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abstract

Cross-Site Prediction on Social Influence for Cold-Start Users in Online Social Networks

Published: 16 August 2022 Publication History

Abstract

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs to emerging OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities which cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

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  • (2024)Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference PredictionACM Transactions on Information Systems10.1145/369783243:1(1-28)Online publication date: 1-Oct-2024

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  1. Cross-Site Prediction on Social Influence for Cold-Start Users in Online Social Networks

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    cover image ACM Conferences
    WWW '22: Companion Proceedings of the Web Conference 2022
    April 2022
    1338 pages
    ISBN:9781450391306
    DOI:10.1145/3487553
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 August 2022

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    Author Tags

    1. Cold-Start Users
    2. Cross-Site Linking
    3. Social Influence

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    • Refereed limited

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    WWW '22
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    WWW '22: The ACM Web Conference 2022
    April 25 - 29, 2022
    Virtual Event, Lyon, France

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    View all
    • (2024)Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference PredictionACM Transactions on Information Systems10.1145/369783243:1(1-28)Online publication date: 1-Oct-2024

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