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相关概念视频

Social Proof00:52

Social Proof

Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

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 are...
Stereotype Content Model02:16

Stereotype Content Model

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 categorization, a person will feel...
Group Polarization01:01

Group Polarization

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.
Social Foundations of Self IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

Since the early 2000s, computer-mediated communication (CMC) has grown rapidly, playing a crucial role in self-development. A key distinction between CMC and real-life interactions is the lack of a physically present partner. This absence makes non-verbal cues such as facial expressions, body language, and paralinguistic signals unavailable in CMC platforms like email, instant messaging, or social media. The lack of these cues can create ambiguity and complicate how feedback is interpreted.The...
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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相关实验视频

Updated: Jul 19, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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一个全面的框架,用于多模式的仇恨言论检测在社交媒体使用深度学习的社交媒体.

R Prabhu1, V Seethalakshmi2

  • 1Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, India. prabhu310591@gmail.com.

Scientific reports
|April 15, 2025
PubMed
概括

这项研究介绍了一种新的多模式仇恨言论检测框架 (MHSDF),使用CNN和RNN准确检测文本,图像和视频中的网上仇恨言论. MHSDF显著提高了复杂的多格式内容的检测准确性和可解释性.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.仇恨言论的识别 仇恨言论的识别经常性的神经网络.社交媒体 社交媒体

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 计算机视觉 计算机视觉

背景情况:

  • 在线仇恨言论越来越多地是多模式的,结合了文本,图像,音频和视频.
  • 传统的单模检测系统在处理复杂,异构的数据流方面遇到了困难.
  • 微妙的仇恨言论,如meme和刺视频,带来了重要的检测挑战.

研究的目的:

  • 提出一个新的多模式仇恨言论检测框架 (MHSDF),以加强在线仇恨言论的检测.
  • 利用混合深度学习模型 (CNN和RNN) 来分析多模式数据.
  • 提高仇恨言论检测系统的准确性,稳定性和可解释性.

主要方法:

  • 开发了一个混合框架,将卷积神经网络 (CNN) 集成到空间特征提取和循环神经网络 (RNN),特别是长短期记忆 (LSTM),用于序列数据.
  • 使用高级文字嵌入 (Word2Vec,BERT) 来进行文本分析和光学字符识别 (OCR) 来进行图像文本.
  • 实施了注意力机制,以便在各种模式 (文本,图像,音频,视频) 中有效地融合功能.

主要成果:

  • 实现了98.53%的检测精度比,97.64%的稳定性比和99.21%的性能比.
  • 与现有模型相比,可解释性 (97.71%) 和可扩展性 (98.67%) 显著改善.
  • 基于注意力的解释提供了多模式仇恨言论识别的见解,提高了透明度.

结论:

  • 拟议的MHSDF有效地解决了多模式仇恨言论检测的挑战.
  • 混合CNN-RNN方法与注意力机制提供了卓越的性能和可解释性.
  • 这一框架为打击复杂的网上仇恨言论提供了更加透明和强大的解决方案.