英雄亦问出处:《英雄联盟》游戏角色刻板印象研究

李耘耕, 黄原

国际新闻界 ›› 2024, Vol. 46 ›› Issue (11) : 92-113.

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国际新闻界 ›› 2024, Vol. 46 ›› Issue (11) : 92-113.
研究论文

英雄亦问出处:《英雄联盟》游戏角色刻板印象研究

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Lineage Defines a Hero: Chinese Audiences’ Stereotypes of League of Legends Characters

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摘要

当代的游戏生产致力于改变性别与种族刻板印象,但是这种努力常常收效甚微。本研究基于“品性-能力”刻板印象模型以及计算文本的方法,对《英雄联盟》游戏角色刻板印象进行“编码-解码”分析。研究发现,在编码层面,游戏创作者致力于打破角色类型与性别种族的强关联,但是中国游戏玩家在认知角色时,依然代入了性别与种族的刻板印象,但部分刻板印象也会随着角色类型的多元化而消解。本研究构建了中文刻板印象内容词典,并对中国游戏玩家对《英雄联盟》游戏角色解读视频的弹幕文本进行计算分析。结果发现,与传统刻板印象一致,相比于女性角色,游戏玩家普遍对男性角色品性评价更低,能力评价更高;当女性被设定为英雄或亡命徒这样的传统游戏角色类型时,游戏玩家认为相关类型女性角色能力显著低于男性。但是当女性被设定为反英雄或统治者这样反传统的游戏角色类型时,游戏玩家则对其能力认知与男性同类型角色趋同,这一结果说明赋予女性角色反传统和多元的角色类型,如女性统治者和反英雄,有助于打破性别刻板印象。中国游戏玩家对黄种人和白人形象的游戏角色的能力评价显著高于其他有色人种,但是种族与角色类型对刻板印象认知影响的交互效应不显著。这说明中国游戏玩家对种族刻板印象具有持久性,而且角色类型多元化策略对种族刻板印象的改变作用不大。本研究扩展了刻板印象和“编码-解码”理论在游戏研究中的应用,通过计算文本的新方法工具说明游戏编码需要从多模态角度突破传统的刻板印象,以促进游戏玩家的多元认知。

Abstract

Contemporary game production aims to change gender and racial stereotypes, but these efforts often yield minimal results. This study employs the “warmth-competence” stereotype model and computational text analysis to conduct a “encoding-decoding” analysis of character stereotypes in the game “League of Legends.” The findings reveal that at the encoding level, game creators strive to break the strong association between character types and gender/race. However, when Chinese players perceive characters, they still project gender and racial stereotypes onto them, although some stereotypes dissipate as character types diversify. This research constructs a Chinese stereotype content dictionary and conducts a computational analysis of the bullet comments from videos interpreting “League of Legends” characters by Chinese audiences. The results show that audiences rate male characters lower in warmth but higher in competence than female characters. Women portrayed in traditional roles, such as heroes, are perceived as having lower abilities, whereas those in non-traditional roles, such as anti-heroes, are rated similarly to their male counterparts. This suggests that non-traditional roles can help dismantle gender stereotypes. Additionally, Chinese audiences rate characters representing Asian and White individuals higher in competence than those representing other people of color. However, the interaction effect of race and character type on stereotype perception is not significant. This indicates that Chinese game audiences have a persistent view of racial stereotypes, and the strategy of diversifying character types has little impact on changing these racial stereotypes. This study illustrates the need for game encoding to shift away from traditional stereotypes, promoting diverse perspectives among audiences through multimodal approaches.

关键词

《英雄联盟》 / 刻板印象词典 / 性别 / 种族 / 角色类型

Key words

League of Legends / stereotype dictionaries / gender / race / character setting type

引用本文

导出引用
李耘耕, 黄原. 英雄亦问出处:《英雄联盟》游戏角色刻板印象研究[J]. 国际新闻界. 2024, 46(11): 92-113
LI Yungeng, HUANG Yuan. Lineage Defines a Hero: Chinese Audiences’ Stereotypes of League of Legends Characters[J]. Chinese Journal of Journalism & Communication. 2024, 46(11): 92-113

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注释 [Notes]

1. 根据刻板印象的两个维度分别为“warmth”和“competence” ,可直译为“温良”和“能力”,在中文语境下,我们认为将标识道德维度的“warmth”翻译为“品性”更为贴切,“品性”比“温良”有更广泛的道德含义,因此在刻板印象的测量中能够捕捉更多信息。

2. 角色类型与“品性-能力”的模型对应并非绝对,比如在有些故事里,英雄也可能被设定为高能力类型,而不需要从低能力成长。这一点并不影响本研究后续分析角色类型的多元分布。

3. 角色的类型编码依据英雄的官方传记与故事,这些内容可以从游戏的官方背景故事页面中获取,检索于https://yz.lol.qq.com/zh_CN/

4. 如up主“靠脸吃饭的徐大王”关于“不屈之枪”潘森的英雄介绍视频《战争之王潘森已死 不屈之枪重生永存》截至2024年5月2日,播放量达到了234万。检索于https://www.bilibili.com/video/BV1e4411X7iS/?spm_id_from=333.337.search-card.all.click&vd_source=c347ce489ce685c795a779255858d8a8

5. 《英雄联盟》的官方衍生作品(如奈飞制作的两季动画)较少,因此观看B站游戏角色解读视频的多为该游戏的玩家或对游戏较为熟悉的受众。同时,弹幕因其匿名和即时性,削弱了社会规范带来的压力,受众可以更加真实地表达自己对视频内容的态度。因此,本研究选择弹幕文本作为测量玩家对游戏角色认知刻板印象的基础文本。

6. Word2vec是一种旨在自然语言处理的深度学习算法(Mikolov,Chen,Corrado & Dean,2013)。相较以频率特征表示词语的词袋模型,它能够将自然语言中的单词转化为低维向量的数值形式,即v(w)∈Rm,其中v(w)是语料中的某个词语,m为词向量长度,并在转换后将其嵌入到具有上下文信息的多维向量空间中,每一维代表一定语义。模型参数设置为:词向量的维数为200,将目标词前后共12个词作为上下文,并且省略在语料库中出现少于5次的词。

7. 弹幕基础语料库来自Github:历时弹幕语料库,检索于https://github.com/Chen-X666/DDmkTCCorpus?tab=readme-ov-file。语料库的建构方式详细参见陈鑫,张以欣,吴俊潮,郭凌宇,余泽汇,杨静(2022. https://github.com/Chen-X666/DDmkTCCorpus?tab=readme-ov-file。语料库的建构方式详细参见陈鑫,张以欣,吴俊潮,郭凌宇,余泽汇,杨静(2022)。历时弹幕语料库的构建与探析——以青年亚文化弹幕为例。《情报探索》,(9),1。

8. 单个词向量在这个空间中为一个点,从而可以通过计算点与点在词向量空间中的余弦距离来衡量它们词义的相近程度。对于两个给定的词语向量 A 和B,余弦距离的计算方法为:

9. Word2vec中的词向量和真正语义存在差距,在扩充时会出现反义词、专有词等情况,因此我们进行了最终人工筛选确定词典内容。

10. 图4描绘了角色在玩家印象中“品性”与“能力”的分布,其中横轴为“品性”,纵轴为“能力”。每个点代表一个角色,坐标值反映了对应角色在这两个维度上的得分。图中的虚线分别表示“能力平均值”和“品性平均值”,且将整个图分为四个象限,第一象限的角色被认知为高品性高能力人物;第二象限内的角色被认为是低品性高能力;第三象限内的角色被认为是低品性和低能力;第四象限内的角色被认为是高品性低能力。


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