Rumors clarification with minimum credibility in social networks

Interaction between official institutions and influential users of rumor control in online social networks

Online interactions have become major channels for people to obtain and disseminate information during the new normal of COVID-19, which can also be a primary platform for rumor propagation. There are many complex psychological reasons for spreading rumors, but previous studies have not fully analyzed this problem from the perspective of the interaction between official institutions and influential users. The purpose of this study is to determine optimal strategies for official institutions considering the impact of two different influential user types (trolls and reputed personalities) by designing two game-theoretic models, namely "Rumor Clarification and Interaction Model" and "Rumor Verification and Interaction Model," which can, respectively decide whether to clarify and when to clarify. The results of this article show that clarification strategies can be decided according to the characteristics of rumors and the influential user's reactions. Meanwhile, publishing verified information prevents trolls' "loophole advantages" and prevents reputed personalities from spreading false information due to the vague authenticity of rumors. Results also show that the verification strategy is limited by cost, period, and verification index.

A probabilistic approach toward evaluation of Internet rumor on COVID

Several people around the world have died from the coronavirus (COVID-19) disease. With the increase in COVID-19 cases, distribution, and deaths, much has occurred regarding the ban on travel, border closure, curfews, and the disturbance in the supply of services and goods. The world economy was severely affected by the spread of the virus. Every day, new discussions and debates started, and more people were in fear. Occasionally, unconfirmed information is shared on social media sites as if it were accurate information. Sometimes, it becomes viral and disturbs people's emotions and beliefs. Fake news and rumors are widespread forms of unconfirmed and false information. This type of news should be tracked speedily to prevent its negative impact on society. An ideal system is the dire need of modern-day society to evaluate the Internet rumors on COVID. Therefore, the current study has considered a probabilistic approach for evaluating the Internet rumors about COVID. The fuzzy logic tool in MATLAB was used for experimental and simulation purposes. The results revealed the effectiveness of the proposed work.

Rumor Detection Based on Attention CNN and Time Series of Context Information

This study aims to explore the time series context and sentiment polarity features of rumors’ life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors’ classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.