Popularity Prediction of Micro-Blog Hot Topics Based on Time-Series Data

Topic popularity prediction has always been a popular research field. In previous studies, traditional methods are mostly used to predict the popularity of topics characterized by long text, long period, and large amounts of data. These models do not apply to hot topics generated on social network platforms. Hot topics on social platforms are characterized by rapid generation, development, explosion, and vanishing, resulting in topics often disappearing within a few days or even hours, significantly reducing the amount of data available. According to these features, this paper collects blogs related to the hot topic “2023 Hangzhou Asian Games” on Sina Micro-blog, and designs a hotness formula to measure the topic's popularity within a certain period. Subsequently, two baseline models were trained by combining convolutional neural networks (CNN) and long short-term memory (LSTM). The prediction results are compared using two evaluation metrics, RMSE and MAE. The model proposed in this paper outperforms the two baseline models in prediction.

I. Introduction

With the development of mobile networks and smartphones, more and more users can easily access the mobile Internet using smartphones. Until December 2022, the scale of mobile Internet users in China exceeded 1.4 billion. At the same time, social networking platforms have also gained tremendous development. Many social platforms are widely used by users, such as Facebook, Twitter, and TikTok. In China, WeChat and Sina Micro-blog are more popular.