随后，在第 2.2 节中，我们将提供有关社交媒体平台 Reddit 的信息，阐明游戏和相关的观赛体验是如何在该平台上体现的。

League of Legends (LoL) is one of the largest online games today. Developed by Riot Games (California, USA), it belongs to the genre of multiplayer online battle arena (MOBA) games. LoL provides different permanent and temporary gameplay modes. The most popular one is a match-based game. Each match takes place between two competing teams composed of five players, where each player selects and operates a character through the whole match. Players are expected to work together in order to conquer the enemy base. An interesting aspect of LoL is that a highly competitive gaming scene has been built around it over the years. One of the most important events in this scene is the LoL World Championship (commonly abbreviated as Worlds), in which several teams from different regions around the world compete for the champion title and a multi-million dollar prize ${}^1$${}^1$^(1){ }^{1}. The tournament format consists of round-robin groups and single eliminations. In the former part, the teams play in a competition and meet others, usually in turn. Then, the latter part involves matches in which the defeated team is immediately eliminated from the tournament.
英雄联盟（LoL）是当今最大的网络游戏之一。它由 Riot Games（美国加利福尼亚州）开发，属于多人在线竞技场（MOBA）游戏类型。LoL 提供不同的永久和临时游戏模式。最受欢迎的是基于匹配的游戏。每场比赛在由五名玩家组成的两支竞争队伍之间进行，每位玩家选择并操作一名角色完成整场比赛。玩家要齐心协力征服敌方基地。LoL 的一个有趣之处在于，多年来围绕它已形成了一个高度竞技化的游戏场景。其中最重要的赛事之一是 LoL 世界锦标赛（通常缩写为 Worlds），来自世界各地不同地区的多支队伍将角逐冠军头衔和数百万美元的奖金 ${}^1$${}^1$^(1){ }^{1} 。比赛形式包括小组循环赛和单败淘汰赛。在前一部分，参赛队在比赛中与其他队交手，通常是依次进行。然后，在后一部分比赛中，落败的队伍会立即被淘汰出局。

Fig. 1. A graphical representation of a Reddit discussion.
图 1.Reddit 讨论的图示。

The tournament usually takes place in a region: matches are played live, and streamed through different streaming services such as Twitch, YouTube, etc. The event attracts a significant number of viewers every year. As an example, the Worlds 2018 final was watched by 99+ million people ${}^2$${}^2$^(2){ }^{2}.
比赛通常在一个地区进行：比赛现场直播，并通过 Twitch、YouTube 等不同的流媒体服务进行流媒体传输。赛事每年都会吸引大量观众。例如，2018 年世界锦标赛决赛的观看人数超过了 9900 万 ${}^2$${}^2$^(2){ }^{2} 。

Originally self-declared as “the front page of the Internet”, Reddit ${}^3$${}^3$^(3){ }^{3} is a heterogeneous, crowd-sourced news aggregator and online social platform. It was founded in 2005 and, at the time of writing, it had $52\mathrm{M}+$$52\mathrm{M}+$52M+52 \mathrm{M}+ daily active users and $100\mathrm{K}+$$100\mathrm{K}+$100K+100 \mathrm{~K}+ communities, totalizing more than 50B monthly views ${}^4$${}^4$^(4){ }^{4}. Reddit’s ecosystem is based on the concept of subreddit. Usually, a subreddit is an interestbased community, although more general cases exist. A subreddit is distinguished by its name and is referred to using the /r/ prefix within Reddit, such as /r/science and /r/funny. Users can submit contents, which are simply called posts or discussions, in subreddits. The content can be read by other users and discussed via comments. One of the distinctive features of Reddit is voting, which is a mechanism affecting the visibility and the ranking of both posts and comments. A user has the capability to upvote or downvote both posts and comments, with each of them being assigned a score determined by the disparity between the number of upvotes and downvotes received. For the unfamiliar reader, a graphical representation of a discussion on Reddit is depicted in Fig. 1. The main elements of a discussion, such as comments and their scores are presented. The figure also depicts flairs. A flair is a subreddit-defined label, and each author can select one or more flairs to appear to the side of her public name whenever she posts a post or a comment. As we will see in the following, these are of crucial importance for several analyses.
Reddit ${}^3$${}^3$^(3){ }^{3} 最初自称是 "互联网的头版"，是一个异构的众包新闻聚合器和在线社交平台。它成立于 2005 年，在撰写本文时，它拥有 $52\mathrm{M}+$$52\mathrm{M}+$52M+52 \mathrm{M}+ 个日活跃用户和 $100\mathrm{K}+$$100\mathrm{K}+$100K+100 \mathrm{~K}+ 个社区，每月总浏览量超过 500 亿次 ${}^4$${}^4$^(4){ }^{4} 。Reddit 的生态系统基于子红人区（subreddit）的概念。通常情况下，子红地毯是一个基于兴趣的社区，但也有更普遍的情况。在 Reddit 中，子红地名以其名称区分，并使用 /r/ 前缀，如 /r/science 和 /r/funny。用户可以在子版块中提交内容，也就是所谓的帖子或讨论。其他用户可以阅读这些内容，并通过评论进行讨论。Reddit 的一个显著特点是投票，这是一种影响帖子和评论的可见度和排名的机制。用户可以对帖子和评论进行向上投票或向下投票，每条帖子和评论都会根据获得的向上投票和向下投票的数量差距进行评分。图 1 是 Reddit 上讨论的图示，不熟悉的读者可以看一下。图中展示了讨论的主要元素，如评论及其分数。图中还描绘了标志。标志是一个由子红迪定义的标签，每位作者都可以选择一个或多个标志，每当她发表文章或评论时，这些标志就会出现在她的公共名称一侧。正如我们在下文中将看到的，这些标签对一些分析至关重要。

Reddit has attracted an incredible volume of research around the platform, its content, and its features. According to Semantic Scholar ${}^5$${}^5$^(5){ }^{5}, in the last ten years, more than 5,000 studies related to Reddit have been published. Reddit has become an invaluable source of information and research possibilities. As a matter of fact, many researchers of different disciplines studied Reddit and its contents from various points of view, such as sociological (Guimaraes, Balalau, Terolli, & Weikum, 2019; Ríssola, Aliannejadi, & Crestani, 2022; Yoo, Lee, & Ha, 2019) and computer science oriented ones (Arazzi, Nicolazzo, Nocera, & Zippo, 2023; Cauteruccio, Corradini, Terracina, Ursino, & Virgili, 2022; Datta & Adar, 2019; Kumar, Hamilton, Leskovec, & Jurafsky, 2018). For the interested reader, a very detailed overview is illustrated in Medvedev, Lambiotte, and Delvenne (2019), while a longitudinal analysis of the evolution of Reddit is presented in Singer, Flöck, Meinhart, Zeitfogel, and Strohmaier (2014).
Reddit吸引了大量围绕该平台、其内容和功能的研究。根据Semantic Scholar ${}^5$${}^5$^(5){ }^{5} 的统计，在过去的十年中，与Reddit相关的研究报告已经发表了5000多篇。Reddit已成为一个宝贵的信息来源和研究机会。事实上，许多不同学科的研究人员从社会学等不同角度研究了 Reddit 及其内容（Guimaraes, Balalau, Terolli, & Weikum, 2019; Ríssola, Aliannejadi, & Crestani, 2022；Yoo, Lee, & Ha, 2019）和面向计算机科学的研究（Arazzi, Nicolazzo, Nocera, & Zippo, 2023; Cauteruccio, Corradini, Terracina, Ursino, & Virgili, 2022; Datta & Adar, 2019; Kumar, Hamilton, Leskovec, & Jurafsky, 2018）。对于感兴趣的读者，Medvedev、Lambiotte 和 Delvenne（2019）中有非常详细的概述，而 Singer、Flöck、Meinhart、Zeitfogel 和 Strohmaier（2014）则对 Reddit 的演变进行了纵向分析。

Among the plethora of subreddits present in Reddit, /r/leagueoflegends is the main unofficial one devoted to the game itself. At the time of writing, it has more than 5 million subscribed users ${}^6$${}^6$^(6){ }^{6} and serves as an unofficial source of information about the game and related events. Indeed, the game is discussed from different points of view, and in particular from the one of competitive gaming. The eSports scene is thoroughly discussed in the subreddit, particularly during the Worlds period. In fact, during the event, users post specific Worlds-related posts called “post-match discussions”. Each of these posts reports information on the match, such as players’ statistics or in-game objective measures, and users can engage in discussions within these post-match discussions through comments, mirroring their interactions with other posts on Reddit.
在 Reddit 的众多子论坛中，/r/leagueoflegends 是专门讨论游戏本身的主要非官方论坛。在撰写本文时，它拥有 500 多万订阅用户 ${}^6$${}^6$^(6){ }^{6} ，是游戏和相关活动的非官方信息来源。事实上，该网站从不同的角度，特别是从竞技游戏的角度对游戏进行了讨论。电子竞技场景在该子版块中得到了深入讨论，尤其是在世界大赛期间。事实上，在赛事期间，用户会发布与世界赛相关的特定帖子，称为 "赛后讨论"。每个帖子都会报告比赛信息，如选手的统计数据或游戏中的客观指标，用户可以通过评论参与赛后讨论，这与 Reddit 上其他帖子的互动如出一辙。

In this section, we want to outline the specific research objectives of our investigation. It is worth pointing out that while the spectator experience of traditional sports has been comprehensively studied, eSports spectatorship and its emotional experience are understudied. Therefore, there are several aspects in this context that one could decide to study, ranging from the parallel between traditional and eSports spectatorship to the exploitation of computational methods to investigate users’ interactions and their emotional experiences. To better clarify our contribution to the literature and the aim of our investigation, we define two general research objectives that we will address through the paper:
在本节中，我们将概述我们调查的具体研究目标。值得指出的是，传统体育的观众体验已经得到了全面的研究，而电子竞技的观众及其情感体验却没有得到充分的研究。因此，在这一背景下，我们可以决定从多个方面进行研究，从传统和电子竞技观赛体验的平行关系，到利用计算方法来研究用户的互动及其情感体验。为了更好地阐明我们对文献的贡献以及我们的调查目的，我们定义了两个总体研究目标，我们将通过本文来实现这两个目标：

We would like to remind that the central theme of our research is the exploratory question of “How to characterize eSports spectators’ emotional experiences?” and we believe that ROs 1 and 2 provide a clear focus on this question.
我们要提醒的是，我们研究的核心主题是 "如何描述电子竞技观众的情感体验？"这一探索性问题，我们认为《规则》第 1 条和第 2 条为这一问题提供了明确的重点。

We position this work at the intersection of two research strands: a large and growing body of literature on spectatorship in eSports and the extant research on spectators’ emotions in traditional sports. In this section, we delve into these two strands, then we provide a final remark on the eSports context from a computer science-oriented perspective.
我们将这项工作定位在两个研究领域的交叉点上：一个是关于电子竞技中观众身份的大量且不断增长的文献，另一个是关于传统体育中观众情绪的现有研究。在本节中，我们将深入探讨这两个方面，然后从计算机科学的角度对电子竞技进行最后的评论。

eSports has been discussed and conceptualized in various terms such as competitive gaming, the digital format, and spectatorship (Freeman & Wohn, 2017). However, spectatorship has always been an integral part of eSports, in which video game players watch high-level gameplay. Because of its spectator-driven nature, eSports spectatorship resembles sport spectatorship in many ways. Seo and Jung (2016) observed that watching eSports is comparable to watching traditional sports, as both pertain to a particular assemblage of understandings, tools, skills, and competencies that spectators use to coordinate this practice. Hamari and Sjöblom deployed the motivations scale for sports consumption to understand eSports spectators, and reported that escapism, knowledge acquisition, novelty, and athlete aggressiveness could predict eSports spectating frequency (Hamari & Sjöblom, 2017). Studies of motivations of eSports spectating similarly pointed to a high degree of similarity between motives of traditional sports and eSports spectators (Brown et al., 2018; Cheung & Huang, 2011; Pizzo, Na, Baker, Lee, Kim, & Funk, 2018; Qian et al., 2020). Tang, Kucek, and Toepfer (2022) observed that while individual factors such as motivations and preferences predict both eSports gameplay and spectatorship, structural factors such as sports fandom and interactive features (e.g., chat and donation) only predict eSports spectatorship. In addition, some scholars have pointed to unique socio-technical processes that may characterize eSports spectator experience, such as chat rooms and virtual rewards (Qian et al., 2020).
电子竞技已被用各种术语进行讨论和概念化，如竞技游戏、数字形式和观赏性（Freeman & Wohn, 2017）。然而，观赏性一直是电子竞技不可或缺的一部分，在电子竞技中，电子游戏玩家观看的是高水平的游戏。由于电子竞技的观赏性是由观众驱动的，因此它在许多方面都与体育观赏性相似。Seo 和 Jung（2016 年）观察到，观看电子竞技与观看传统体育具有可比性，因为两者都涉及观众用来协调这一实践的特定理解、工具、技能和能力的组合。Hamari 和 Sjöblom 运用体育消费动机量表来了解电子竞技观众，并报告说，逃避现实、获取知识、新奇感和运动员的攻击性可以预测电子竞技的观赛频率（Hamari & Sjöblom, 2017）。对电子竞技观赛动机的研究同样指出，传统体育和电子竞技观众的观赛动机具有高度相似性（Brown 等人，2018；Cheung 和 Huang，2011；Pizzo、Na、Baker、Lee、Kim 和 Funk，2018；Qian 等人，2020）。Tang、Kucek 和 Toepfer（2022 年）观察到，虽然动机和偏好等个体因素可以预测电子竞技游戏性和观赏性，但体育狂热和互动功能（如聊天和捐赠）等结构性因素只能预测电子竞技观赏性。此外，一些学者指出，独特的社会技术过程可能是电子竞技观众体验的特征，如聊天室和虚拟奖励（Qian et al.）

Spectatorship can happen in many forms. Traditionally, dating back to as early as the 1980s, players must go to live events in order to spectate professional gameplay up close (Taylor, 2012, 2018). In recent years, media technologies such as live streaming easily support players to broadcast real-time gameplay to a wide audience (Kow & Young, 2013; Kriglstein et al., 2020), while allowing spectators to actively participate (Lessel, Mauderer, Wolff, & Krüger, 2017). The rise of live streaming platforms like Twitch.tv is particularly important to the growth of the eSports ecosystem because it allows spectators to engage with the game and learn about high-level play, provides a way for eSports players to be professionalized, and creates a new cultural phenomenon where streamers become aspirational celebrity figures (Johnson, Carrigan, & Brock, 2019). Thus, spectatorship enabled through live streaming platforms such as Twitch.tv is an important facet of the eSports culture, through both facilitating the spectating of eSports events, as well as normalizing the spectating of others’ gameplay in players’ everyday gaming practices.
旁观有多种形式。传统上，早在 20 世纪 80 年代，玩家就必须前往现场赛事，才能近距离观看职业比赛（Taylor，2012，2018）。近年来，流媒体直播等媒体技术轻松支持玩家向广大观众直播实时游戏（Kow & Young, 2013; Kriglstein et al., 2020），同时允许观众积极参与（Lessel, Mauderer, Wolff, & Krüger, 2017）。Twitch.tv等直播平台的兴起对电子竞技生态系统的发展尤为重要，因为它允许观众参与游戏并了解高水平的比赛，为电子竞技选手提供了职业化的途径，并创造了一种新的文化现象，使直播者成为令人向往的名人（Johnson、Carrigan和Brock，2019）。因此，通过Twitch.tv等直播平台实现的观赏性是电子竞技文化的一个重要方面，它既促进了电子竞技赛事的观赏性，也使在玩家的日常游戏实践中观赏他人的游戏正常化。

Spectatorship offers spectators a multi-faceted, immersive experience. From the uses and gratifications perspective, Sjöblom and Hamari (2017) observed that information seeking, tension release, social integrative, and affective motivations were positively associated with how many hours people spent on Twitch.tv. Gros, Wanner, Hackenholt, Zawadzki, and Knautz (2017) similarly reported that entertainment, socialization, and information are three key motivations for people to use Twitch.tv. Emphasizing the experiential aspect of spectatorship, Taylor (2018) remarked that “streamers work to convey the moment by moment of gameplay, externalize the internal, make visible visceral experiences, and render the affective legible to spectators”. To make live streaming
围观为观众提供了多方面的沉浸式体验。从使用和满足的角度来看，Sjöblom 和 Hamari（2017 年）观察到，信息寻求、紧张释放、社会整合和情感动机与人们在Twitch.tv上花费的时长呈正相关。Gros、Wanner、Hackenholt、Zawadzki 和 Knautz（2017）同样报告说，娱乐、社交和信息是人们使用Twitch.tv的三个主要动机。泰勒（Taylor，2018 年）在强调观赏性的体验方面时指出，"流媒体工作者努力传达游戏的每一瞬间，将内在外化，使内脏体验可见，并使观众的情感清晰可见"。为了使直播
really work, much labor falls onto the streamers in order to render an enjoyable spectating experience (Johnson & Woodcock, 2021). For example, streamers may devise a multiplicity of strategies to utilize digital interfaces in shaping their spectators’ experience (Reeves, Benford, O’Malley, & Fraser, 2005). Sometimes streamers invite a select few of spectators to co-perform in their streams (Li, Gui, Kou, & Li, 2019). Argumentation techniques such as real-time graphical representation of musical instruments could increase spectators’ subjective comprehension and improve their spectator experience (Capra, Berthaut, & Grisoni, 2020).
为了让观众获得愉悦的观赏体验，很多工作都落在了观众身上（Johnson & Woodcock, 2021）。例如，流媒体制作者可以设计多种策略，利用数字界面来塑造观众的体验（Reeves, Benford, O'Malley, & Fraser, 2005）。有时，流媒体会邀请少数观众在他们的流媒体中共同表演（Li, Gui, Kou, & Li, 2019）。论证技术（如乐器的实时图形表示）可以提高观众的主观理解能力，改善他们的观赏体验（Capra, Berthaut, & Grisoni, 2020）。

In the meantime, spectators are not just passive consumers of eSports content. The fact that a spectator could use their own device to spectate allows the “extreme customization” of experience (Taylor, 2012), in which spectators could take advantage of a range of streaming platforms’ affordances and external tools to customize how they spectate. Some enjoy participating in the collective typing activity in the chat room (Ford et al., 2017), while others may find their bonds with fellow spectators strengthened as they share these moments of collective spectatorship (Taylor, 2012).
同时，观众不仅仅是电子竞技内容的被动消费者。观众可以使用自己的设备进行观赛，这就实现了体验的 "极端定制化"（Taylor，2012），观众可以利用一系列流媒体平台的功能和外部工具来定制自己的观赛方式。有些人喜欢参与聊天室中的集体打字活动（Ford et al.

Taken together, although this work is focused on spectatorship in eSports, it is best understood in the broader eSports context as a form of cultural co-creation that is enacted through an assemblage of people, organizations, and technologies (Borowy & Jin, 2013; Taylor, 2018). Against this backdrop, this work sets off to investigate the spectator experience, with a focus on its emotional aspect.
综上所述，尽管本研究的重点是电子竞技中的观众身份，但最好将其理解为在更广泛的电子竞技背景下，通过人、组织和技术的组合而形成的一种文化共创形式（Borowy & Jin, 2013; Taylor, 2018）。在此背景下，本作品着手研究观众体验，重点关注其情感方面。

Emotion has always been an important facet of the spectatorship of traditional sports like soccer and basketball. Sports carry an enormous emotional load, where spectators’ emotions change rapidly in accordance with events on the sports field (Kim, Magnusen, & Lee, 2017; Moore, 2019; Wohl, 1970). A survey study of 466 football spectators in Portugal found that the emotion of joy has positive effects on spectators’ satisfaction and behavioral intentions (Biscaia, Correia, Rosado, Maroco, & Ross, 2012). Another survey of 1194 respondents highlighted the need to elicit positive emotions to boost attendance in the big four US-based major sport leagues (Jang, Byon, & Yim, 2020).
情感一直是足球和篮球等传统体育运动观赏性的一个重要方面。体育运动承载着巨大的情感负荷，观众的情绪会随着运动场上发生的事件而迅速变化（Kim、Magnusen 和 Lee，2017；Moore，2019；Wohl，1970）。一项针对葡萄牙 466 名足球观众的调查研究发现，喜悦情绪对观众的满意度和行为意向有积极影响（Biscaia, Correia, Rosado, Maroco, & Ross, 2012）。另一项针对 1194 名受访者的调查强调，在美国四大主要体育联赛中，需要激发观众的积极情绪以提高上座率（Jang, Byon, & Yim, 2020）。

Spectators’ emotional experiences can intensify when they identify with sports teams on the field (Sutton, McDonald, Milne, & Cimperman, 1997). Particularly, the discrepancies between their expectations and game outcomes could predict their emotional arousal (Oshimi, Harada, & Fukuhara, 2014). reported that spectators who identify with teams experience an increase in positive emotions following a win, and an increase in negative emotions following a loss (Wann, Dolan, MeGeorge, & Allison, 1994). Chang found that spectators expressed positive emotions when their team scored; conversely, they expressed negative emotions when the opposite team scored (Chang, 2019). For highly identified spectators, even a brief piece of information could trigger strong emotional arousal (Wann & Branscombe, 1992).
当观众认同场上的运动队时，他们的情绪体验会增强（Sutton, McDonald, Milne, & Cimperman, 1997）。据报道，认同球队的观众在获胜后会增加积极情绪，而在失利后会增加消极情绪（Wann、Dolan、MeGeorge 和 Allison，1994 年）。Chang 发现，当自己的球队得分时，观众会表现出积极情绪；相反，当对方球队得分时，观众会表现出消极情绪（Chang，2019）。对于高度认同的观众来说，即使是一个简短的信息也会引发强烈的情绪唤醒（Wann & Branscombe, 1992）。

When spectators experience emotion, they do not experience it in a vacuum. Stieler and Germelmann (2016) noted that spectators feel bonds with fellow spectators and experience emotions together. Katz, Heere, and Reifurth (2018) found that spectators would make vocal responses such as screen in correlation with surprising plays. Negative emotions could give rise to spectator violence (Case & Boucher, 1981; Dolan & Connolly, 2014). Additionally, marketing research showed that spectators’ emotional experiences have a strong influence over their purchase intentions and behaviors (Wang & Kaplanidou, 2013).
当观众体验情感时，他们并不是在真空中体验情感。Stieler 和 Germelmann（2016）指出，观众会与其他观众产生联系，共同体验情感。Katz、Heere和Reifurth（2018）发现，观众会在令人惊讶的戏剧中做出屏幕等相关的声音反应。负面情绪可能会引发观众暴力（Case & Boucher，1981；Dolan & Connolly，2014）。此外，市场营销研究表明，观众的情绪体验对其购买意向和行为有很大影响（Wang & Kaplanidou，2013）。

Compared to the richness of literature on spectators’ emotions during traditional sports events, limited research has been done in the context of eSports. Mostly, eSports researchers have identified emotions as an important dimension of eSports spectatorship. Taylor (2012) stressed the important affective and embodied dimensions of spectatorship, where spectators can become “activated” in their emotions and bodies. Cheung and Huang (2011) reported on how StarCraft spectators shared joy and excitement while watching competitive events. Cumming (2018) conducted an interview study with 19 attendees of two major Australian eSports events to report that their participants would seek emotionally arousing matches to spectate. Rodrigues, Filgueiras, and Valente (2021) proposed to use biosensors to capture spectators’ reactions, including emotional responses. Affirming the importance of emotion in spectatorship, Woodcock and Johnson’s interview study (Woodcock & Johnson, 2019) detailed aspects of affective labor that streamers performed in front of their spectators.
与有关传统体育赛事中观众情绪的丰富文献相比，有关电子竞技的研究十分有限。电子竞技研究者大多将情感视为电子竞技观众身份的一个重要维度。Taylor（2012）强调了观众身份的重要情感和体现维度，即观众的情感和身体会被 "激活"。Cheung和Huang（2011）报告了《星际争霸》的观众如何在观看竞技赛事时分享喜悦和兴奋。Cumming（2018）对澳大利亚两大电子竞技赛事的 19 名参与者进行了访谈研究，报告称他们的参与者会寻求情绪激昂的比赛来观赛。Rodrigues、Filgueiras 和 Valente（2021 年）建议使用生物传感器捕捉观众的反应，包括情绪反应。伍德科克和约翰逊的访谈研究（Woodcock & Johnson, 2019）肯定了情感在观赛过程中的重要性，并详细介绍了流媒体在观众面前进行的情感劳动。

The spectator experience of traditional sport has also been studied considering the social platform Reddit (Zhang, Tan, & Lv, 2018; Zhang et al., 2018; Zhang, Tan, & Lv, 2019) characterize online fan communities of the NBA teams. The main objective of their study is to analyze the impact of team performance on fan behavior, both at the game level and the season level. They test their hypothesis via a regression analysis. A subsequent study on the same context is presented in Zhang et al. (2019), in which characterize language differences between intergroup and single-group members in NBA-related discussions. This work shares few similarities with our study. As an example, in both studies flairs are employed as a distinctive feature to identifying team affiliation and support of users. However, the goals of the two approaches and the methodologies to achieve them are very different.
考虑到社交平台 Reddit（Zhang, Tan, & Lv, 2018; Zhang et al.他们研究的主要目的是分析球队表现对球迷行为的影响，包括比赛层面和赛季层面。他们通过回归分析检验了自己的假设。随后，Zhang 等人（2019）针对同一语境进行了研究，分析了在与 NBA 有关的讨论中，群体间成员和单一群体成员之间的语言差异。这项研究与我们的研究有一些相似之处。例如，在这两项研究中，炫耀都被用作识别用户的球队归属和支持的显著特征。然而，这两种方法的目标和实现目标的方法却截然不同。

In sum, traditional sports research has pointed to the importance of emotions in spectating competitive events. Much could be done to understand eSports spectators’ emotional experiences, such as their primary types of emotional responses as well as their team identifications.
总之，传统体育研究已经指出了情绪在观看竞技赛事中的重要性。要了解电子竞技观众的情感体验，比如他们的主要情感反应类型以及他们对团队的认同，还有很多工作要做。

Final remarks. We believe it is worth pointing out that some studies approach the eSports context from a more computer scienceoriented perspective. As an example, Mora-Cantallops and Sicilia (Mora-Cantallops & Sicilia, 2019) studied team performance in professional LoL through network-based analysis and discovered that team efficiency is positively affected by the intensity of interaction, similar to traditional sports. Marchenko and Suschevskiy (Marchenko & Suschevskiy, 2018) focused on analyzing the eSports transfer market, while (Khromov et al., 2019) conducted a comparative study of player and eSports athlete performance in a team-based first-person shooter. Nevertheless, to the best of our knowledge, our study is the first to propose a mixed-methods investigation, including network-based-analysis and thematic analysis, of the emotional experiences in eSports spectatorship. Furthermore, to provide the reader with an overview, in Table 1 we report a summary of the most recent works on eSports along with some comparison properties.
结束语我们认为值得指出的是，一些研究从更注重计算机科学的角度来研究电子竞技。例如，Mora-Cantallops 和 Sicilia（Mora-Cantallops & Sicilia, 2019）通过基于网络的分析研究了职业 LoL 中的团队表现，发现团队效率受到交互强度的积极影响，这与传统体育运动类似。Marchenko和Suschevskiy（Marchenko & Suschevskiy，2018）重点分析了电子竞技转会市场，而（Khromov等人，2019）则对基于团队的第一人称射击游戏中玩家和电竞选手的表现进行了比较研究。尽管如此，据我们所知，我们的研究是第一项对电子竞技观众的情感体验进行混合方法调查的研究，包括基于网络的分析和主题分析。此外，为了向读者提供一个概览，我们在表 1 中报告了有关电子竞技的最新研究摘要以及一些比较特性。

Table 1 表 1
A summary of recent eSports-related studies and comparison properties related to our study.
近期与电子竞技相关的研究摘要以及与我们的研究相关的比较属性。

Fig. 2. Workflow of the proposed investigation.
图 2.拟议调查的工作流程。

In this section we describe our methodological approach. We start by describing the data collection process and characterizing our dataset. After this, we illustrate the computational methods and qualitative analyses used to carry out the investigation, providing the reader with all the necessary background information related to them. A graphical depiction of our mixed-methods methodology is shown in Fig. 2. The overall structure of our proposed investigation includes three parts, namely: (i) dataset collection, (ii) computational analysis, and (iii) qualitative analysis. The outcomes of such analyses are presented as our findings in Section 6 . Note that the comprehensive formalization and technical details pertaining to the computational analysis are provided in Appendix A for interested readers seeking further in-depth information. Additionally, we made all the data and the source code used in this study available in an online repository ${}^7$${}^7$^(7){ }^{7}.
本节将介绍我们的方法论。我们首先介绍数据收集过程和数据集的特点。之后，我们将说明用于开展调查的计算方法和定性分析，并为读者提供与之相关的所有必要背景信息。图 2 展示了我们的混合方法。我们建议的调查的总体结构包括三个部分，即：(i) 数据集收集；(ii) 计算分析；(iii) 定性分析。这些分析的结果将作为我们的研究成果在第 6 节中介绍。请注意，有关计算分析的全面形式化和技术细节在附录 A 中提供，有兴趣的读者可进一步深入了解。此外，我们还在 ${}^7$${}^7$^(7){ }^{7} 在线资源库中提供了本研究中使用的所有数据和源代码。

This section is devoted to presenting the dataset for our investigation. The dataset is generated from Reddit utilizing the pushshift.io API (Baumgartner, Zannettou, Keegan, Squire, & Blackburn, 2020). Considering the aim of our investigation, we focus on collecting the Worlds 2020 post-match discussions posted on /r/leagueoflegends. We exclude team-related subreddits, i.e., subreddits dedicated to discussing a specific eSports team, for two primary reasons. Firstly, we aim to avoid polarized content, as it is reasonable to assume that users engaging in a team-related subreddit may exhibit a bias toward that particular team. Secondly, there is no associated subreddit for every team participating in the event. Therefore, the dataset gathered via the primary LoL-related subreddit can provide a comprehensive and varied representation of the LoL fan community’s event experience on Reddit.
本节将介绍我们调查的数据集。数据集是利用 pushshift.io API 从 Reddit 生成的（Baumgartner, Zannettou, Keegan, Squire, & Blackburn, 2020）。考虑到我们调查的目的，我们重点收集了在 /r/leagueoflegends 上发布的 2020 年世锦赛赛后讨论。出于两个主要原因，我们排除了与团队相关的子版块，即专门讨论特定电子竞技团队的子版块。首先，我们的目的是避免两极分化的内容，因为我们有理由认为，参与团队相关子版块的用户可能会表现出对特定团队的偏见。其次，每个参赛队都没有相关的子论坛。因此，通过主要的《英雄联盟》相关子论坛收集的数据集可以全面、多样地反映《英雄联盟》粉丝社区在 Reddit 上的活动体验。

We start by retrieving all posts within the timeframe spanning from October 1, 2020 to October 30, 2020; these dates correspond to the starting and ending dates of the Worlds 2020 event, respectively. Then, we filter these posts retaining only the post-match discussions. Such filtering is possible thanks to post-match discussions presenting a recognizable title ${}^8$${}^8$^(8){ }^{8}. This process yields a total of 78 post-match discussions, and upon manual inspection we confirm these posts to be related to the Worlds 2020 event. Additionally, we retrieve all available comments for each of these posts. A comment could be marked as deleted, indicating that the comment is no longer available on Reddit; in such case, we discard it. For both posts and comments, we collect various features, including textual content and the name of the author. The complete list of features along with technical details on data processing are provided in Appendix A.1. While retrieving the comments related to such discussions, we performed an ETL (Extraction, Transformation and Loading) activity to delete comments containing only URLs. The total number of collected comments is 40,587. Finally, for each author, we derive the list of their associated flairs, which will be exploited in the following analyses.
我们首先检索了 2020 年 10 月 1 日至 2020 年 10 月 30 日时间范围内的所有帖子；这些日期分别对应于 2020 年世界大赛的开始和结束日期。然后，我们对这些帖子进行过滤，只保留赛后讨论。由于赛后讨论的标题 ${}^8$${}^8$^(8){ }^{8} 具有可识别性，因此这种过滤是可行的。这一过程总共产生了 78 篇赛后讨论，经过人工检查，我们确认这些帖子与 2020 年世界大学生运动会有关。此外，我们还检索了这些帖子的所有可用评论。评论可能被标记为已删除，表明该评论在 Reddit 上已不可用；在这种情况下，我们将其丢弃。对于帖子和评论，我们都会收集各种特征，包括文本内容和作者姓名。完整的特征列表以及数据处理的技术细节见附录 A.1。在检索与此类讨论相关的评论时，我们执行了一项 ETL（提取、转换和加载）活动，以删除仅包含 URL 的评论。收集到的评论总数为 40,587 条。最后，我们得出了每位作者的相关耀斑列表，并将在接下来的分析中加以利用。

In this section, we first describe the dataset via an exploratory data analysis. Then, we annotate our data to quantify the sentiments within it.
在本节中，我们首先通过探索性数据分析来描述数据集。然后，我们对数据进行注释，以量化其中的情感。

Table 2 表 2
$\underset{―}{\text{Descriptive summary of the submissions and comments collected for our study.}}$$\underset{\_}{\text{ Descriptive summary of the submissions and comments collected for our study. }}$" Descriptive summary of the submissions and comments collected for our study. "_\underline{\text { Descriptive summary of the submissions and comments collected for our study. }}

Fig. 3. Distribution of comments against their length (log-log scale).
图 3.评论长度的分布（对数标度）。

Fig. 4. Distribution of scores against comments (log-log scale).
图 4.对照评论的得分分布情况（对数--线性比例）。

We start by reporting in Table 2 a descriptive summary of the collected posts and comments. During the Worlds 2020 event, interactions within the post-match discussions involved over 3,000 distinct authors, with an average of more than 1,700 comments per post-match discussion. To characterize the distribution of posts and comments, in Figs. 3, 4 and 5 we report the distribution of comments against the length of their textual content, the distribution of comments against their received score, and the distribution of comments against authors, respectively. Interestingly, upon analyzing Fig. 3, we observe a consistent number of comments that are nearly a hundred characters in length, with only a few comments exceeding a thousand characters. This observation aligns with expectations, as comments typically serve as fast-paced messages. Fig. 4 shows the number of comments per score, highlighted by negative and positive score. We observe that more than 10,000 comments have a score of 1 , whereas only a very small number of comments have a score exceeding 1,000 . This suggests how most comments tend to not receive upvotes nor downvotes: in fact, in Reddit the default score for a comment is 1 . Fig. 5 shows the distribution of number of authors against the number of comments. We observe that the majority of the authors have a very small number of comments, while a few authors have more than 100 comments. This data suggests that some authors make significant contributions to discussions by participating in them multiple times. This insight will be also confirmed in the following. Appendix A. 2 reports more details on the collected data, such as the numerical characterization of the aforementioned distributions.
我们首先在表 2 中报告了收集到的帖子和评论的描述性摘要。在 2020 年世界大赛期间，赛后讨论中的互动涉及 3,000 多名不同的作者，平均每个赛后讨论有 1,700 多条评论。为了描述帖子和评论的分布情况，我们在图 3、图 4 和图 5 中分别报告了评论与文本内容长度的分布情况、评论与收到的分数的分布情况以及评论与作者的分布情况。有趣的是，在对图 3 进行分析时，我们观察到长度接近 100 个字符的评论数量是一致的，只有少数评论超过了 1000 个字符。这一观察结果与预期相符，因为评论通常是快节奏的信息。图 4 显示了每项评分的评论数量，并按负面和正面评分进行了突出显示。我们观察到，超过 10,000 条评论的分数为 1，而只有极少数评论的分数超过 1,000。这说明大多数评论往往不会获得向上或向下投票：事实上，在 Reddit，评论的默认分数是 1。图 5 显示了作者数量与评论数量的分布。我们发现，大多数作者的评论数量很少，而少数作者的评论数量超过 100 条。这一数据表明，一些作者通过多次参与讨论做出了重大贡献。这一观点也将在下文中得到证实。附录 A. 2 报告了所收集数据的更多细节，如上述分布的数值特征。

To better highlight the spectatorship experience, and to better depict the Worlds 2020 event, we provide the reader with a thematic compass underlining the most important games played in it. The importance of each game can be reflected by authors
为了更好地突出观赛体验，更好地描绘 2020 年世乒赛的盛况，我们为读者提供了一个主题指南针，突出其中最重要的比赛。每场比赛的重要性可通过以下作者反映出来

Fig. 5. Distribution of authors against comments (log-log scale).
图 5.作者与评论意见的分布情况（对数-对数比例）。

Fig. 6. Top 10 most commented post-match discussions.
图 6.评论最多的十大赛后讨论。

Table 3 表 3
Examples of comments in the collected dataset along with their associated sentiment value.
所收集数据集中的评论示例及其相关情感值。

in their interest in discussing about it. Fig. 6 shows the top 10 post-match discussions w.r.t. the received number of comments. The $y$$y$yy-axis shows the title of the post-match discussion ${}^9$${}^9$^(9){ }^{9} and the $x$$x$xx-axis reports the number of received comments. It is interesting to observe how the post with the most comments is the one related to a semi-final stage game, while the one related to the final stage game only received half of the comments. Also, we note how some teams, e.g., G2 Esports and Fnatic, appear repeatedly in these top post-match discussions. This is the first indicator of how these teams serve as reference points in this kind of spectatorship experience. We will observe that this intuition holds w.r.t. the findings presented in Section 6.
的讨论兴趣。图 6 显示了收到评论数最多的 10 个赛后讨论。 $y$$y$yy 轴表示赛后讨论的标题 ${}^9$${}^9$^(9){ }^{9} ， $x$$x$xx 轴表示收到的评论数量。有趣的是，评论最多的帖子与半决赛阶段的比赛有关，而与决赛阶段的比赛有关的帖子只收到了一半的评论。此外，我们还注意到一些队伍，如 G2 Esports 和 Fnatic，是如何反复出现在这些热门赛后讨论中的。这是这些队伍如何在这种观赛体验中充当参照点的第一个指标。我们将在第 6 部分的研究结果中观察到这一直觉是否成立。

Finally, we annotate each comment to quantify sentiments expressed by it. To do so, we associate each comment with a sentiment value extracted from the textual content of the comment itself. We resort to the compound score provided by a lexicon and rule-based model, specifically attuned to sentiments expressed in social media, called VADER (Hutto & Gilbert, 2014). It lies in the interval $[-1,1]$$[-1,1]$[-1,1][-1,1], with -1 (resp., 1) being the most extreme negative (resp., positive) sentiment, and it is recognized as one of the most useful metrics when a single unidimensional sentiment measure is needed for a given text (Horne, Adali, & Sikdar, 2017; Keneshloo, Wang,
最后，我们对每条评论进行注释，以量化评论所表达的情感。为此，我们将每条评论与从评论文本内容中提取的情感值关联起来。我们采用的是一个基于词典和规则的模型提供的复合分数，该模型专门针对社交媒体中表达的情感，名为 VADER（Hutto & Gilbert，2014 年）。它位于 $[-1,1]$$[-1,1]$[-1,1][-1,1] 区间内，-1（或 1）表示最极端的负面（或正面）情绪，当需要对给定文本进行单一的单维情绪测量时，它被认为是最有用的指标之一（Horne, Adali, & Sikdar, 2017; Keneshloo, Wang、

Table 4 表 4

Sam Han, & Ramakrishnan, 2016). In our analysis, we refer to it as sentiment value. Some examples of comments, along with their sentiment values, are reported in Table 3.
Sam Han, & Ramakrishnan, 2016）。在我们的分析中，我们将其称为情感值。表 3 列出了一些评论及其情感值的例子。

In this section, we first illustrate the identification of supported teams through the analysis of flairs. Then, we propose our network-based representation of users’ interactions.
在本节中，我们首先通过对标志的分析来说明如何识别受支持的团队。然后，我们提出了基于网络的用户互动表示法。

A core task in the investigation of spectators’ interactions during eSports events is the identification and characterization of authors supporting one or more teams. Indeed, in our setting, it is reasonable to assume that spectators participate to varying degrees in the discussions, rather than solely spectating the event. Therefore, being able to understand whether an author supports one team or another is crucial in the characterization of supporters. Such knowledge is useful for observing and understanding spectators’ dynamics toward a particular team and for studying it in detail. Moreover, it also allows studying interactions between authors within different levels of abstraction.
调查电子竞技赛事期间观众互动的一项核心任务是识别和描述支持一支或多支队伍的作者。事实上，在我们的环境中，可以合理地假设观众在不同程度上参与了讨论，而不仅仅是观看赛事。因此，了解作者是否支持一个或另一个团队对于确定支持者的特征至关重要。这些知识有助于观察和了解观众对特定球队的动态，并对其进行详细研究。此外，它还可以在不同的抽象层面上研究作者之间的互动。

To characterize spectators’ interactions, we focus on understanding whether an author supports a team or at least refers to one. We could assume that an author refers to a team if she comments on a post-match discussion involving that particular team. However, for an author to participate in such a discussion does not imply that the author supports one of the teams the discussion is about. Therefore, relying on this assumption could be reductive and misleading. In light of this consideration, to identify which teams an author supports we resort to analyzing the flairs of each author. We recall that a flair is a subreddit-defined label, and each author can select one or more flairs to appear to the side of their public name whenever they post a comment, as shown in the graphical representation depicted in Fig. 1. The LoL subreddit provides several flairs for authors regarding different aspects of the game as well as for each team competing in the eSports scene. Authors can display such flairs to show their support for the respective teams. Thanks to this knowledge, we can then utilize flairs to indicate that authors support a team, provided they have a flair associated with that specific team. It is important to point out that there could be cases where carrying a flair does not necessarily imply support for the related team. For instance, authors could carry a flair for a team they dislike. However, in our study, we consider flairs as a sign of support due to the meaning these express in the subreddit. Before delving further into our analysis, it is important to list the teams that participated in the Worlds 2020 event. This list is presented in Table 4, where we indicate the region each team belongs to and the flair used by authors in the subreddit to refer to it. For example, Fnatic is a European team, and the flair representing it in the subreddit is eufnc. To identify teams within post-match discussions, we focus on the flairs carried by authors. If an author carries a team’s flair, we assume that the author is supporting or referring to that team.
为了描述观众互动的特点，我们重点了解作者是否支持某支球队或至少提及某支球队。我们可以假设，如果作者在赛后讨论中发表了涉及某支球队的评论，那么她就提到了这支球队。然而，作者参与这样的讨论并不意味着作者支持讨论所涉及的其中一支球队。因此，依赖这一假设可能会产生误导。有鉴于此，为了确定作者支持哪支球队，我们采用了分析每位作者亮点的方法。如图 1 所示，每个作者都可以选择一个或多个标志，在发表评论时显示在其公开姓名的一侧。LoL subreddit 为作者提供了多个标志，涉及游戏的不同方面以及电子竞技场景中的每支参赛队伍。作者可以通过展示这些标志来表达他们对相应团队的支持。有了这些知识，我们就可以利用标志来表示作者对某个团队的支持，前提是他们拥有与该团队相关的标志。需要指出的是，在某些情况下，拥有标志并不一定意味着支持相关球队。例如，作者可能会为自己不喜欢的球队带有一种标志。然而，在我们的研究中，我们认为标语是一种支持的标志，因为这些标语在 subreddit 中表达了意义。在深入分析之前，有必要先列出参加 2020 年世界大学生运动会的队伍名单。表 4 列出了这份名单，我们在其中标明了每支队伍所属的地区以及作者在 subreddit 中用来指代该队伍的标志。 例如，Fnatic 是一支欧洲队伍，其在子论坛中的代表标志是 eufnc。为了在赛后讨论中识别球队，我们重点关注作者所携带的标志。如果某位作者带有某支球队的标志，我们就认为该作者在支持或提及该球队。

Furthermore, to characterize the behavior that users follow in discussing the Worlds 2020 event, we use a network-based representation and analysis. Our aim is two-fold: (i) to represent and characterize interactions between authors in the post-match discussions, and (ii) to characterize interactions between authors grouped by the teams they support. To achieve this, we borrow techniques from the field of (social) network analysis. Specifically, we study the collected post-match discussions by representing them as networks in which nodes are entities such as authors or teams, and edges between nodes are interactions between these entities. By utilizing such representations, we can analyze these networks to gain insights and uncover latent knowledge within the interactions.
此外，为了描述用户在讨论 2020 年世界大学生运动会时的行为特征，我们使用了基于网络的表示和分析方法。我们的目标有两个方面：(i) 表示和描述作者之间在赛后讨论中的互动，(ii) 描述按所支持的团队分组的作者之间的互动。为此，我们借鉴了（社会）网络分析领域的技术。具体来说，我们将收集到的赛后讨论表示为网络，其中节点是作者或团队等实体，节点之间的边是这些实体之间的互动。通过利用这种表示方法，我们可以分析这些网络以获得洞察力，并挖掘出互动中的潜在知识。

Our network-based analysis involves defining and studying two networks constructed from the collected discussions, namely, (i) Author Interaction network, and (ii) Team Interaction network. We denote the former as ${\mathcal{N}}_{AI}$${\mathcal{N}}_{AI}$N_(AI)\mathcal{N}_{A I}, representing interactions between authors through comments. Each node is an author, and each edge is an interaction between two authors. Each node is associated
我们的网络分析包括定义和研究从收集的讨论中构建的两个网络，即 (i) 作者互动网络和 (ii) 团队互动网络。我们用 ${\mathcal{N}}_{AI}$${\mathcal{N}}_{AI}$N_(AI)\mathcal{N}_{A I} 表示前者，代表作者之间通过评论进行的互动。每个节点代表一位作者，每条边代表两位作者之间的互动。每个节点都与
with two attributes, namely, (i) the average score received by the author it represents, and (ii) the average sentiment value of the comments published by that author. Instead, we denote the latter as ${\mathcal{N}}_{TI}$${\mathcal{N}}_{TI}$N_(TI)\mathcal{N}_{T I}. This network highlights authors’ interactions grouped by the team(s) they support. Each node represents a team, and an edge between two teams indicates that there are at least two authors, supporting either one of the two teams, that interacted through a comment. Three different values are associated with each team: the first one is the number of comments made by authors supporting that team; the second one is the average score of the comments made by these authors, while the third one is the average sentiment value of the comments made by the same authors. The findings drawn from the analysis of these networks are presented in Section 6 while their complete formalization is reported in Appendix A.2.
有两个属性，即 (i) 它所代表的作者所获得的平均分数，以及 (ii) 该作者所发表评论的平均情感值。我们将后者记为 ${\mathcal{N}}_{TI}$${\mathcal{N}}_{TI}$N_(TI)\mathcal{N}_{T I} 。该网络按作者支持的团队分组，突出了作者之间的互动。每个节点代表一个团队，两个团队之间的边表示至少有两个支持这两个团队中任何一个团队的作者通过评论进行了互动。每个团队都有三个不同的关联值：第一个是支持该团队的作者所发表评论的数量；第二个是这些作者所发表评论的平均得分；第三个是同一作者所发表评论的平均情感值。对这些网络的分析结果将在第 6 节中介绍，其完整的形式化过程将在附录 A.2 中报告。

A peculiar aspect of observing eSports spectator behavior pertains to the potential victories or defeats of certain teams. A question that arises is, “Could the victory (or defeat) of one of the most recognized teams influence the future interactions of spectators supporting that team?”. The intuition here is that a team’s victory (resp., defeat) could increase (resp., decrease) the degree of involvement of spectators in future discussions of the event. To investigate this question, we focus on observing spectators supporting a team before and after a particular game, which we refer to as a breakpoint. We define a breakpoint as an important defeat that occurs at a certain stage of the event and leads to the elimination of the defeated team.
观察电子竞技观众行为的一个奇特方面与某些战队的潜在胜利或失败有关。由此产生的一个问题是："最知名战队的胜利（或失败）是否会影响支持该战队的观众未来的互动？这里的直觉是，一支球队的胜利（或失败）可能会增加（或减少）观众参与未来赛事讨论的程度。为了研究这个问题，我们将重点放在观察观众在某场比赛前后对某支球队的支持上，我们将其称为 "爆发点"。我们将断点定义为在赛事的某一阶段发生的重要失败，并导致失败球队被淘汰。

Given a team $t$$t$tt that suffered an elimination, we are interested in analyzing the comments, made by authors supporting the team $t$$t$tt, posted before and after the elimination. To do so, we select the post-match discussion related to the elimination, and we denote with $B_t^{<}$$B_t^{<}$B_(t)^( < )B_{t}^{<}(resp., $B_t^{\ge }$$B_t^{\ge }$B_(t)^( >= )B_{t}^{\geq}) the comments made before (resp., after) the timestamp of the post-match discussion. For a formal definition of breakpoints, we refer the reader to Appendix A.2.
给定一支遭遇淘汰的球队 $t$$t$tt ，我们有兴趣分析支持球队 $t$$t$tt 的作者在淘汰赛前后发表的评论。为此，我们选择了与淘汰赛相关的赛后讨论，并用 $B_t^{<}$$B_t^{<}$B_(t)^( < )B_{t}^{<} （或 $B_t^{\ge }$$B_t^{\ge }$B_(t)^( >= )B_{t}^{\geq} ）表示在赛后讨论的时间戳之前（或之后）发表的评论。关于断点的正式定义，我们请读者参阅附录 A.2。

We characterize a breakpoint based on these two sets and three different points of view, namely, (i) quantitative observation, (ii) word usage exploration, and (iii) network-based analysis. In the first point of view, we collect different quantitative properties highlighting how spectators behave before and after the breakpoint. For both sets, we focus on the number of comments, the number of distinct authors, the average number of comments per distinct author, the average comment score, and the average comment sentiment value. These allow us to identify possible radical changes in the volume of produced content between the two parts, i.e., before and after the defeat of a team. The rationale behind this quantitative observation is that, following their supported team’s defeat, authors are less likely to actively contribute to future discussions.
我们基于这两组数据和三个不同的视角来描述断点的特征，即 (i) 定量观察，(ii) 词语用法探索，以及 (iii) 基于网络的分析。在第一个视角中，我们收集了不同的定量属性，突出了观众在断点前后的行为方式。在这两组数据中，我们主要关注评论数量、不同作者数量、每个不同作者的平均评论数量、平均评论分数和平均评论情感值。通过这些数据，我们可以发现两部分之间，即一个团队战败前后，所生产内容的数量可能发生的根本性变化。这种定量观察背后的理论依据是，在其支持的团队战败后，作者不太可能积极地参与未来的讨论。

The second point of view allows us to explore word usage of spectators, particularly in the after part of the breakpoint, by investigating word categories; to do so, we employ the well-known LIWC word categories, and we quantify the proportion of words, used in the comments within the two parts, falling into such categories. LIWC word categories were created to capture people’s social and psychological states (Pennebaker, Boyd, Jordan, & Blackburn, 2015; Tausczik & Pennebaker, 2010). These are used extensively in the literature, and also in the context of Reddit (Horne et al., 2017; Trujillo et al., 2021; Wang, Zhang, Han, & Lv, 2020). In our setting, we focus on two categories, namely (i) cogproc, and (ii) drives. We select these categories based on their significance in relation to the context. In fact, the former category collects words that suggest cognitive processes such as insight, certainty, differentiation, etc. The latter collects words expressing motives and drivers such as power, achievements, risk, etc.
第二种观点允许我们通过调查词类来探索观众的用词情况，尤其是在断点之后的部分；为此，我们采用了著名的 LIWC 词类，并量化了两部分评论中属于此类词类的用词比例。LIWC词类的创建是为了捕捉人们的社会和心理状态（Pennebaker, Boyd, Jordan, & Blackburn, 2015; Tausczik & Pennebaker, 2010）。这些词类在文献和 Reddit 中得到了广泛应用（Horne 等人，2017 年；Trujillo 等人，2021 年；Wang、Zhang、Han 和 Lv，2020 年）。在我们的环境中，我们重点关注两个类别，即 (i) cogproc 和 (ii) drives。我们选择这些类别的依据是它们与上下文的关系。事实上，前一类收集的是暗示认知过程的词语，如洞察力、确定性、区分等。后者收集表达动机和驱动力的词语，如权力、成就、风险等。

Finally, in the last point of view, we study the interactions of spectators whose comments are in $B_t^{<}$$B_t^{<}$B_(t)^( < )B_{t}^{<}and $B_t^{\ge }$$B_t^{\ge }$B_(t)^( >= )B_{t}^{\geq}by modeling them through a network-based structure, similar to what we presented in Section 5.3. In particular, we exploit two networks representing the spectators and their interactions before and after the breakpoint, respectively. The former network represents the before part of the breakpoint, consisting of the authors who posted a comment before the timestamp of the breakpoint with each edge being the interaction between two authors. The latter network is analogous to the former and represents the interactions between spectators in the after part of the breakpoint. Note that, with this representation, we consider all the users regardless of their flairs.
最后，我们从最后一个角度研究了评论位于 $B_t^{<}$$B_t^{<}$B_(t)^( < )B_{t}^{<} 和 $B_t^{\ge }$$B_t^{\ge }$B_(t)^( >= )B_{t}^{\geq} 中的观众之间的互动，通过基于网络的结构对其进行建模，这与我们在第 5.3 节中介绍的情况类似。具体来说，我们利用两个网络分别代表断点前后的旁观者及其交互。前一个网络代表断点之前的部分，由在断点时间戳之前发表评论的作者组成，每条边代表两个作者之间的互动。后一个网络与前一个网络类似，代表断点后部分观众之间的互动。请注意，在这种表示方法中，我们考虑的是所有用户，而不考虑他们的炫耀。

In parallel with the computational analysis, we perform a qualitative analysis on the collected data. Qualitative analysis offers unique methodological advantages in “discovering the who, what, and where of events or experiences and on gaining insights from informants regarding a poorly understood phenomenon” (Kim, Sefcik, & Bradway, 2017). As such, our goal of performing a qualitative analysis is to explore LoL spectators’ ‘emic’ perspectives and generate insights into their subjective experiences. While many specific qualitative analysis approaches are available, we determine that thematic analysis (Braun & Clarke, 2012) suits the goal of our study the best, because of this method’s flexibility with sample size, number of coders, and theoretical frameworks.
在进行计算分析的同时，我们还对收集到的数据进行了定性分析。定性分析在 "发现事件或经历的人物、内容和地点，以及从信息提供者那里获得对不甚明了的现象的见解"（Kim, Sefcik, & Bradway, 2017）方面具有独特的方法论优势。因此，我们进行定性分析的目的是探索 LoL 观众的 "情感 "视角，并深入了解他们的主观体验。虽然有许多具体的定性分析方法，但我们认为主题分析法（Braun & Clarke, 2012）最适合我们的研究目标，因为这种方法在样本量、编码者人数和理论框架方面具有灵活性。

Specifically, the second author performs thematic analysis (Braun & Clarke, 2012) to understand LoL spectators’ emotional experiences, with a focus on emotive factors in their spectating experiences. Following the steps of thematic analysis (Braun & Clarke, 2012), the coder/second author starts the coding process by familiarizing himself with the data. The purpose of this step is to develop a general understanding of what is in the data and to what extent the data aligns with our initial research goals. In this step, the coder reads through all the posts and comments, with a focus on the comments, because posts only describe basic competition information in a standard format. After this step, the coder determines that the data is suitable for extracting emotive factors in LoL spectators’ experiences.
具体来说，第二作者进行主题分析（Braun & Clarke, 2012），以了解 LoL 观众的情感体验，重点关注他们观赛体验中的情感因素。按照主题分析的步骤（Braun & Clarke, 2012），编码员/第二作者通过熟悉数据开始编码过程。这一步骤的目的是对数据中的内容以及数据在多大程度上符合我们最初的研究目标有一个总体的了解。在这一步中，编码员会通读所有帖子和评论，重点放在评论上，因为帖子只是以标准格式描述了基本的竞争信息。在这一步之后，编码员将确定数据是否适合用于提取 LoL 观众体验中的情感因素。

Then, the coder starts to perform initial coding, meaning that the coder describes his initial impression of a piece of data, usually using a sentence or a phrase. The piece of data is the unit of analysis, which can be a phrase, a sentence, or a paragraph, depending on the density of ideas contained in a piece of data. An example of initial coding is that, for a comment of "Congrats Damwon [DWG]!
然后，编码员开始进行初始编码，这意味着编码员通常用一个句子或一个短语来描述他对某一数据的初步印象。数据片段是分析的单位，可以是一个短语、一个句子或一个段落，这取决于数据片段所含观点的密度。初始编码的一个例子是，对于 "恭喜 Damwon [DWG]！

Fig. 7. Number of authors supporting a team.
图 7.支持一个团队的作者人数。

Really happy that LCK finally reclaimed the throne", the coder writes his first impression as “positive emotion because their favorite team won”. In this way, the initial code describes both an emotive factor “their favorite team won” and the spectator’s emotional reaction. While the sample size consisting of 78 posts and 40,587 comments is suitable for a computational analysis, it is neither feasible nor necessary for a qualitative analysis to process all the pieces of data. Rather, we adopt a sampling strategy to select and analyze data, until we reach the point of “saturation” (Morse, 1995), meaning that no new idea is found in the process. Importantly, the strategy of saturation does not apply to the full epistemological spectrum of conducting thematic analysis, and is more suitable in a positivist approach that seeks to discover existing themes than a reflexive one (Braun & Clarke, 2021). Our study is designed as leaning towards the positivist end and thus compatible with the idea of saturation. Since the coding process may not process the complete dataset, it is important to first sample representative data. To achieve this purpose, the coder randomizes all the posts based on their key metadata such as region, date, and number of comments. After this randomization, the coder starts the initial coding process over the resulting data. In total, the initial coding processes 24 posts with their associated comments, and results in 45 unique initial codes.
真的很高兴 LCK 终于夺回了王座"，编码者将其第一印象写成了 "因为自己喜欢的队伍获胜而产生的积极情绪"。这样，初始代码既描述了 "他们喜欢的球队赢了 "这一情感因素，也描述了观众的情感反应。虽然由 78 篇帖子和 40,587 条评论组成的样本量适合于计算分析，但对于定性分析来说，处理所有数据既不可行，也没有必要。相反，我们采用抽样策略来选择和分析数据，直到达到 "饱和 "点（Morse，1995 年），即在此过程中没有发现新的想法。重要的是，"饱和 "策略并不适用于进行主题分析的全部认识论范畴，它更适用于寻求发现现有主题的实证主义方法，而不是反思性方法（Braun & Clarke, 2021）。我们的研究在设计上倾向于实证主义，因此符合饱和的理念。由于编码过程可能无法处理完整的数据集，因此首先抽取具有代表性的数据样本非常重要。为此，编码员根据帖子的关键元数据（如地区、日期和评论数量）对所有帖子进行随机化处理。随机化后，编码员开始对所得数据进行初始编码。初始编码总共处理了 24 个帖子及其相关评论，并产生了 45 个唯一的初始编码。

Upon the initial coding process that leads to a list of initial codes, the next step is to develop the initial codes into major themes that can answer our research question. To do so, the coder refines initial codes in an iterative way, a process through which codes with similar meanings are combined into larger groups. It is also possible that a code already put in one group is later moved to another. Still using the earlier example, the initial code indicates “their favorite team won” as an emotive factor, which was similar to a few other initial codes, such as “favorite team lost” and “felt sad for fans from a region because of a match outcome”. All these initial codes point to a common emotive factor, which is the outcome of the competition. Thus, all these initial codes are gradually linked and categorized under an overarching theme, which is “competition outcome”. During this step, the coder needs to go back and forth between the initial codes and their related data in order to ensure that the process of combination still preserves the accurate meaning of data.
初步编码过程产生了初始代码列表，下一步是将初始代码发展成能够回答我们的研究问题的主要主题。为此，编码员会以迭代的方式完善初始编码，在此过程中，具有相似含义的编码会被合并到更大的组中。也有可能已经归入一组的代码后来又被移到另一组。仍以前面的例子为例，初始代码表明 "他们最喜欢的球队赢了 "是一个情感因素，这与 其他几个初始代码相似，如 "最喜欢的球队输了 "和 "因比赛结果而为某个地区的球迷感到 悲伤"。所有这些初始代码都指向一个共同的情感因素，即比赛结果。因此，所有这些初始代码都会逐渐联系起来，并归类为一个总主题，即 "比赛结果"。在这一步骤中，编码员需要在初始编码及其相关数据之间来回切换，以确保组合过程仍 然能保留数据的准确含义。

This iterative process is completed when we reach a satisfactory thematic map, meeting the criteria of thematic analysis including external heterogeneity and internal homogeneity (Braun & Clarke, 2012). External heterogeneity means that major themes are distinct from each other, and internal homogeneity means that ideas under each theme are close to each other. Through this step of refinement, we identify six emotive factors, namely competition outcome, gameplay quality, team growth, fan groups’ interactions, casting, and local context. We will use the findings section to detail each of these emotive factors.
当我们绘制出令人满意的主题图，符合主题分析的标准（包括外部异质性和内部同质性）时，这一迭代过程就完成了（Braun & Clarke, 2012）。外部异质性是指主要主题彼此不同，内部同质性是指每个主题下的观点彼此接近。通过这一步的提炼，我们确定了六个情感因素，即比赛结果、游戏质量、团队成长、粉丝群体互动、演员阵容和当地环境。我们将在研究结果部分逐一详述这些情感因素。

In this section, we present the results of our investigation, which consists of two main analyses, namely computational and qualitative. In the former, we analyze interactions between authors via the computational methods presented in Section 5. Instead, in the latter we present six major emotive factors that triggered spectators’ emotions during the Worlds 2020 event. We demonstrate that the support exhibited by spectators for particular teams played a pivotal role in shaping their emotional experiences, both on a collective and individual level.
本节将介绍我们的调查结果，其中包括两项主要分析，即计算分析和定性分析。在前者中，我们通过第 5 节中介绍的计算方法分析了作者之间的互动。而在定性分析中，我们介绍了在 2020 年世乒赛期间引发观众情绪的六大情感因素。我们证明，观众对特定团队的支持在集体和个人层面上对其情感体验的形成起到了关键作用。

The computational methods presented in Section 5 helped us exploring the data in order to extrapolate several interconnected peculiarities. These peculiarities express ways of observing experiences in eSports spectatorship while also enabling the creation of a quantitative overview of this context.
第 5 节中介绍的计算方法有助于我们探索数据，从而推断出几个相互关联的特殊性。这些特殊性表达了观察电子竞技观众经验的方式，同时也有助于对这一背景进行量化概述。

Table 5 表 5

The identification of supported teams in Section 5.3 provides us with the opportunity to understand which teams were supported by authors. The results are depicted in Fig. 7, where we report the number of authors supporting each team. From the analysis of this figure, we first observe that the total number of authors supporting at least one team is 2,373 , which is smaller than the number of authors who have posted or commented on the Worlds 2020 event, that is 3,171 . This indicates that only the $75\mathrm{\%}$$75\mathrm{\%}$75%75 \% of authors carry a team-related flair. Then, we observe that two teams, Fnatic and G2 Esports, present the highest number of authors supporting them, that is 671 and 515 authors, respectively. Among other teams, the one with most authors supporting it is TSM, with 308 authors. This is somewhat expected, considering that Fnatic and G2 Esports are two of the most influential teams in Europe and also in other eSports scenes (Bloomberg, 2021; The Esports Observer, 2019), while TSM is one of the most recognized eSports organizations in the North America (Dexerto.com, 2021). Furthermore, two teams, PSG.Talon Esports and Machi Esports, are supported by 4 authors and no authors, respectively, in the discussions under consideration. Lastly, we also observe that the average number of authors supporting a team is 139 .
第 5.3 节中对支持团队的识别为我们提供了了解哪些团队得到了作者支持的机会。结果如图 7 所示，我们在图中报告了支持每个团队的作者人数。通过对该图的分析，我们首先观察到，至少支持一个团队的作者总数为 2,373 人，少于在世界大赛 2020 上发表文章或评论的作者人数（3,171 人）。这表明，只有 $75\mathrm{\%}$$75\mathrm{\%}$75%75 \% 的作者带有与团队相关的倾向。然后，我们观察到，Fnatic 和 G2 Esports 这两支队伍获得了最多作者的支持，分别为 671 位和 515 位。在其他队伍中，获得最多作者支持的是 TSM，有 308 位作者。考虑到 Fnatic 和 G2 Esports 是欧洲以及其他电子竞技领域最有影响力的两支队伍（Bloomberg，2021 年；The Esports Observer，2019 年），而 TSM 是北美最知名的电子竞技组织之一（Dexerto.com，2021 年），这种情况在一定程度上是意料之中的。此外，PSG.Talon Esports 和 Machi Esports 这两支队伍在讨论中分别有 4 位作者和无作者支持。最后，我们还注意到，支持一个团队的作者平均人数为 139 人。

It is also interesting to identify whether two teams share authors who support them. Given two different teams, we compute the ratio between the number of common authors and the total number of authors between them. We find that the average of this ratio is $0.5\mathrm{\%}$$0.5\mathrm{\%}$0.5%0.5 \%, indicating that the number of authors in common between two different teams is consistently very low. However, we observe that this ratio is relatively higher when considering teams from the same region. For instance, the two highest values for this ratio are $0.625\mathrm{\%}$$0.625\mathrm{\%}$0.625%0.625 \% and $0.369\mathrm{\%}$$0.369\mathrm{\%}$0.369%0.369 \%, corresponding to the teams JD Gaming and LGD Gaming, and Fnatic and Rogue, respectively. The former is a pair of Chinese teams, while the latter is a pair of European teams.
确定两个团队是否共享支持他们的作者也很有趣。给定两个不同的团队，我们计算它们之间的共同作者人数与作者总人数之比。我们发现，该比率的平均值为 $0.5\mathrm{\%}$$0.5\mathrm{\%}$0.5%0.5 \% ，这表明两个不同团队之间的共同作者人数一直很少。然而，我们发现，当考虑同一地区的团队时，这一比率相对较高。例如，该比率的两个最高值是 $0.625\mathrm{\%}$$0.625\mathrm{\%}$0.625%0.625 \% 和 $0.369\mathrm{\%}$$0.369\mathrm{\%}$0.369%0.369 \% ，分别对应 JD Gaming 和 LGD Gaming 以及 Fnatic 和 Rogue。前者是一对中国战队，后者是一对欧洲战队。

This bird’s-eye view of team identification allows us to make several observations about the Lol subreddit community during the time of the Worlds 2020 event. Firstly, it is clear that the most recognized teams received the highest level of support through comments and interactions. Teams such as G2 Esports and Fnatic prominently appear as the most frequently discussed, followed by teams such as TSM and Team Liquid. The two most discussed teams are from the same region, as are the following two. However, not all teams from the same region appear to share the same user base. For example, while the first two most discussed teams are from Europe, no other teams from the same region share the same spectator population. Therefore, based on the collected data, it is fair to assume that the LoL subreddit community centers around the so-called “big teams”. These observations could be leveraged in various contexts. For instance, from a digital marketing perspective, teams that are less supported could enhance their brand’s position in the subreddit by creating content or events to attract more users.
通过对团队识别情况的鸟瞰，我们可以对 2020 年世界锦标赛期间的 Lol subreddit 社区提出几点看法。首先，通过评论和互动，最受认可的战队显然获得了最高程度的支持。G2 Esports 和 Fnatic 等战队是讨论频率最高的战队，其次是 TSM 和 Team Liquid。讨论最多的两支队伍来自同一地区，后面两支也是如此。然而，并非所有来自同一地区的队伍都拥有相同的用户群。例如，讨论最多的前两支队伍都来自欧洲，但同一地区的其他队伍却没有相同的观众群体。因此，根据收集到的数据，可以认为 LoL subreddit 社区是以所谓的 "强队 "为中心的。这些观察结果可以在各种情况下加以利用。例如，从数字营销的角度来看，支持率较低的战队可以通过创建内容或活动来吸引更多用户，从而提高其品牌在 subreddit 中的地位。

In Section 5.3 we introduced a network-based representation and analysis of the interactions between authors in the Worlds 2020 post-match discussions. Our aim is to characterize authors both in general and by inspecting the teams they support. To do so, we represent authors’ interactions by two networks, namely (i) Author Interaction network, and (ii) Team Interaction network. In the following, we illustrate the findings obtained by analyzing such networks.
在第 5.3 节中，我们介绍了一种基于网络的表达方式，并分析了 2020 年世界大赛赛后讨论中作者之间的互动。我们的目的是对作者进行总体描述，并通过检查他们所支持的团队来描述作者的特点。为此，我们用两个网络来表示作者之间的互动，即 (i) 作者互动网络和 (ii) 团队互动网络。下面，我们将说明通过分析这些网络得出的结论。

Table 5 reports the values of some basic properties for the Author Interaction and Team Interaction networks. From the analysis of this table, we can discern several aspects. First off, the Author Interaction network presents a not negligible value for the clustering coefficient: this leads the way to say that the network consists of many triads, and authors tend to form very cohesive communities. The low density is expected, considering that edges represent interactions between authors and not all authors interact in the same way. The largest connected component comprises about $75\mathrm{\%}$$75\mathrm{\%}$75%75 \% of the overall number of nodes, which indicates that a significant number of authors consistently interact with each other. Also, there are 160 authors with no interactions, i.e., they commented on comments which have been marked as deleted, thus no information about the author is present. Additionally, the Author Interaction network is useful to highlight the emotional presence exhibited by authors in the post-match discussions: in fact, most authors express slightly positive sentiments, with an average value of 0.1324 ( $\pm 0.4840$$\pm 0.4840$+-0.4840\pm 0.4840 ). The findings observed in the analysis of the Author Interaction network show us several peculiarities regarding eSports spectatorship on Reddit. The LoL subreddit community tends to be active and considerable cohesive within the considered event. Particularly, the size of the largest connected component suggests that a considerable percentage of authors interacted with each other across all post-match discussions. If we also consider the non-negligible clustering coefficient, we can describe the behavior of authors as cohesive. This indicates that authors actively engaged in post-match discussions and were more likely to participate in debates. Since the context of post-match discussion is peculiar, i.e., it focuses on the discussion of a game between two teams, these debates illustrate how authors often expressed and reiterated their opinions dynamically.
表 5 报告了作者互动网络和团队互动网络的一些基本属性值。通过对该表的分析，我们可以发现几个方面的问题。首先，作者互动网络的聚类系数值不容忽视：这说明该网络由许多三元组组成，作者往往形成非常有凝聚力的社区。考虑到边代表了作者之间的互动，而且并非所有作者都以相同的方式进行互动，因此低密度是意料之中的。最大的连接部分约占总节点数的 $75\mathrm{\%}$$75\mathrm{\%}$75%75 \% ，这表明有相当数量的作者一直在相互影响。此外，有 160 位作者没有互动，即他们对已被标记为删除的评论发表了评论，因此没有作者的相关信息。此外，作者互动网络还有助于突出作者在赛后讨论中表现出的情绪：事实上，大多数作者都表达了略微积极的情绪，平均值为 0.1324 ( $\pm 0.4840$$\pm 0.4840$+-0.4840\pm 0.4840 )。作者互动网络的分析结果向我们展示了 Reddit 上电子竞技观众的一些特殊性。在所考虑的赛事中，LoL 子 Reddit 社区趋于活跃并具有相当的凝聚力。特别是，最大连接成分的大小表明，相当大比例的作者在所有赛后讨论中进行了互动。如果考虑到不可忽略的聚类系数，我们可以将作者的行为描述为具有凝聚力。这表明作者积极参与赛后讨论，并更有可能参与辩论。 由于赛后讨论的背景比较特殊，即侧重于讨论两支球队之间的比赛，这些辩论说明了作者如何经常动态地表达和重申自己的观点。

As far as the Team Interaction network is concerned, we observe in Table 5 that the number of nodes is notably smaller compared to the Author Interaction network. Here, the nodes represent the eSports teams participating in the event. More than one hundred
就团队互动网络而言，我们在表 5 中看到，与作者互动网络相比，节点数量明显较少。在这里，节点代表了参加赛事的电子竞技团队。一百多个

Fig. 8. Number of interactions between authors supporting two teams.
图 8.支持两个团队的作者之间的互动次数。

Table 6 表 6
Top most important authors according to the betweenness and eigenvector centrality along with the number of comments, average received score and average sentiment value ( $\pm$$\pm$+-\pm standard deviation); B/E indicates the rank of the author w.r.t. the betweenness and eigenvectory centrality, respectively.
最重要的作者（根据作者间中心度和特征向量中心度，以及评论数量、平均收到分数和平均情感值（ $\pm$$\pm$+-\pm 标准偏差））；B/E 分别表示作者在作者间中心度和特征向量中心度中的排名。

edges are present, with each edge representing a direct interaction between two teams, carried out by authors. It is worth pointing out that the Team Interaction network includes self-edges, i.e., edges connecting the same node. This is because authors supporting the same team often reply to comments or posts submitted by different authors who also support that team. The density of the Team Interaction network is higher than the one of the Author Interaction network, as is its clustering. Furthermore, only three connected components are present. In particular, the largest one includes all teams listed in Table 4, except for Machi Esports and PSG.Talon Esports, which are represented by disconnected nodes. These teams are excluded from the following analyses. To characterize interactions, in Fig. 8 we show the number of interactions between two teams. From this figure, we can observe that the majority of interactions involve the same team. For example, authors supporting G2 Esports replied to authors supporting the same team 3,496 times. It is also interesting to observe the interactions between different teams. For instance, authors supporting Fnatic interacted a significant number of times (721) with authors supporting G2 Esports. This is expected, given that the two teams belong to the same region and have a historic rivalry. It is worth noting that the number of interactions between Chinese teams is very low. This can be explained by the fact that eSports spectators in China comment on local and well-known social media such as Weibo ${}^{10}$${}^{10}$^(10){ }^{10}, rather than Reddit. It is interesting to observe spectators’ experiences through the lens of this network. One of the most peculiar observations that arises is that interactions in the Team Interaction network were much more intricate and cohesive than the ones in the Author Interaction network. This is an expected result since the former network consists of interactions between authors, but these are analyzed with respect to the corresponding supported team. Nevertheless, the consistent values of density and clustering coefficient suggest how the spectatorship experience translated into interactions within the post-match discussions. Thus, experiencing the event through a social media forum, with a format similar to post-match discussions, allowed spectators to experience the event at different levels. This also suggests that eSports organizations could exploit these insights to improve their brand awareness with targeted and interesting events.
边，每条边代表两个团队之间由作者进行的直接互动。值得注意的是，团队互动网络包括自边，即连接同一节点的边。这是因为支持同一团队的作者经常会回复同样支持该团队的不同作者提交的评论或帖子。团队互动网络的密度高于作者互动网络，其聚类也是如此。此外，网络中只有三个相连的部分。其中，最大的一个部分包括表 4 中列出的所有团队，只有 Machi Esports 和 PSG.Talon Esports 除外，这两个团队的节点是断开的。以下分析不包括这两支队伍。为了描述互动的特点，我们在图 8 中显示了两支队伍之间的互动次数。从图中我们可以看出，大多数互动都涉及同一个团队。例如，支持 G2 Esports 的作者回复了支持同一团队的作者 3,496 次。观察不同团队之间的互动也很有趣。例如，支持 Fnatic 的作者与支持 G2 Esports 的作者进行了大量互动（721 次）。这是意料之中的，因为这两支队伍同属一个地区，并且有着历史性的竞争。值得注意的是，中国战队之间的互动次数非常少。这可以解释为中国的电子竞技观众是在微博 ${}^{10}$${}^{10}$^(10){ }^{10} 等本地知名社交媒体上发表评论，而不是在 Reddit 上发表评论。从这一网络的视角来观察观众的经历是非常有趣的。 最奇特的观察结果之一是，团队互动网络中的互动比作者互动网络中的互动更复杂、更有凝聚力。这是意料之中的结果，因为前一个网络由作者之间的互动组成，但这些互动是针对相应的受支持团队进行分析的。然而，密度和聚类系数的一致值表明，观赛体验如何转化为赛后讨论中的互动。因此，通过与赛后讨论形式类似的社交媒体论坛体验赛事，可以让观众在不同层面上体验赛事。这也表明，电子竞技组织可以利用这些见解，通过有针对性和有趣的活动来提高其品牌知名度。

It is also captivating to observe individual authors to understand their behavior within the community. To achieve this, we compute the top 5 authors based on two commonly used centrality measures, namely (i) betweenness centrality and (ii) eigenvector centrality. We recall that a centrality measure assigns a number or a rank to nodes in a network to reflect their importance in the network. In particular, the betweenness centrality of a node in a network is defined as the fraction of the shortest paths between all the pairs of nodes that pass through it (Newman, 2010). The eigenvector centrality measures the influence of a node in a network, and encompasses the idea that a node is important if it is linked to by other important nodes (Newman, 2010). In Table 6, we report the top 5 most important authors according to the betweenness centrality and eigenvector centrality, respectively. In addition to anonymizing the original nicknames, we report each author’s rank in both centralities, the number of comments submitted, as well as the average received score and sentiment value. By analyzing this table, it is possible to note few peculiarities. First off, the top 3
观察单个作者以了解他们在社区中的行为也很有吸引力。为此，我们根据两种常用的中心度量，即(i) 间度中心度 (betweenness centrality) 和 (ii) 特征向量中心度 (eigenvector centrality)，计算出排名前五的作者。我们记得，中心度量是给网络中的节点分配一个数字或等级，以反映它们在网络中的重要性。具体而言，网络中节点的节点间中心度被定义为通过该节点的所有节点对之间最短路径的分数（Newman，2010 年）。特征向量中心度衡量的是一个节点在网络中的影响力，它包含这样一种观点，即如果一个节点与其他重要节点相连，那么这个节点就是重要的（纽曼，2010 年）。在表 6 中，我们分别根据 "间度中心性 "和 "特征向量中心性 "列出了最重要的五位作者。除了对原始昵称进行匿名处理外，我们还报告了每位作者在两个中心度中的排名、提交的评论数量以及平均收到分数和情感值。通过分析这个表格，我们可以发现一些特殊之处。首先，排名前三的

Fig. 9. Distribution of received comment score (a) and sentiment value (b) of comments from authors supporting a team.
图 9.来自支持团队的作者的评论得分（a）和情感值（b）的分布情况。
nodes are equally ranked. This means that both centrality measures assign authors $a_1,a_2$$a_1,a_2$a_(1),a_(2)a_{1}, a_{2}