About: Feature Driven and Point Process Approaches for Popularity Prediction

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Feature Driven and Point Process Approaches for Popularity Prediction
Creator	Mishra, Swapnil Rizoiu, Marian-Andrei Xie, Lexing
source	ArXiv
abstract	Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with a new hybrid approach and a new performance benchmark. We model each social cascade with a marked Hawkes self-exciting point process, and estimate the content virality, memory decay, and user influence. We then learn a predictive layer for popularity prediction using a collection of cascade history. To our surprise, Hawkes process with a predictive overlay outperform recent feature-driven and generative approaches on existing tweet data [43] and a new public benchmark on news tweets. We also found that a basic set of user features and event time summary statistics performs competitively in both classification and regression tasks, and that adding point process information to the feature set further improves predictions. From these observations, we argue that future work on popularity prediction should compare across feature-driven and generative modeling approaches in both classification and regression tasks.
has issue date	2016-08-17(xsd:dateTime)
bibo:doi	10.1145/2983323.2983812
has license	arxiv
sha1sum (hex)	fca8a095b67b9c13bc969d3b726c3996b9550816
schema:url	https://doi.org/10.1145/2983323.2983812
resource representing a document's title	Feature Driven and Point Process Approaches for Popularity Prediction
resource representing a document's body	covid:fca8a095b67b9c13bc969d3b726c3996b9550816#body_text
is schema:about of	named entity 'point process' named entity 'generative' named entity 'performance benchmark' named entity 'OBSERVATIONS' named entity 'USING' »more»

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