A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements (2019)

Abstract: Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. Such information is increasing rapidly due to the availability of enormous web information sources including social media feeds, news blogs, online newspapers etc. In this paper, we develop various deep learning models for detecting fake news and classifying them into the pre-defined fine-grained categories. At first, we develop individual models based on Convolutional Neural Network (CNN), and Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations obtained from these two models are fed into a Multi-layer Perceptron Model (MLP) for the final classification. Our experiments on a benchmark dataset show promising results with an overall accuracy of 44.87%, which outperforms the current state of the arts.

## methodology we propose a deep multi-label classifier for classifying a statement into six fine-grained classes of fake news. our approach is based on an ensemble model that makes use of convolutional neural network (cnn) @xcite and bi-directional long short term memory (bi-lstm) (hochreiter and schmidhuber, 1997). the information presented in a statement is essentially sequential in nature. in order to capture such sequential information we use bi-lstm architecture. bi-lstm is known to capture information in both the directions: forward and backward. identifying good features manually to separate true from fake even for binary classification, is itself, a technically complex task as human expert even finds it difficult to differentiate true from the fake news. convolutional neural network (cnn) is known to capture the hidden features efficiently. we hypothesize that cnn will be able to detect hidden features of the given statement and the information related to the statements to eventually judge the authenticity of each statement. we make an intuition that both-capturing temporal sequence and identifying hidden features, will be necessary to solve the problem. as described in data section, each short statement is associated with 11 attributes that depict different information regarding the speaker and the statement. after our thorough study we identify the following relationship pairs among the various attributes which contribute towards labeling of the given statements. to ensure that deep networks capture these relations we propose to feed each of the two attributes, say a x and a y , of a relationship pair into a separate individual model say m i and m j respectively. then, concatenate the output of m i and m j and pass it through a fully connected layer to form an individual relationship network layer say n etwork n representing a relation. fig. 1 illustrates an individual relationship network layer. eventually after capturing all the relations we group them together along with the fivecolumn attributes containing information regarding speaker's total credit history count. in addition to that, we also feed in a special feature vector that is proposed by us and is to be formed using the count history information. this vector is a five-digit number signifying the five count history columns, with only one of the digit being set to '1' (depending on which column has the highest count) and the rest of the four digits are set to '0'. the deep ensemble architecture is depicted in fig. 2 . ## experiments and results in this section, we report on the experimental setup, evaluation results, and the necessary analysis. ## conclusion and future works in this paper, we have tried to address the problem of fake news detection by looking into short political statements made by the speakers in different types of daily access media. the task was to classify any statement into one of the fine-grained classes of fakeness. we have built several deep learning models, based on cnn, bi-lstm and the combined cnn and bi-lstm model. our proposed approaches mainly differ from previously mentioned models in system architecture, and each model performs better than the state of the art as proposed by @xcite , where the statements were passed through one lstm and all the other details about speaker's profile through another lstm. on the other hand, we have passed every different attribute of speaker's profile through a different layer, captured the relations between the different pairs of attributes by concatenating them. thus, producing a meaningful vector representation of relations between speaker's attributes, with the help of which we obtain the overall accuracy of 44.87%. by further exploring the confusion matrices we found out that classes which are closely related in terms of meaning are getting overlapped during prediction. we have made a thorough analysis of the actual statements, and derive some interesting facts. there are some speakers whose statements are not present in the training set but present in the test set. for some of those statements, our model tends to produce the wrong answers. this shows the importance of speakers' profile information for the task. also as the classes and the meaning of the classes are very near, they tend to overlap due to less number of examples in training data. we would like like to highlight some of the possible solutions to solve the problems that we encountered while attempted to solve fake news detection problem in a more fine-grained way. fake news detection into finely grained classes that too from short statements is a challenging but interesting and practical problem. hypothetically the problem can be related to sarcasm detection @xcite problem. thus it will also be interesting to see the effect of implementing the existing methods that are effective in sarcasm detection domain in fake news detection domain.