Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Social Scripts02:10

Social Scripts

10.3K
People tend to know what behavior is expected of them in specific, familiar settings. A script is a person’s knowledge about the sequence of events expected in a specific setting (Schank & Abelson, 1977). Essentially, scripts are a particular kind of schema, one containing default values for the features within an event. In the restaurant example, the script's features include the props (e.g., tables, menu, food, and money), the roles to be played (e.g., customer and waiter),...
10.3K
Social Proof00:52

Social Proof

32.4K
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.
32.4K
Language01:16

Language

921
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
921
Social Traps01:41

Social Traps

27.0K
Social traps are negative situations where people get caught in a direction or relationship that later proves to be unpleasant, with no easy way to back out of or avoid. The concept was orignally introduced by John Platt who applied psychology to Garrett Hardin's "Tragedy of the Commons", where in New England herd owners could let their cattle graze in the common ground. This situation seems like a good idea, but an individual could have an advantage. If they owned...
27.0K
Social Facilitation01:04

Social Facilitation

36.6K
Not all intergroup interactions lead to negative outcomes. Sometimes, being in a group situation can improve performance. Social facilitation occurs when an individual performs better when an audience is watching than when the individual performs the behavior alone. This typically occurs when people are performing a task for which they are skilled.
36.6K
Social Loafing01:37

Social Loafing

39.7K
Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
39.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Photolysis of CO<sub>2</sub> Carbamate for Hydrocarboxylation Reactions.

Journal of the American Chemical Society·2026
Same author

ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments.

Proceedings. IEEE International Conference on Cloud Computing·2024
Same author

Deep Learning Methods to Help Predict Properties of Molecules from SMILES.

Proceedings of the International Symposium on Intelligent Computing and Networking 2024 : (ISICN 2024). International Symposium on Intelligent Computing and Networking (1st : 2024 : San Juan, P.R.)·2024
Same author

Lung Ultrasound Score, Severity of Acute Lung Disease, and Prolonged Mechanical Ventilation in Children.

American journal of respiratory and critical care medicine·2024
Same author

Finding Similar Tweets in Health Related Topics.

2021 IEEE International Conference on Digital Health (ICDH)·2022
Same author

Additional evidence that the rat renal interstitium contracts in vivo.

PloS one·2019

相关实验视频

Updated: Feb 12, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats
15:01

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Published on: January 18, 2013

15.9K

用大型语言模型在社交媒体中打击与健康相关的错误信息.

Moisés Robles-Pagán1, Manuel Rodríguez-Martínez1

  • 1University of Puerto Rico - Mayagüez, Mayagüez PR 00680, USA.

Proceedings of the International Symposium on Intelligent Computing and Networking 2025 : Isicn 2025. International Symposium on Intelligent Computing and Networking (2025)
|February 11, 2026
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 可以有效地打击社交媒体上的健康虚假信息. 精心调整的模型准确地分类与健康相关的内容,并识别虚假信息,帮助专家反驳努力.

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias
09:03

Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias

Published on: February 29, 2020

6.3K

相关实验视频

Last Updated: Feb 12, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats
15:01

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Published on: January 18, 2013

15.9K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias
09:03

Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias

Published on: February 29, 2020

6.3K

科学领域:

  • 人工智能的人工智能
  • 公共卫生 公共卫生
  • 计算语言学 计算语言学

背景情况:

  • 社交媒体平台面临着健康错误信息的传播带来的重大挑战.
  • 准确识别和反驳医学错误信息对于公共卫生至关重要.
  • 应对在线健康错误信息的现有方法需要加强.

研究的目的:

  • 微调大型语言模型 (LLM) 以识别和应对社交媒体上的与健康有关的虚假信息.
  • 评估T5,BERT和LaMa-2模型在分类健康内容和检测虚假信息方面的表现.
  • 开发一个利用Retrieval Augmented Generation (RAG) 来反驳被发现的错误信息的系统.

主要方法:

  • 微调T5,BERT和LaMa-2模型在两个阶段:与健康相关的文本分类和虚假信息验证.
  • 采用搜索增强生成 (RAG) 来查询可信的医疗数据库以获得准确的信息.
  • 通过使用精度,回忆和F1分数来实验评估模型性能.

主要成果:

  • 模型实现了94%的精度,95%的回忆率和90%的F1对与健康有关的内容进行分类.
  • 虚假信息文本的分类准确率为99%,回忆率为95%,F1率为97%.
  • 开发的系统在识别和分类健康虚假信息方面具有很高的准确性.

结论:

  • 精心调整的LLM是打击社交媒体上的健康虚假信息的高效工具.
  • 通过利用权威的医学来源,RAG方法可以有效地反驳错误信息.
  • 本系统为卫生专家提供了一种有价值的解决方案,以管理在线与健康相关的错误信息.