# MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
## 2 Related Work
### Information Cascade Prediction  
Studies on information cascades modeling mainly focus on cascade growth prediction [1; 16; 17; 18; 39] and outbreak prediction [10; 16; 40; 41]. Nowadays, methods on information cascades prediction fall into the following three categories:  
* Generative process approaches: These approaches model the intensity function for each message arrival independently. They observe every event and learn the parameters by maximizing the probability during the observation time window. There are mainly two typical generative processes. The first is Poisson process [5; 8], which models the stochastic popularity by employing the reinforced Poisson process and incorporating it into the Bayesian framework for external factor inference and parameter estimation. The second is the Hawkes process [1; 6; 7], which constructs predictors that combine [PERSON] self-exciting point process for modeling each cascade and uses feature-driven methods to estimate the properties of an information cascade, such as content virality, memory decay, and user influence [7]. These methods demonstrate enhanced comprehensibility. However, these methods cannot fully leverage the implicit information in the cascade dynamics for a satisfactory prediction.  
Feature-based approaches: These approaches rely on extracting various features from raw data, typically containing information content features [39; 42; 43; 44], user characteristics [9; 10; 11], cascade's structural attributes [14; 15; 16], and temporal features [12; 13]. These features are fed into discriminative machine learning models to perform predictions. Combining content information with other types of features, such as temporal and structural features, can significantly reduce errors in prediction [39]. Incorporating features related to early adopters [9] demonstrated that user features are informative predictors. In addition, by comparing the prediction power of models using different sets of features, a recent study [45] found that temporal features have the largest impact on prediction.
* Deep learning-based approaches: These approaches are inspired by the recent success of deep learning in many fields and have made significant performance improvement [1; 17; 18; 19] on cascade prediction. The first deep learning-based predictor of information cascades (DeepCas) [17] transforms the cascade graph as node sequences through a random walk and automatically learns the representation of individual graphs. Ref. [1] proposed a deep learning-based process with high predictive power that inherits the high interpretability of [PERSON] process. Ref. [19] introduced to use coverage and attention mechanisms for capturing the cross-dependencies in cascades and alignments to better reflect the structural information, whereas [18] employed topological RNNs to explore the dynamic directed acyclic graph diffusion structure and tailor it for the task of node activation prediction. Refs. [46; 47] used a Graph Neural Network (GNN) to effectively capture structural information and dynamics in cascade. Overall, these approaches treat the cascade modeling as a sequence modeling problem using RNN--avoiding strong prior knowledge imposed by the diffusion models and feature design while flexibly capturing sequential dependence in cascades.