Skip to main content

Category Specific Post Popularity Prediction

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10704))

Included in the following conference series:

Abstract

Social media have become dominant in everyday life during the last few years where users share their thoughts and experiences about their enjoyable events in posts. Most of these posts are related to different categories related to: activities, such as dancing, landscapes, such as beach, people, such as a selfie, and animals such as pets. While some of these posts become popular and get more attention, others are completely ignored. In order to address the desire of users to create popular posts, several researches have studied post popularity prediction. Existing works focus on predicting the popularity without considering the category type of the post. In this paper we propose category specific post popularity prediction using visual and textual content for action, scene, people and animal categories. In this way we aim to answer the question What makes a post belonging to a specific action, scene, people or animal category popular? To answer to this question we perform several experiments on a collection of 65K posts crawled from Instagram.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://isis-data.science.uva.nl/Masoud/MMM17Data.

References

  1. Bae, Y., Lee, H.: 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)

    Article  Google Scholar 

  2. Borth, D., Ji, R., Chen, T., Breuel, T., Chang, S.-F.: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: MM (2013)

    Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  4. Cappallo, S., Mensink, T., Snoek, C.G.M.: Latent factors of visual popularity prediction. In: ICMR (2015)

    Google Scholar 

  5. Chen, T., Borth, D., Darrell, T., Chang, S.F.: Deepsentibank: visual sentiment concept classification with deep convolutional neural networks. CoRR (2014)

    Google Scholar 

  6. Gelli, F., Uricchio, T., Bertini, M., Del Bimbo, A., Chang, S.F.: Image popularity prediction in social media using sentiment and context features. In: MM (2015)

    Google Scholar 

  7. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: WWW (2011)

    Google Scholar 

  8. Iordache, O.: Self-Evolvable Systems: Machine Learning in Social Media. Understanding Complex Systems. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28882-1

    Book  Google Scholar 

  9. Khosla, A., Das Sarma, A., Hamid, R.: What makes an image popular? In: WWW (2014)

    Google Scholar 

  10. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR (2014)

    Google Scholar 

  11. Li, C., Lu, Y., Mei, Q., Wang, D., Pandey, S.: Click-through prediction for advertising in Twitter timeline. In: KDD (2015)

    Google Scholar 

  12. MacKuen, M.B.: Political drama, economic conditions, and the dynamics of presidential popularity. Am. J. Polit. Sci. 27(2), 165–192 (1983)

    Article  Google Scholar 

  13. Mazloom, M., Hendriks, B., Worring, M.: Multimodal context-aware recommender for post popularity prediction in social media. In: MM (2017)

    Google Scholar 

  14. Mazloom, M., Rietveld, R., Rudinac, S., Worring, M., van Dolen, W.: Multimodal popularity prediction of brand-related social media posts. In: MM (2016)

    Google Scholar 

  15. McParlane, P.J., Moshfeghi, Y., Jose, J.M.: Nobody comes here anymore, it’s too crowded; predicting image popularity on flickr. In: ICMR (2014)

    Google Scholar 

  16. Overgoor, G., Mazloom, M., Worring, M., Rietveld, R., van Dolen, W.: A spatio-temporal category representation for brand popularity prediction. In: ICMR (2017)

    Google Scholar 

  17. Rong, X.: word2vec parameter learning explained. CoRR (2014)

    Google Scholar 

  18. Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)

    Article  Google Scholar 

  19. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR (2015)

    Google Scholar 

  20. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the Amsterdam Academic Alliance Data Science (AAA-DS) Program Award to the UvA and VU Universities. Thanks to Dr Efstratios Gavves for his collaboration in this project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masoud Mazloom .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mazloom, M., Pappi, I., Worring, M. (2018). Category Specific Post Popularity Prediction. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73603-7_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73602-0

  • Online ISBN: 978-3-319-73603-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics