摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者从Menl O College发布的新闻报道，研究表明，“人工智能(AI)和社交媒体的整合为企业SE带来了许多好处，包括改进了受众分析和内容优化。然而，人工智能促进了错误信息的传播，强调了采取平衡兼顾技术积极应用和道德风险的重要性。”新闻记者从门洛学院的研究中获得了一句话：“这篇论文关注人工智能和社交媒体的交叉。研究人员使用混合方法，结合文献计量学和系统的文献回顾技术，分析1540篇学术文献。这项研究的目的是确定最重要的主题和趋势，以及潜在的商业价值和意义。”在人工智能社交媒体领域。研究的第一阶段涉及定量关键词共现分析，结果确定了十个主要主题。这些主题包括会话代理和用户体验，人类情感和内容推荐与调节，应急管理中的集体智能，社交媒体上的算法激进主义，深度假新闻和假新闻，生成人工智能，内容调节系统中的算法偏差，深度情感分析、元宇宙技术和NLP与心理健康检测。然后对每个确定的ME进行定性的主题文献回顾，从而对相关发现提供更深入、特定背景的理解。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Menl o College by NewsRx correspondents, research stated, “The integration of artific ial intelligence (AI) and social media has provided numerous benefits to busines ses, including improved audience analysis and content optimization. However, AI has facilitated the spread of misinformation, emphasizing the importance of taki ng a balanced approach that considers both the technology’s positive application s and its ethical risks.” The news journalists obtained a quote from the research from Menlo College: “Thi s paper looks at the intersection of AI and social media. The researchers use a mixed-method approach to analyze 1540 scholarly documents, combining bibliometri c and systematic literature review techniques. The goal of this research is to i dentify the most important topics and trends, as well as potential business valu es and implications, in the AI Social Media domain. The first stage of the resea rch involved a quantitative keyword co-occurrence analysis, which resulted in th e identification of ten dominant themes. These include Conversational Agents & User Experience, Human Emotion and Content Recommendation & Modera tion, Collective Intelligence in Emergency Management, Algorithmic Activism on s ocial media, Deep Fakes and Fake News, Generative Artificial Intelligence, Algor ithmic Bias in Content Moderation Systems, Deep Sentiment Analysis, Metaverse Te chnologies, and NLP & Mental Health Detection. Each identified the me is then subjected to a qualitative thematic literature review, which provides a more in-depth, contextspecific understanding of the associated findings.”
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