What's in a Caption? Leveraging Caption Pattern for Predicting the Popularity of Social Media Posts

Shintami Chusnul Hidayati, Raden Bimo Rizki Prayogo, Satria Ade Veda Karuniawan, Mhd Fadly Hasan, Yeni Anistyasari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In the past few years, social media has become an integral part of modern society. It also hassurfaced as an influential tool that helps a business or individual in gaining identity and reputation. Predicting the popularity of images before they are posted on social media thus may have a profound impact to reveal individual preference and public attention. However, an accurate prediction is a challenging task, mainly on account of factors that play a part in this. Previous studies, although achieve favourable results, overlook one unique characteristic of semantics in textual metadata, i.e., the language modeling, to better model the context information of a post. To that end, wepropose to exploit the language modeling features together with user profile and post metadata features. The language model features are extracted by utilizing the probability of word occurrence, while the user profile and post metadata features are provided as attributes by the original data source. Several state-of-the-art statistical modeling techniques are employed to investigate the performance of the proposed features on different estimation procedures. Experiments on a large-scale Flickr dataset demonstrate the benefits of the proposed features on predicting the popularity of social media posts.

Original languageEnglish
Title of host publicationProceeding - 2020 3rd International Conference on Vocational Education and Electrical Engineering
Subtitle of host publicationStrengthening the framework of Society 5.0 through Innovations in Education, Electrical, Engineering and Informatics Engineering, ICVEE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728174341
DOIs
Publication statusPublished - 3 Oct 2020
Event3rd International Conference on Vocational Education and Electrical Engineering, ICVEE 2020 - Virtual, Surabaya, Indonesia
Duration: 3 Oct 20204 Oct 2020

Publication series

NameProceeding - 2020 3rd International Conference on Vocational Education and Electrical Engineering: Strengthening the framework of Society 5.0 through Innovations in Education, Electrical, Engineering and Informatics Engineering, ICVEE 2020

Conference

Conference3rd International Conference on Vocational Education and Electrical Engineering, ICVEE 2020
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period3/10/204/10/20

Keywords

  • affective computing
  • popularity prediction
  • social media
  • textual pattern

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