Enhancing User Behavior Modeling via Machine Learning with Combined Text and Image Data
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Date
2024-01-03
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2495
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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.
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Data Analytics, Data Mining, and Machine Learning for Social Media, advertisements, deep neural networks, ensemble models, machine learning, social media
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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