Rumor Detection on T witter Using Multiloss Hierarchical B i LSTM with an Attenuation Factor (2020)

Abstract: Social media platforms such as Twitter have become a breeding ground for unverified information or rumors. These rumors can threaten people’s health, endanger the economy, and affect the stability of a country. Many researchers have developed models to classify rumors using traditional machine learning or vanilla deep learning models. However, previous studies on rumor detection have achieved low precision and are time consuming. Inspired by the hierarchical model and multitask learning, a multiloss hierarchical BiLSTM model with an attenuation factor is proposed in this paper. The model is divided into two BiLSTM modules: post level and event level. By means of this hierarchical structure, the model can extract deep information from limited quantities of text. Each module has a loss function that helps to learn bilateral features and reduce the training time. An attenuation factor is added at the post level to increase the accuracy. The results on two rumor datasets demonstrate that our model achieves better performance than that of state-of-the-art machine learning and vanilla deep learning models.

## introduction currently, social media has a significant influence on people's daily lives. with social media, people can share information, speak freely and reproduce news online conveniently. take twitter as an example: over 500 million new tweets are sent every day, that is, nearly 5787 tweets per second @xcite . however, unverified information, or rumors, is also diffused in social media; therefore, in the absence of a rumor detection system, social media platforms can become a breeding ground for rumors. in 2013, the associated press's official twitter account was hacked and sent out a rumor that the president of the us was injured in an attack. this rumor caused wide panic and resulted in a brief crash of the stock market, in which investors lost $136 billion in just two minutes @xcite . this incident highlighted that misinformation can threaten people's lives. therefore, an automatic rumor detection system is vital. information popularity in social media has a short lifetime; it usually stays for a few days, which is called the explosive increase phase. for instance, in figure 1 , tweets related to the paris attack only stayed popular for two days @xcite . unfortunately, @xcite confirmed that false information propagated faster and was longer lasting than true information. their research shows that it took nearly six times longer for verified information to reach 1500 people than for a rumor. according to the study, early as possible detection is highly practical to minimize harmful effects before rumors enter into the ## related work current automatic rumor detection systems suffer from low accuracy @xcite . two main approaches are used to debunk misinformation: the traditional method and the artificial intelligence approach. the traditional method manually analyzes text using statistics to define the critical features before detection. @xcite proposed a large number of features for rumor detection by analyzing user attributes, rumor diffusion routes and text. some researchers introduce various sets of features from different perspectives @xcite @xcite . with the development of artificial intelligence, some scholars have attempted to recognize rumors using deep learning. @xcite and @xcite used the rnn model to learn the abstract expression of rumors. @xcite proposed a hierarchical social attention model by combining a deep learning model and feature engineering, which improved the precision of rumor detection. early detection is the most challenging part of rumor detection. many attempts at early rumor detection have been made. @xcite analyzed prominent features of rumor propagation and proposed a probabilistic model. @xcite found that user and linguistic features could be used as important indicators in rumor detection. in addition to traditional methods, machine learning and deep learning have been applied to early rumor detection. @xcite proposed a graph-kernel-based hybrid svm classifier that could capture high-order propagation patterns. zhao et al. (2015) 2017 ) presented an approach that leveraged convolutional neural networks for learning the hidden representations of each tweet in combination with a time series. inspired by multitask learning and the hierarchical structure, we developed a multiloss hierarchical bilstm model with an attenuation factor that has high accuracy and performs well in early rumor detection. ## problem formulation a tweet consists of a limited number of words, some emojis and a few hashtags. this limited text makes it hard to classify misinformation. therefore, we consider combining the source tweet and some of its comments as a whole event for rumor detection. we employ an event as the primary processing unit. an event contains more information and implicit users' stance @xcite @xcite @xcite . our hierarchical structure model begins with word embedding followed by post embedding and event embedding, with a fully connected layer at the end to detect whether the event is a rumor. multiple topics in the dataset are defined as 𝑇 = {𝑇 1 , 𝑇 2 , … 𝑇 |𝑡| } , and each 𝑇 𝑖 contains multiple events, 𝑇 𝑖 = {𝐸 1 , 𝐸 2 , … 𝐸 |𝑒| }. an event consists of a source post and a few comments, 𝐸 𝑒 = {𝑃 𝑠 , 𝑃 1 , 𝑃 3 , … 𝑃 |𝑝| }. notably, different topics have a different number of events, and events contain different numbers of posts, which means our model can handle variable length information. we develop this rumor detection task as a supervised classification problem. the classifier can perform learning via labeled information, that is, 𝑓 𝑒 : {𝐸 1 , 𝐸 2 , … 𝐸 |𝑒| } → 𝑦 𝑒 . at the same time, each post has its own label. here, we define that each post label is identical to its corresponding source post and equivalent to the label of the event. therefore, the post-level classifier 𝑓 𝑝 : {𝑃 𝑠 , 𝑃 1 , 𝑃 3 , … 𝑃 |𝑝| } → 𝑦 𝑝 can be established, and all labels take one of two possible class labels: rumor or nonrumor. ## multiloss hierarchical bilstm with an attenuation factor the experimental results show that the hierarchical structure has strong information expression ability. however, our observations indicate that the hierarchical model has certain deficiencies, and backpropagation has to go through the time steps of all previous layers, which is computationally expensive and inefficient. it may also lead to vanishing gradient problems and substantially increase the training time. to shorten the training time and improve the training efficiency, we proposed a multiloss bilstm hierarchical structure model. compared to the regular hierarchical model, this multiloss model is equivalent to a multitask learning model that can benefit bilaterally from the information features among multiple related tasks. rumor detection at the post level and event level represent two branches under this theme, and the representations learned in the post level can be shared and used to reinforce the feature learning at the event level. importantly, the backpropagation of the post level can help to alleviate the vanishing gradient problem in the early stage; thus, the model is stable and the training time is reduced. although the multiloss function can accelerate model training, the accuracy is slightly reduced because both post and event factors are considered in the classification process. therefore, we added an attenuation factor to the post level to decrease the training time and maintain the high accuracy simultaneously. taking the text of all posts under an event as the input, we first perform word-embedding processing, where the processed word can be expressed as a fixed-length text vector. the formula is as follows: next, all the vectors with the post as the unit pass through the post-level bilstm layer in proper order. here, a deep bilstm structure is used. for each time point t, the formula is as follows: according to the physical meaning of lstm, the cell state ℎ 𝑡 of the uppermost lstm at the last time point is used as the result of the post encoding. due to the use of the bidirectional structure, the final state of both directions is jointed, and an event can be represented by a matrix in which each column is a vector representing a post. the formula is as follows: the event-level bilstm formula is similar to the post-level bilstm. the difference is the input, where post-level bilstm uses 𝑥 𝑖 and the eventlevel bilstm uses x 𝐼 : in the rumor detection classification task, the state of the event-level bilstm top layer at the last time point can be understood as an abstract representation of all post understandings. to shorten the training time, the concept of multitask learning is used as a reference to realize the detection tasks of both posts and events. these two tasks are highly correlated, and the parameters in the post layer can be understood as common features. from the perspective of multitasking, the common feature region can assist the model training for the purpose of rapid convergence. a post-level classifier and an event-level classifier are included in the model. note that the post-level classifier functions only as an auxiliary convergence, so the post-level classifier classifies and backpropagates only the last set of posts for each event. ## conclusion and future work in this paper, we introduced a multiloss hierarchical bilstm with an attenuation factor model for rumor detection. by means of the hierarchical structure, the model can learn deeply from limited text. the multiloss function makes the model learn efficiently and robustly, while the attenuation factor at the post level helps to increase the accuracy of rumor detection. the experimental results based on two pheme datasets demonstrate that the model consistently outperforms other models by a significant margin. the model represents any post and event text with a fixed size length vector, which means it has strong applicability for both early and widely spread rumor detection with only a few modifications. in the future, the model can be extended by implementing social feature engineering to analyze and track rumors. test dataset number of posts in each event test_5 2 -5 test_10 6 -10 test_15 11 -15 test_20 16 -20 test_25 21 -25 test_30 26 -30