什么驱动在线流行度：作者、内容还是分享者？基于贝叶斯混合霍克斯过程估计传播动态
What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes
AI总结：
提出贝叶斯混合霍克斯模型，联合学习来源、内容和传播因素，在转发级联数据上优于现有模型，并揭示点击诱饵风格对不同出版商的有效性差异。
摘要：
社交媒体上内容的传播受到三个层面相互交织因素的影响：来源、内容本身以及内容传播的路径。在最底层，分享用户的流行度决定了其最终的触达范围。然而，更高层面的因素，如在线内容的性质及其来源的可信度，也在决定在线内容传播的广度和速度方面发挥着关键作用。在这项工作中，我们提出了贝叶斯混合霍克斯（BMH）模型，以联合学习来源、内容和传播的影响。我们将BMH模型构建为可分离霍克斯过程的层次混合模型，容纳不同类别的霍克斯动态以及特征集对这些类别的影响。我们在两个学习任务上测试了BMH模型：冷启动流行度预测和时间剖面泛化性能，并将其应用于两个真实世界的转发级联数据集，这些数据集引用了来自争议性和传统媒体出版商的文章。BMH模型在两个数据集上都优于最先进的模型和预测基线，并且比替代方法更好地利用了级联和条目层面的信息。最后，我们进行了一项反事实分析，将训练好的出版商级BMH模型应用于一组文章标题，并表明标题写作风格（中立、点击诱饵、煽动性）的有效性因出版商而异。BMH模型揭示了争议性与信誉良好的出版商之间风格有效性的差异，我们发现点击诱饵对信誉良好的出版商显著更有效，而对争议性出版商则不然，这与后者过度使用点击诱饵有关。
英文摘要：
The spread of content on social media is shaped by intertwining factors on three levels: the source, the content itself, and the pathways of content spread. At the lowest level, the popularity of the sharing user determines its eventual reach. However, higher-level factors such as the nature of the online item and the credibility of its source also play crucial roles in determining how widely and rapidly the online item spreads. In this work, we propose the Bayesian Mixture Hawkes (BMH) model to jointly learn the influence of source, content and spread. We formulate the BMH model as a hierarchical mixture model of separable Hawkes processes, accommodating different classes of Hawkes dynamics and the influence of feature sets on these classes. We test the BMH model on two learning tasks, cold-start popularity prediction and temporal profile generalization performance, applying to two real-world retweet cascade datasets referencing articles from controversial and traditional media publishers. The BMH model outperforms the state-of-the-art models and predictive baselines on both datasets and utilizes cascade- and item-level information better than the alternatives. Lastly, we perform a counter-factual analysis where we apply the trained publisher-level BMH models to a set of article headlines and show that effectiveness of headline writing style (neutral, clickbait, inflammatory) varies across publishers. The BMH model unveils differences in style effectiveness between controversial and reputable publishers, where we find clickbait to be notably more effective for reputable publishers as opposed to controversial ones, which links to the latter's overuse of clickbait.
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。
