Enhancing User Behavior Modeling via Machine Learning with Combined Text and Image Data

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dc.contributor.author Crowe, Chad
dc.contributor.author Ricks, Brian
dc.contributor.author Hall, Margeret
dc.date.accessioned 2023-12-26T18:38:40Z
dc.date.available 2023-12-26T18:38:40Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other f4ba1ee9-4a1d-4b02-bb31-34dea1471910
dc.identifier.uri https://hdl.handle.net/10125/106685
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining, and Machine Learning for Social Media
dc.subject advertisements
dc.subject deep neural networks
dc.subject ensemble models
dc.subject machine learning
dc.subject social media
dc.title Enhancing User Behavior Modeling via Machine Learning with Combined Text and Image Data
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Existing works typically operate on either image or text data from social media, but rarely work with both content types simultaneously. We propose and validate a technique for combining image and text data for predicting user engagement metrics based on social media data. We collected image and text data from 366,415 Facebook posts and a respective 1,305,375 million comments. The combined model achieves a 3.5x improvement in mean squared error when predicting share count and a 14% improvement for comment sentiment over single data type models. Finally, the study demonstrates the ability to pick more performant advertisement out of 16.7 billion pairs; the resulting machine learning models successfully predicts for a greater comment sentiment, comment count, and share count 93%, 65%, and 63% of the time.
dcterms.extent 10 pages
prism.startingpage 2495
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