AI应用为用户观看的社交媒体视频提供实时和平水平反馈
AI Application Gives Users Real-Time Feedback on the Level of Peace in the Social Media Videos They Watch
AI总结：
开发了一个实时分析YouTube视频中语言和平程度的AI应用，使用监督学习和大语言模型，大语言模型在测量和平相关社会维度上更接近人类编码者。
摘要：
现在大多数人通过社交媒体（如YouTube和Facebook）上的视频获取新闻，而不是通过精心策划的新闻业。“我们成为我们所注视的。”语言的内容和语调在开始或结束冲突中起着至关重要的作用。“仇恨言论”会加剧冲突，“和平言论”会促进和平。我们开发了一个应用程序，可以实时测量YouTube视频中这些方面的言论，从而为用户提供关于自身媒体消费的有用反馈。我们使用了两种方法：1）监督机器学习。在线新闻媒体文本中的语言通过衡量这些国家和平水平的调查进行标记。一个全连接前馈网络和两个卷积神经网络在该数据上训练，在测试集上预测和平水平的准确率约为97%，在另一个不同的新闻文本数据集中准确率约为70%，但未能泛化到YouTube视频，表明书面文本与转录的口语不同。2）社会科学维度。没有类似的外部数据来标记YouTube视频转录文本中的语言。因此，我们使用了2个词级情感分析（SA）和6个上下文级大语言模型（LLM）来测量59项社会科学研究确定的和平中的5个社会维度：同情-蔑视、新闻-观点、促进-预防、创造力-秩序、细微差别-简化。在52个视频上，LLM与3个人类编码者的值更接近（r^2~0.60），而SA的r^2~0.03。结果：与人类编码者相比，LLM成功测量了YouTube视频中与和平相关的重要社会维度。这些结果构成了一个分析引擎的基础，该引擎可以为用户和内容创作者提供关于自身媒体消费和创作的反馈。
英文摘要：
Most people now get their news from videos on social media, such as YouTube and Facebook, rather than through curated journalism. "We become what we behold." The content and tone of language plays an essential role in starting or ending conflicts. "Hate Speech" can enhance conflict, "Peace Speech" can enhance peace. We developed an application that measures, in real time, these aspects of speech from YouTube videos, which can give users helpful feedback on their own media diet. We used two approaches: 1) supervised machine learning. Language in the text of online news media text was tagged by surveys that measure the level of peace in those countries. One fully connected feedforward and 2 convolutional neural networks trained on that data were $\sim 97\%$ accurate in predicting levels of peace in the test set and $\sim 70\%$ accurate in another distinct news text data set, but did not generalize to YouTube videos, suggesting that written text is different than transcribed spoken language. 2) social science dimensions. There is no similar external data to tag the text in the YouTube video transcripts. We therefore used 2 word-level sentiment analysis (SA) and 6 context-level large language models (LLMs) to measure 5 social dimensions in peace identified by 59 social science studies: compassion-contempt, news-opinion, promotion-prevention, creativity-order, nuance-simplification. LLMs more closely matched the values by 3 human coders on 52 videos, $r^2\sim0.60$ than SA, at $r^2\sim0.03$. Results: LLMs successfully measured social dimensions important in peace in YouTube videos, compared to human coders. These results serve as the basis of an analysis engine that can give users and content creators feedback on their own media diet and creations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
