Developing the Hoax: A Discord Chatbot That Classify Fake News Using Recurrent Neural Network
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Internet has replaced traditional media and become one of the major news media platforms. News from internet sources tend, since they are accessible and convenient, to travel quicker and simpler than conventional news sources. However, not all of the media reports obtained from unverified sources are authentic as fake news arises in large numbers and is prevalent in online communities for both political and commercial reasons. Fake news can deceive or misinform readers theoretically or intentionally because people will easily get tangled by any of this information which may impact on the offline community. Although some manual websites are designed to check if the piece of information is true, the volume of quick-spread information online, notably on the web, does not scale. In order to solve this issue automatic fact-checking applications were designed to tackle the requirement of scalability and automation. However, current application methods lack an inclusive multi-dimensional data set to identify fake news features to improve machine learning classification model performance. To overcome this problem, this research paper proposed the Hoax chatbot which classifies the data when user enters an article headline into it. In this research work, the classification of dataset has been done using recurrent neural network (RNN) and long short-term memory (LSTM) model. The fake and true news dataset are preprocessed and used to train the model. Saved model is deployed on the discord server in order to check the credibility of the given input text. Discord API gives an access to run python files into their chatbot. In terms of analysis, the proposed model outperforms already existed neural network model such as convolutional neural network (CNN) with an accuracy of 96.77%.
Abstract
Internet has replaced traditional media and become one of the major news media platforms. News from internet sources tend, since they are accessible and convenient, to travel quicker and simpler than conventional news sources. However, not all of the media reports obtained from unverified sources are authentic as fake news arises in large numbers and is prevalent in online communities for both political and commercial reasons. Fake news can deceive or misinform readers theoretically or intentionally because people will easily get tangled by any of this information which may impact on the offline community. Although some manual websites are designed to check if the piece of information is true, the volume of quick-spread information online, notably on the web, does not scale. In order to solve this issue automatic fact-checking applications were designed to tackle the requirement of scalability and automation. However, current application methods lack an inclusive multi-dimensional data set to identify fake news features to improve machine learning classification model performance. To overcome this problem, this research paper proposed the Hoax chatbot which classifies the data when user enters an article headline into it. In this research work, the classification of dataset has been done using recurrent neural network (RNN) and long short-term memory (LSTM) model. The fake and true news dataset are preprocessed and used to train the model. Saved model is deployed on the discord server in order to check the credibility of the given input text. Discord API gives an access to run python files into their chatbot. In terms of analysis, the proposed model outperforms already existed neural network model such as convolutional neural network (CNN) with an accuracy of 96.77%.
참고문헌 (13)
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