Rashid, Junaid, Kim, Jungeun, and Naseem, Usman (2023) Coherent Topic Modeling for Creative Multimodal Data on Social Media. In: Proceedings of the ACM Web Conference 2023. pp. 3923-3927. From: WWW 2023: The ACM Web Conference 2023, April 30 - May 4 2023, Austin, TX, USA.

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Abstract

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.

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