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NesTPP: Modeling Thread Dynamics in Online Discussion Forums

Published: 13 July 2020 Publication History

Abstract

Online discussion forum creates an asynchronous conversation environment for online users to exchange ideas and share opinions through a unique thread-reply communication mode. Accurately modeling information dynamics under such a mode is important, as it provides a means of mining latent spread patterns and understanding user behaviors. In this paper, we design a novel temporal point process model to characterize information cascades in online discussion forums. The proposed model views the entire event space as a nested structure composed of main thread streams and their linked reply streams, and it explicitly models the correlations between these two types of streams through their intensity functions. Leveraging the Reddit data, we examine the performance of the designed model in different applications and compare it with other popular methods. The experimental results have shown that our model can produce competitive results, and it outperforms state-of-the-art methods in most cases.

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Cited By

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  • (2024)Source Localization for Cross Network Information DiffusionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671624(5419-5429)Online publication date: 25-Aug-2024
  • (2023)Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot CaptureElectronics10.3390/electronics1213287512:13(2875)Online publication date: 29-Jun-2023
  • (2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
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    cover image ACM Conferences
    HT '20: Proceedings of the 31st ACM Conference on Hypertext and Social Media
    July 2020
    327 pages
    ISBN:9781450370981
    DOI:10.1145/3372923
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 13 July 2020

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    Author Tags

    1. information dynamics
    2. online discussion forum
    3. temporal point process

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    View all
    • (2024)Source Localization for Cross Network Information DiffusionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671624(5419-5429)Online publication date: 25-Aug-2024
    • (2023)Dynamic End-to-End Information Cascade Prediction Based on Neural Networks and Snapshot CaptureElectronics10.3390/electronics1213287512:13(2875)Online publication date: 29-Jun-2023
    • (2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
    • (2021)Online discussion threads as conversation pools: predicting the growth of discussion threads on redditComputational & Mathematical Organization Theory10.1007/s10588-021-09340-128:2(112-140)Online publication date: 27-Jul-2021

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