skip to main content
10.1145/3078971.3078998acmconferencesArticle/Chapter ViewAbstractPublication PagesicmrConference Proceedingsconference-collections
research-article

A Spatio-Temporal Category Representation for Brand Popularity Prediction

Published: 06 June 2017 Publication History

Abstract

Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new category based popularity prediction method by incorporating the spatio-temporal dimension in the representation. In particular, we focus on brands as a specific category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.

References

[1]
Y. Bae and H. Lee. Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers. J. Am. Soc. Inf. Sci. Technol., 63(12):2521--2535, 2012.
[2]
D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In MM, 2013.
[3]
S. Cappallo, T. Mensink, and C. G. Snoek. Latent factors of visual popularity prediction. In ICMR, 2015.
[4]
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009.
[5]
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 9:1871--1874, 2008.
[6]
F. Gelli, T. Uricchio, M. Bertini, A. Del Bimbo, and S.-F. Chang. Image popularity prediction in social media using sentiment and context features. In MM, 2015.
[7]
L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In WWW, 2011.
[8]
B. Jiang and Y. Sha. Modeling temporal dynamics of user interests in online social networks. Procedia Computer Science, 51:503--512, 2015.
[9]
A. Khosla, A. Das Sarma, and R. Hamid. What makes an image popular? In WWW, 2014.
[10]
W. G. Mangold and D. J. Faulds. Social media: The new hybrid element of the promotion mix. Business horizons, 52(4):357--365, 2009.
[11]
M. Mazloom, A. Habibian, D. Liu, C. G. M. Snoek, and S.-F. Chang. Encoding concept prototypes for video event detection and summarization. In ICMR, 2015.
[12]
M. Mazloom, R. Rietveld, S. Rudinac, M. Worring, and W. Van Dolen. Multimodal popularity prediction of brand-related social media posts. In MM, 2016.
[13]
P. J. McParlane, Y. Moshfeghi, and J. M. Jose. Nobody comes here anymore, it's too crowded; predicting image popularity on flickr. In ICMR, 2014.
[14]
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013.
[15]
C. North. Toward measuring visualization insight. IEEE Comput. Graph. Appl., 26(3), 2006.
[16]
M. Risius and R. Beck. Effectiveness of corporate social media activities in increasing relational outcomes. Information Management, 52(7):824--839, 2015.
[17]
B. Stieglitz, Dang-Xuan and Neuberger. Social media analytics - an interdisciplinary approach and its implications for information systems. Business Horizons, 6(2):89--96, 2014.
[18]
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
[19]
M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol., 61(12):2544--2558, 2010.
[20]
B. Wu, T. Mei, W.-H. Cheng, and Y. Zhang. Unfolding temporal dynamics: Predicting social media popularity using multi-scale temporal decomposition. In AAAI, 2016.
[21]
J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, 2011.
[22]
D. Zeng, H. Chen, R. Lusch, and S.-H. Li. Social media analytics and intelligence. Intelligent Systems, 25(6):13--16, 2010.

Cited By

View all
  • (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
  • (2022)Simplicity is not key: Understanding firm-generated social media images and consumer likingInternational Journal of Research in Marketing10.1016/j.ijresmar.2021.12.00539:3(639-655)Online publication date: Sep-2022
  • (2020)Learning Visual Elements of Images for Discovery of Brand PostsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/338541316:2(1-21)Online publication date: 22-May-2020
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval
June 2017
524 pages
ISBN:9781450347013
DOI:10.1145/3078971
  • General Chairs:
  • Bogdan Ionescu,
  • Nicu Sebe,
  • Program Chairs:
  • Jiashi Feng,
  • Martha Larson,
  • Rainer Lienhart,
  • Cees Snoek
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. brand representation
  2. multimedia analysis
  3. popularity prediction

Qualifiers

  • Research-article

Funding Sources

  • Amsterdam Data Science

Conference

ICMR '17
Sponsor:

Acceptance Rates

ICMR '17 Paper Acceptance Rate 33 of 95 submissions, 35%;
Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)0
Reflects downloads up to 19 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (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
  • (2022)Simplicity is not key: Understanding firm-generated social media images and consumer likingInternational Journal of Research in Marketing10.1016/j.ijresmar.2021.12.00539:3(639-655)Online publication date: Sep-2022
  • (2020)Learning Visual Elements of Images for Discovery of Brand PostsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/338541316:2(1-21)Online publication date: 22-May-2020
  • (2019)Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal dataJournal of Big Data10.1186/s40537-019-0266-46:1Online publication date: 16-Nov-2019
  • (2019)Prediction of Social Image Popularity DynamicsImage Analysis and Processing – ICIAP 201910.1007/978-3-030-30645-8_52(572-582)Online publication date: 2-Sep-2019
  • (2018)Beyond the ProductProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240689(465-473)Online publication date: 15-Oct-2018
  • (2018)Exploiting Category-Specific Information for Image Popularity Prediction in Social Media2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2018.8551545(45-46)Online publication date: Jul-2018
  • (2018)Category Specific Post Popularity PredictionMultiMedia Modeling10.1007/978-3-319-73603-7_48(594-607)Online publication date: 13-Jan-2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media