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.

社交媒体上的内容传播受到三个层面的相互影响因素的塑造：源头、内容本身和传播途径。本文提出了贝叶斯混合霍克斯模型，共同学习源头、内容和传播的影响。我们将 BMH 模型制定为可分离霍克斯过程的分层混合模型，适应不同类别的霍克斯动力学和特征集对这些类别的影响。我们将 BMH 模型应用于两个真实世界的转推级联数据集的两个学习任务：冷启动热度预测和时间特征广义性能。BMH 模型在这两个数据集上表现优于现有模型和预测基准，并比其他选项更好地利用了级联和项目级别的信息。最后，我们进行了一个反事实分析，将训练的出版商级别 BMH 模型应用于一组文章标题，并展示了标题写作方式（中立、点击诱导、煽动性）在不同出版商之间的有效性差异。BMH 模型揭示了具有争议性和有声望的出版商之间的风格有效性差异，我们发现点击诱导对有声望的出版商而言比争议性出版商更加有效，这与后者过度使用点击诱导的情况相关。