Abstract
Predicting influencers' views and public sentiment on social media is crucial for anticipating societal trends and guiding strategic responses. This study introduces a novel computational framework to predict opinion leaders' perspectives and the emotive reactions of the populace, addressing the inherent challenges posed by the unstructured, context-sensitive, and heterogeneous nature of online communication. Our research introduces an innovative module that starts with the automatic 5W1H (Where, Who, When, What, Why, and How) questions formulation engine, tailored to emerging news stories and trending topics. We then build a total of 60 anonymous opinion leader agents in six domains and realize the views generation based on an enhanced large language model (LLM) coupled with retrieval-augmented generation (RAG). Subsequently, we synthesize the potential views of opinion leaders and predicted the emotional responses to different events. The efficacy of our automated 5W1H module is corroborated by an average GPT-4 score of 8.83/10, indicative of high fidelity. The influencer agents exhibit a consistent performance, achieving an average GPT-4 rating of 6.85/10 across evaluative metrics. Utilizing the 'Russia-Ukraine War' as a case study, our methodology accurately foresees key influencers' perspectives and aligns emotional predictions with real-world sentiment trends in various domains.
Abstract (translated)
预测影响者在社交媒体上的观点和公众情绪是预测社会趋势和指导战略反应的关键。这项研究引入了一个新的计算框架,用于预测意见领袖的观点和公众的情绪反应,回答了互联网沟通无结构、上下文敏感和异质性质所带来的固有挑战。我们的研究引入了一种创新模块,从自动5W1H(哪里,谁,什么时候,什么,为什么,和如何)问题格式化引擎开始,特别针对新兴新闻故事和热门话题。然后,在六个领域中构建了60个匿名意见领袖代理,并使用增强的大语言模型(LLM)与检索增强生成(RAG)相结合来生成观点。随后,我们合成了一些意见领袖的观点,并预测了针对不同事件的情感反应。我们自动5W1H模块的效力得到了平均GPT-4得分为8.83/10的证实,表明其高保真度。影响者代理表现出一致的表现,在评估指标上平均GPT-4得分为6.85/10。将“俄罗斯-乌克兰战争”作为一个案例研究,我们的方法准确预测了关键影响者的观点,将情感预测与现实世界各种领域的情感趋势相一致。
URL
https://arxiv.org/abs/2407.20668
