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Multimodal Popularity Prediction of Brand-related Social Media Posts

Published: 01 October 2016 Publication History

Abstract

Brand-related user posts on social networks are growing at a staggering rate, where users express their opinions about brands by sharing multimodal posts. However, while some posts become popular, others are ignored. In this paper, we present an approach for identifying what aspects of posts determine their popularity. We hypothesize that brand-related posts may be popular due to several cues related to factual information, sentiment, vividness and entertainment parameters about the brand. We call the ensemble of cues engagement parameters. In our approach, we propose to use these parameters for predicting brand-related user post popularity. Experiments on a collection of fast food brand-related user posts crawled from Instagram show that: visual and textual features are complementary in predicting the popularity of a post; predicting popularity using our proposed engagement parameters is more accurate than predicting popularity directly from visual and textual features; and our proposed approach makes it possible to understand what drives post popularity in general as well as isolate the brand specific drivers.

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    cover image ACM Conferences
    MM '16: Proceedings of the 24th ACM international conference on Multimedia
    October 2016
    1542 pages
    ISBN:9781450336031
    DOI:10.1145/2964284
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    Published: 01 October 2016

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    Author Tags

    1. brand-related user post
    2. instagram
    3. popularity prediction

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    MM '16: ACM Multimedia Conference
    October 15 - 19, 2016
    Amsterdam, The Netherlands

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    MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

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    • (2024)Heterogeneous Hierarchical Fusion Network for Multimodal Sentiment Analysis in Real-World EnvironmentsElectronics10.3390/electronics1320413713:20(4137)Online publication date: 21-Oct-2024
    • (2024)Higher-Order Vision-Language Alignment for Social Media PredictionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3688999(11457-11463)Online publication date: 28-Oct-2024
    • (2024)Creating an Intelligent Social Media Campaign Decision-Support MethodProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659543(149-158)Online publication date: 22-Jun-2024
    • (2024)Multimodal Temporal Fusion Transformers are Good Product Demand ForecastersIEEE MultiMedia10.1109/MMUL.2024.337382731:2(48-60)Online publication date: 1-Apr-2024
    • (2024)Evidential Reasoning Approach for Predicting Popularity of Instagram PostsIEEE Access10.1109/ACCESS.2024.351063712(182603-182617)Online publication date: 2024
    • (2024)Decoding digital engagement: a comparative analysis of English and Turkish brand post popularity dynamics on platform XJournal of Research in Interactive Marketing10.1108/JRIM-10-2023-0368Online publication date: 24-May-2024
    • (2024)Social mood and M&A performance: An empirical investigation enhanced by multimodal analyticsJournal of Business Research10.1016/j.jbusres.2024.114614176(114614)Online publication date: Apr-2024
    • (2024)Enhancing social media post popularity prediction with visual contentJournal of the Korean Statistical Society10.1007/s42952-024-00270-753:3(844-882)Online publication date: 21-May-2024
    • (2023)Multimodal Sentiment Analysis in Realistic Environments Based on Cross-Modal Hierarchical Fusion NetworkElectronics10.3390/electronics1216350412:16(3504)Online publication date: 18-Aug-2023
    • (2023)Examining visual impact: predicting popularity and assessing social media visual strategies for NGOsOnline Media and Global Communication10.1515/omgc-2023-00252:4(594-620)Online publication date: 1-Nov-2023
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