Coherent topic modeling for creative multimodal data on social media

Junaid Rashid, Jungeun Kim*, Usman Naseem

*Corresponding author for this work

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


The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.

Original languageEnglish
Title of host publicationThe ACM Web Conference 2023
Subtitle of host publicationproceedings of the World Wide Web Conference WWW 2023
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages5
ISBN (Electronic)9781450394161
Publication statusPublished - 2023
Externally publishedYes
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023


Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States


  • Topic Modeling
  • Multimodal
  • Creative Web
  • Coherence

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