## 2 Related Work
### _Retweet Prediction Based on Heterogeneous Data Sources_
For example, [PERSON] et al. [15] proposed a prediction model MDF-RP (multidimensional feature-based retweeting prediction) that combines features extracted from three different sources: author, tweet, and user in order to predict if the user will retweet the given tweet message. The evaluation of the proposed model was performed on the dataset of messages crawled from Weibo (social network in China, similar to Twitter) from 1 June 2018 to 31 July 2018 with 3352 users and 316,829 tweets (the dataset is available upon request). Their results based on different classifiers showed that the performances of MDF-RP outperformed the basic features in terms of precision, recall, and F1 score. On the other hand, [PERSON] et al. performed only user-centered prediction [1]. They analyzed the impact of the users' behaviors on retweet activities based on three aspects: topic preference, emotion, and personality. They proposed two types of retweet-prediction models, one of which uses classification algorithms, and the other matrix factorization algorithms. For their experiment, authors collected a dataset of tweets (dataset of Twitter IDs: [[https://github.com/snadiaf/Twitter-Data](https://github.com/snadiaf/Twitter-Data)]([https://github.com/snadiaf/Twitter-Data](https://github.com/snadiaf/Twitter-Data)), accessed on 20 September 2022). The evaluation results showed that in terms of the F1 score, the proposed classification models based on user behavior-related features provided a 5-9% improvement over baseline models and the matrix factorization model showed a 4-6% improvement over the baseline. Similarly, [PERSON] et al. [17] explored features from different sources related to the hot topics discussed by the users' followees proposing a novel masked self-attentive model to perform retweet prediction. They incorporated the posting histories of users with an external memory and utilized a hierarchical attention mechanism to construct the users' interests. The results obtained on a dataset collected from Twitter with a total of 411,054 users and 36,807,681 tweets showed that the proposed method can perform better than state-of-the-art methods. [PERSON] et al. [16] improved the SVM model for the prediction of user forwarding behavior of hot topics which was also based on user features. The prediction of user retweeting behavior is based on combining three different data sources: user interest tags, user history behavior, and external factors influence. For the purpose of the experiment, the collected dataset of tweets was divided into a training dataset and a test dataset (the training and test datasets are available at: [[https://www.sciencedirect.com/topics/enginering/test-dataset](https://www.sciencedirect.com/topics/enginering/test-dataset)]([https://www.sciencedirect.com/topics/enginering/test-dataset](https://www.sciencedirect.com/topics/enginering/test-dataset)), accessed on 20 September 2022). In addition to retweet prediction, heterogeneous feature sources have also been successfully used to predict buzz tweets. [PERSON] et al. [36], in their study on the classification of \"buzz\" tweets, examine the trends on social media and propose a classification method to study the factors that cause the buzz phenomenon on Twitter. This phenomenon can be understood as an explosion of popularity within a short period of time. The authors note that it is difficult to determine the causes of the buzz phenomenon based solely on texts posted on Twitter. However, they developed a multitask neural network by using both image and text-extracted features as input and buzz class (buzz or non-buzz) and number of \"likes\" and \"retweets\" as output. The text features of the tweets were extracted by using the pre-trained BERT model, and the image features were obtained from pre-trained models such as VGG16. The results of the experiments showed that the correct response rate for predicting buzz classes with the proposed method using both text and image features was higher than when using the text or image features alone [36].