Prediction of Number of Likes and Retweets based on the Features of Tweet Text and Images

The current study aimed to investigate social media trends and propose an analysis method to explore the factors underpinning the buzz phenomenon on Twitter. As it is not always possible to determine the cause of the buzz phenomenon from the text content alone posted on Twitter, we limited the analysis to tweets with attached images and devised an analysis method using both text and images. We investigated whether there is a relationship between the features of both tweet text and its attached images, and how the relationship between these features is related to the number of likes and retweets (RTs) received—that is, indicators of popularity. We trained a multi-task neural network that takes the features extracted from the images and text as input, and then outputs the number of likes and RTs before extracting the feature vectors of the same dimension from the two inputs (images and text, respectively) from the middle layer. By calculating the distance between these feature vectors, we analyzed the relationship between the number of likes and RTs. The results revealed that the average vectors of BERT and inceptionresnetv2 served as predictors of the number of likes and RTs. We also found that tweet text with a low number of likes and RTs was short and simple.