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相关概念视频

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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Social Proof00:52

Social Proof

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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.
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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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Factorial Design02:01

Factorial Design

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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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Routes of Persuasion02:20

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Persuasion is the process of changing our attitude toward something based on some kind of communication. Much of the persuasion we experience comes from outside forces. How do people convince others to change their attitudes, beliefs, and behaviors? What communications do you receive that attempt to persuade you to change your attitudes, beliefs, and behaviors?
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Nonconscious Mimicry01:13

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Updated: Jun 21, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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HAPI:一种高效的混合功能基于工程的方法,用于在社交媒体上识别宣传.

Akib Mohi Ud Din Khanday1, Mudasir Ahmad Wani2, Syed Tanzeel Rabani3

  • 1Dept. of Computer Sciences & Software Engineering-CIT, United Arab Emirates University, Al Ain, United Arab Emirates.

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概括
此摘要是机器生成的。

本研究介绍了一种用于宣传识别 (HAPI) 的混合特征工程方法,用于检测社交媒体上的欺骗性信息. 基于SVM的HAPI实现了69.2%的准确性,超过了识别在线宣传的现有方法.

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科学领域:

  • 计算语言学 计算语言学
  • 社交计算社会计算
  • 信息科学 信息科学 信息科学

背景情况:

  • 社交媒体平台促进了快速的信息共享,通常不考虑准确性.
  • 包括假新闻和阴谋论在内的宣传在这些平台上传播,以影响公众论.
  • 有效地检测宣传对于保持信息完整性至关重要.

研究的目的:

  • 为基于文本的内容引入宣传识别 (HAPI) 的混合特征工程方法.
  • 开发和评估用于分类宣传和非宣传推特的机器学习方法.
  • 为了提高宣传探测系统的准确性.

主要方法:

  • 通过API从Twitter收集数据,并为二进制分类 (宣传/非宣传) 提出了一个注释方案.
  • 采用混合功能工程,结合TF-IDF,词汇袋,情感功能和推文长度.
  • 训练和评估多个机器学习分类器,包括SVM,MNB,DT和LR,使用40个选定的功能.

主要成果:

  • 基于支持矢量机器的HAPI (SVM-HAPI) 实现了卓越的性能,总准确率为69.2%,精度为0.69,回忆率为0.69和F-Measure为0.69.
  • 提出的HAPI方法在几个评估指标上超过了大多数现有方法.
  • 所有评估的算法在宣传检测方面都显示出有希望的结果.

结论:

  • 开发的HAPI系统为识别文本内容中的宣传提供了一个强大的解决方案.
  • 这项研究强调了将传统和机器学习功能用于宣传检测的有效性.
  • 未来的研究应该探索深度学习用于多模式宣传检测,包括文本,图像和视频.