## 2 Related Work
### _Retweet Prediction Based on Heterogeneous Data Sources_  
Usually, in approaches based on heterogeneous data sources authors combine features extracted from the message text and from the social network. There have been attempts to combine content features (extracted from text) and contextual features (extracted from networks) even in some earlier research studies. However, the proposed feature sets are relatively simple and include only partial information extracted from the text and networks. Thus, [PERSON] et al. [34] examined two classes of features that might affect the retweetability of tweets: (i) the content features that include whether the tweet contains URLs, hashtags, and mentions and (ii) the contextual features that include the number of followers and followees, the age of the account, the number of favorite tweets, and the number and frequency of tweets. The results revealed that, when it comes to content features, URLs and hashtags have a strong influence on retweeting. When it comes to contextual features, the number of followers and followees, as well as the age of the account, seem to affect retweetability. Similarly, in [35] the authors studied the effect of the message content in the task of the spreading prediction of ideas. They analyzed the contribution and the limitations of the various feature sets on the information spreading. According to their results, it seems that a combination of content features with temporal and topological network features minimizes prediction error. In one more recent study, [PERSON] and [PERSON] [14] explored the impact of different numerical features extracted from tweet content and network in the task of retweet prediction. They proposed three features from the author's profile that can capture the behavioral pattern of the user: author total activity, author activity per year, and author tweets per year. They performed their experiment by using a large dataset ([PERSON], [PERSON], and Sketch the Cow. \"Archiveteam-Twitter-Stream-2018-08: Free Download, Borrow, and Streaming\". Internet Archive, Archive Team: The Twitter Stream Grab: [[https://archive.org/details/archiveteam-twitter-stream-2018-08](https://archive.org/details/archiveteam-twitter-stream-2018-08)]([https://archive.org/details/archiveteam-twitter-stream-2018-08](https://archive.org/details/archiveteam-twitter-stream-2018-08)), accessed on 20 September 2022) containing 100 million random tweets from the online twitter archive of August 2018. Their results showed that the proposed model has better accuracy when user features are combined with tweet content features. [PERSON] et al. [13] combined text and network features and proposed a novel deep fusion of multimodal features (DFMF) method for retweet time prediction. Their method combines text features and node features in a way that it constructs a word-embedding layer to learn the semantics of a tweet and a node-embedding layer to learn social relationships within the network. The proposed method is evaluated on the real-world Twitter dataset (UDI-Twitter Crawl-Aug20123: [[https://wiki.illinois.edu/wiki/display/forward/Dataset-UDI-TwitterCrawl-Aug2012](https://wiki.illinois.edu/wiki/display/forward/Dataset-UDI-TwitterCrawl-Aug2012)]([https://wiki.illinois.edu/wiki/display/forward/Dataset-UDI-TwitterCrawl-Aug2012](https://wiki.illinois.edu/wiki/display/forward/Dataset-UDI-TwitterCrawl-Aug2012)), accessed on 20 September 2022) that contains 284 million following relationships, 3 million user profiles, and 50 million tweets. The evaluation results showed that the proposed method was more accurate in predicting the retweet time and can achieve as much as an 11.25% performance improvement on the recall accuracy compared to logistic regression (LR) and support vector machine (SVM). The properties used for retweet prediction may be extracted from the author of the tweet (author-centered prediction) or from the user who will retweet (user-centered prediction). Thus, some recent studies combine features extracted from author, tweet text, and user.