Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes

Quyu Kong, Marian Andrei Rizoiu, Lexing Xie

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    10 Citations (Scopus)

    Abstract

    It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades. We first start from the observation that maximum likelihood estimates for content virality and influence decay are separable in a Hawkes process. Next, our proposed model, which leverages a Borel mixture model and a kernel mixture model, jointly models the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and supplies interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets - - one relating to YouTube videos and the second relating to controversial news articles - - we show that our models capture the differences between online items at the granularity of items, publishers and categories. In particular, we are able to distinguish between far-right, conspiracy, controversial and reputable online news articles based on how they diffuse through social media, achieving an F1 score of 0.945. On holdout datasets, we show that the dual mixture model provides, for reshare diffusion cascades especially unpopular ones, better generalization performance and, for online items, accurate item popularity predictions.

    Original languageEnglish
    Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
    PublisherAssociation for Computing Machinery
    Pages645-654
    Number of pages10
    ISBN (Electronic)9781450368599
    DOIs
    Publication statusPublished - 19 Oct 2020
    Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
    Duration: 19 Oct 202023 Oct 2020

    Publication series

    NameInternational Conference on Information and Knowledge Management, Proceedings

    Conference

    Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
    Country/TerritoryIreland
    CityVirtual, Online
    Period19/10/2023/10/20

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