Users and businesses can both benefit from being able to predict how popular a single tweet will be. Adopting current topic or event prediction models, however, does not yield acceptable outcomes. The explanation for this is that a topic or event with several tweets about it has more qualities and characteristics than just one tweet. In order to forecast the popularity of a single tweet at both the early and stable stages, we present two variants of the Heterogeneous Bass models (HBass), which were first created in the field of marketing science. These are the Feature-Driven Heterogeneous Bass Model (FD-HBass) and the Spatial-Temporal Heterogeneous Bass Model (ST-HBass). In order to further enhance performance, we create an interaction enhancement that takes cooperation and competition from several tweets with the same topic into account. Furthermore, it is frequently challenging to quantify popularity. We create an experiment to determine the popularity by calculating the weight of favourites, retweets, and replies. We then use linear regression to find the popularity. In addition, we develop a clustering technique to constrain the popular threshold. It is possible to justify whether or not a tweet will become popular once the weight and popular criteria have been established. Real-world Twitter data is used to evaluate our model through tests, and the outcomes demonstrate the model's efficiency and accuracy with the lowest absolute percent error and the best Precision and F-score. Overall, we demonstrate the superior performance of the Bass model by introducing it into social network single-tweet prediction..

Users and businesses can both benefit from being able to predict how popular a single tweet will be.