Choice-Aware User Engagement Modeling andOptimization on Social Media
This work addresses engagement optimization for social media platforms, but it appears incremental as it builds on existing methods like neural networks and clustering for a specific domain.
The paper tackles the problem of maximizing user engagement (likes, replies, retweets) on Twitter by formulating it as a multi-label classification task based on tweet-topic clusters and user history, and reports results from solving an optimization problem using a large Twitter dataset.
We address the problem of maximizing user engagement with content (in the form of like, reply, retweet, and retweet with comments)on the Twitter platform. We formulate the engagement forecasting task as a multi-label classification problem that captures choice behavior on an unsupervised clustering of tweet-topics. We propose a neural network architecture that incorporates user engagement history and predicts choice conditional on this context. We study the impact of recommend-ing tweets on engagement outcomes by solving an appropriately defined sweet optimization problem based on the proposed model using a large dataset obtained from Twitter.