Social rumor detection based on multilayer transformer encoding blocks

Abstract

The propagation of rumors on social media has been identified as a critical problem in recent years that causes social panic or economic turmoil (to some extent), thereby giving rise to the need for faster identification. With the advancements in deep learning, researches based on neural networks become popular. Most of the existing methods extensively adopt recurrent neural networks (RNNs), such as gated recurrent unit and long short-term memory. This results in a significant degradation in the concurrency performance of the models, implying an increased consumption of time and resources. This study proposes a model with multilayer transformer encoding blocks for detecting rumors. The self-attention mechanism in the transformer encoding blocks provides better concurrency to the proposed model and improves its performance. The proposed model performs faster executions using less or no recurrences in comparison to other models based on RNNs. Experiments on two real social datasets verified that our model could achieve significantly better results than baseline methods. The accuracy rate increased by 1.1% and 2.2% on the Weibo and PHEME datasets, respectively. In comparison to the methods that use RNNs as a feature extractor, the training duration in the proposed model was reduced by 16% and 70% in Weibo and PHEME, respectively.

A Rumor Detection Method from Social Network Based on Deep Learning in Big Data Environment

“…Criminals can use the characteristics of human nature to spread false information and create fear to achieve their own purposes [ 8 – 10 ]. In addition, ordinary people may also publish their hearsay and shadowy information to social media for verification, which will lead to the generation and spread of rumors [ 11 – 14 ]. The widely spread rumors are usually very confusing.…”

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

“…These negative aspects of social networks, represented by the spread of fake news, portend a serious danger that negatively affects society and the lives of citizens. This calls for the existence of models that detect fake news and limit its propagation [ 15 ].…”

“…A survey conducted in 2017 claimed that 67% of people in the US got their news mainly from social media [ 14 ]. In 2021, Facebook announced that about 1.3 billion fake accounts had been closed, and more than 12 million posts containing false information about COVID-19 and vaccines had been deleted [ 15 ]. Recent research has shown that the spread of fake news on social networks has become an urgent global problem that must be addressed and curbed quickly as it spreads social panic and even economic turmoil [ 16 , 17 ].…”

“…Many studies on the identification of fake news on social networks depend on one or more features such as content, network propagation, or user [ 19 , 20 , 21 ]. Analyzing users’ comments to ascertain their attitudes toward the news could play a major role in identifying fake news [ 22 , 23 , 24 ] and giving an idea of the credibility of the published news [ 14 , 15 ]. Albahar [ 25 ] posited that user comments have great discriminatory value in the detection of fake news, wherein the expression of sentiment [ 26 ] or emotion [ 27 ] is crucial.…”

Network Rumor Detection Method Using Deep Learning in Big Data Environment

“…In the rumor detection model, the data set to be used is timely and effective [22]. Microblog is currently the social media platform with top traffic in China, with more than 556 million user resources and an average daily consumption of more than 33 billion per day.…”