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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Group Polarization01:01

Group Polarization

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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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相关实验视频

Updated: Jun 13, 2025

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
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评估极右极端分子之间的暴力风险:自然语言处理的新角色

Julia Ebner1, Christopher Kavanagh1, Harvey Whitehouse1

  • 1Department of Anthropology and Museum Ethnography, University of Oxford, Oxford, UK.

Terrorism and political violence
|September 11, 2024
PubMed
概括
此摘要是机器生成的。

对极端主义团体的语言分析可以检测暴力极端主义的风险因素. 一个新的框架确定了语言标记,表明潜在的现实世界暴力,帮助早期发现和预防工作.

关键词:
极右极端主义的极右极端主义.认同融合身份融合身份恐怖主义 恐怖主义 恐怖主义暴力风险评估 暴力风险评估 暴力风险评估暴力极端主义 暴力极端主义

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相关实验视频

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科学领域:

  • 极端群体的心理学 极端群体的心理学
  • 计算语言学计算语言学
  • 冲突的社会学冲突的社会学.

背景情况:

  • 为群体而战的意愿源于个人和群体身份的融合.
  • 受到威胁的群体,宽容暴力和对敌人的非人道化是关键因素.
  • 早期发现暴力极端主义风险因素至关重要.

研究的目的:

  • 调查极端主义语言是否可以促进早期发现暴力极端主义风险因素.
  • 开发和应用一种新的基于融合的语言暴力风险评估框架.
  • 分析极右翼极端主义在线团体的语言模式.

主要方法:

  • 应用了基于融合的语言暴力风险评估框架.
  • 利用基于R的自然语言处理 (NLP) 分析.
  • 开发了一种暴力风险指数,将恐怖主义宣言中的语言标记整合在一起.
  • 极端主义团体的风险分数与非极端主义对照组的风险分数进行比较.
  • 结合了定性见解来将发现置于背景.

主要成果:

  • 融合标记和其他语言变量准确地表明了现实世界的暴力水平.
  • 在NLP分析中,每个组都获得了加权的风险评估分数.
  • 确定了显著的语言标记,区分暴力与非暴力公报.
  • 极右翼极端主义团体表现出不同的语言模式.

结论:

  • 语言分析为发现和预防暴力恐怖主义提供了一个有希望的途径.
  • 基于融合的框架有效地识别了与暴力极端主义相关的风险因素.
  • 语言分析可以作为潜在暴力的早期预警系统.