Lifang Fu¹, Huanxin Peng^2,*, Changjin Ma², Yuhan Liu²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053

Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is dominated by textural content and… More >

Lujuan Deng, Boyi Liu^*, Zuhe Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1157-1170, 2024, DOI:10.32604/cmc.2023.042150

Abstract Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data. Concatenating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method. This fusion method does not utilize the correlation information between modalities. To solve this problem, this paper proposes a model based on a multi-head attention mechanism. First, after preprocessing the original data. Then, the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence. Next, the input coding sequence is fed into the transformer model for further… More >