Twitter上信息传播的标记自激点过程模型
Marked Self-Exciting Point Process Modelling of Information Diffusion on Twitter
摘要：
信息传播发生在像Twitter这样的微博平台上，因为转发级联。当一条推文被发布时，它可以被转发，并且此后进一步被转发，并且转发过程迭代地和无限期地继续。衡量一条推文受欢迎程度的一个自然标准是它产生的转发数量。对推文受欢迎程度的准确预测可以帮助Twitter更有效地对内容进行排名，并有助于评估营销和竞选策略的潜力。在本文中，我们提出了一个模型，称为显着的自激过程与时间相关的激励功能，或简称MaSEPTiDE，模型的转发动态和预测的鸣叫流行度。我们的模型不需要昂贵的功能工程，但能够利用观察到的动态，以准确地预测未来的演变转推级联。我们将我们提出的方法应用于大量的Twitter数据，并报告了文献中现有方法的预测性能的大幅改善。
英文摘要：
Information diffusion occurs on microblogging platforms like Twitter as retweet cascades. When a tweet is posted, it may be retweeted and henceforth further retweeted, and the retweeting process continues iteratively and indefinitely. A natural measure of the popularity of a tweet is the number of retweets it generates. Accurate predictions of tweet popularity can assist Twitter to rank contents more effectively and facilitate the assessment of potential for marketing and campaigning strategies. In this paper, we propose a model called the Marked Self-Exciting Process with Time-Dependent Excitation Function, or MaSEPTiDE for short, to model the retweeting dynamics and to predict the tweet popularity. Our model does not require expensive feature engineering but is capable of leveraging the observed dynamics to accurately predict the future evolution of retweet cascades. We apply our proposed methodology on a large amount of Twitter data and report substantial improvement in prediction performance over existing approaches in the literature.
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。
