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In recent years, due to the booming development of online social networks, fake news has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. However, fake news detection remains to be a challenge, primarily because the content is crafted to resemble the truth in order to deceive readers, and without fact-checking or additional information, it is often hard to determine veracity by text analysis alone. In this paper, we first proposed multi-level convolutional neural network (MCNN), which introduced the local convolutional features as well as the global semantics features, to effectively capture semantic information from article texts which can be used to classify the news as fake or not. We then employed a method of calculating the weight of sensitive words (TFW), which has shown their stronger importance with their fake or true labels. Finally, we develop MCNN-TFW, a multiple-level convolutional neural network-based fake news detection system, which is combined to perform fake news detection in that MCNN extracts article representation and WS calculates the weight of sensitive words for each news. Extensive experiments have been done on fake news detection in cultural communication to compare MCNN-TFW with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, it has become an indispensable part of human daily life. Network culture is one of the forms of network information expression, and humans can obtain various information from it. The formation of multiculturalism is inseparable from the effective dissemination of online culture. The information of these cultures not only has an impact on the individual's life but also brings many changes to the society. It is very valuable to be able to quickly detect and disseminate positive factors in the network culture. In view of this situation, this paper first introduces the classification technology of culture and the introduction of semantic association in network text. With the help of semantic association information in network text, this paper proposes a fusion video of convolutional neural network algorithm for football scenes. Cultural factors in the event are tested. The training text is used for initialization learning, and the training module is used to effectively extract the network text features. Based on the convolutional neural network, a detection model of wonderful events was established, which achieved the correct detection of goals, corner kicks, penalty kicks, and red and yellow cards. It can quickly provide useful cultural information to human beings, enabling such cultures to spread rapidly. The experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the big data set around the world, cultural communication in a single-layer social network cannot handle data in a two-tier social network. Combined with the characteristics of cultural communication, a two-layer coupled social network model for predicting cultural information dissemination is proposed. According to the law of big data set in the process of cultural communication, the ethical review and theoretical construction of cultural network communication was carried out. Combining the online two-layer coupling network and the CRF word segmentation algorithm, the known link relationship is obtained, and the feature attributes of the network are extracted. This paper mainly completed the construction of online social network link prediction. Starting from the topological structure of two-layer coupled social network, based on the association rules to analyze the data generated by information dissemination between two-layer coupled network nodes, a two-layer coupled network information propagation law model is proposed. The prediction and construction of the cultural information communication link in the online social network is completed, the process of data opening and sharing is accelerated, and the privacy and copyright issues in the process of cultural communication are solved. The influence of immune node and node self-healing time on the cultural information dissemination process of two-layer coupled social network is discussed. By comparing the information dissemination in social networks and the information dissemination in two-layer coupled social networks, the control of certain lyric cultures can be effectively prevented. The experimental results show that it has reference value. [ABSTRACT FROM AUTHOR]