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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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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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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Before mRNAs are exported to the cytoplasm, it is crucial to check each mRNA for structural and functional integrity. Eukaryotic cells use several different mechanisms, collectively known as mRNA surveillance, to look for irregularities in mRNAs. Irregular or aberrant mRNA are rapidly degraded by various enzymes. If a defective mRNA escapes the surveillance, it would be translated into a protein which would either be non-functional or not function properly. One of the primary irregularities in...
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Bias01:22

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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相关实验视频

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An Experimental Analysis of Children's Ability to Provide a False Report about a Crime
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Lurker:基于后门攻击的可解释的谣言检测在在线媒体上.

Yao Lin1, Wenhui Xue1, Congrui Bai1

  • 1School of Information Science and Technology, Northwest University, Xi'an, China.

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概括

这项研究介绍了Lurker,一个新的算法,通过识别它们独特的传播结构来检测在线谣言. 露克有效地发现关键用户及其互动,提高了谣言检测的准确性.

关键词:
图形分类的图形分类.图形神经网络是一个神经网络.谣言检测 谣言检测 谣言检测后门攻击后门攻击

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科学领域:

  • 图形神经网络 图形神经网络
  • 计算社会科学 计算社会科学
  • 网络分析 网络分析

背景情况:

  • 图形神经网络 (GNN) 在图形级别的分类方面表现出色,包括谣言检测.
  • 在线谣言通过操纵网络 (例如机器人) 迅速传播,表现出明显的早期传播结构.
  • 识别这些结构是区分谣言和可信信息的关键.

研究的目的:

  • 开发一个可解释的谣言检测算法,灵感来自后门攻击.
  • 识别关键的在线用户及其互动模式,从而定义谣言传播结构.
  • 将谣言检测转化为检测特定传播结构的问题.

主要方法:

  • 使用因果分析来识别因果子图,确定图形分类 (谣言与正常).
  • 提取关键在线用户并探索他们的特定传播模式.
  • 实施基于后门的方法,将已识别的特殊传播结构植入检测模型中作为触发器.

主要成果:

  • 提出的算法,Lurker,在检测特殊的谣言传播结构方面表现出有效性.
  • 在三个真实世界数据集上的实验结果验证了算法的性能.
  • 与基线方法相比,Lurker取得了显著的改进,攻击成功率增加了高达33.1%,清洁精度下降了61.8%.

结论:

  • 这项研究成功地提出了一个可解释的,以后门为灵感的谣言检测算法.
  • 该方法有效地识别了关键用户和传播结构,这对于谣言识别至关重要.
  • 卢克提供了一种有前途的方法来提高谣言检测系统的准确性和可解释性.