What is it about?
This study focuses on predicting the number of retweets stemming from any specific tweet on a microblogging platform, Twitter (referred to as X since July 2023). Many approaches have been proposed for this purpose, most of which predict based on the average number of retweets obtained from the predictive distribution. We explore the idea of using theoretically consistent methods to improve the accuracy of these predictions.
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Why is it important?
Predicting retweet counts is crucial for understanding the impact of tweets. Current models often choose practical but not necessarily optimal methods. We delve into the theoretical side to investigate if there are better alternatives to make these predictions. This can lead to more accurate assessments of a tweet's popularity, which is essential for individuals and businesses using Twitter/X for communication and promotion.
Perspectives
Based on the numerical results, using theoretically sound methods did not seem to significantly outperform the more common approach based on averaging the number of retweets from the predictive distribution. Notably, the consistent prediction accuracy based on the average number of retweets suggests its practical value. We believe that exploring these theoretical aspects contributes to the ongoing conversation about improving prediction models, assisting us to better understand the strengths and limitations of different prediction methods in the context of social media engagement.
Wai Hong Tan
Read the Original
This page is a summary of: On the choice of functionals obtained from the predictive distribution of future retweet counts, January 2024, American Institute of Physics,
DOI: 10.1063/5.0192284.
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