Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024)

Abstract: Short video fake news detection is crucial for combating the spread of misinformation. Current detection methods tend to aggregate features from individual modalities into multimodal features, overlooking the implicit opinions and the evolving nature of opinions across modalities. In this paper, we mine implicit opinions within short video news and promote the evolution of both explicit and implicit opinions across all modalities. Specifically, we design a prompt template to mine implicit opinions regarding the credibility of news from the textual component of videos. Additionally, we employ a diffusion model that encourages the interplay among diverse modal opinions, including those extracted through our implicit opinion prompts. Experimental results on a publicly available dataset for short video fake news detection demonstrate the superiority of our model over state-of-the-art methods.

## introduction fake news has the potential to mislead the public, influencing their decisions and behaviors. identifying and stopping the spread of fake news promptly is vital for maintaining societal stability. initially, fake news primarily relied on textual content @xcite . however, with the rise of online video platforms, fake news dissemination expanded to include videos @xcite . short video news comprises multiple elements such as images, videos, audio, social content, comments, and various media sources. fake news creators can employ various manipulative tactics on different dimensions of video content, thereby complicating the task of assessing the authenticity of a news story. the modality features of fake news contain various aspects of information. in order to distinguish comments one day in feb. 1994, about 300,000 rats committed suicide by jumping off a cliff at a reservoir, a large number of rats floating on the water... transcript there's not enough food to live! …… the campus atmosphere is very good … primary school students eating at a different peak went viral online. in the video, some students eat while others, wearing masks, wait their turn. title information that is not relevant to fake news judgments, we use opinion to refer to the judgement related to fake news contained in each modality, which is the result obtained from the modality features. currently, approaches for identifying fake news in short videos have shown promising results by skillfully combining various modality features @xcite . however, these methods mainly focus on incorporating single-modal features into multimodal ones for authenticity assessment, neglecting the implicit opinion on the authenticity of news and the dynamic evolution of opinions across different modalities. implicit opinions are instrumental in identifying fake news. this is because, in fake news, the creators often conceal their true opinions and mislead readers through seemingly objective statements. for instance, consider short video news depicted in figure 1 (a), the video, transcript, and comments are all discussing the event of rats collectively committing suicide by jumping off cliffs. this apparent consistency across multiple modalities may lead the public to believe the news is real. however, it's a deliberate fabrication by the publisher for sensationalism. such deceptive content isn't easily discerned as fake at first glance; instead, its authen-ticity requires assessing the implied opinions in the news. therefore, identifying implicit opinions is of great significance for assessing news authenticity. opinion evolution is crucial since fake news often contains localized deceptive elements in only a few modalities. opinions regarding news authenticity vary across modalities, and individual frames in videos or specific sentences in texts may not significantly influence other single-modal opinions, potentially leading to erroneous authenticity judgments. for example, as illustrated in figure 1(b) , a short video news is fake due to tampering in the blue section of the title, yet opinions from other modalities suggest the news is real. integrating such diverse modalities might erroneously conclude the news is real, disregarding the presence of fake information. to accurately evaluate the authenticity of such news, there is a need for mutual reinforcement among different modal opinions to comprehensively diffuse deceptive elements. therefore, uncovering opinion evolution is essential for detecting fake news in short videos. to address the aforementioned issues, we analyze the implicit opinions embedded within short video news and facilitate the evolution of both explicit and implicit opinions across all modalities within the news. we propose the model opevfake, unveiling the opinion evolution via prompting and diffusion for short video fake news detection. firstly, to conduct a thorough analysis of news authenticity, we create a customized chain-of-thought prompt template that is specifically designed for mining implicit opinions regarding the credibility of news from their textual content. subsequently, recognizing that multimodal interaction involves a continuous updating and summarizing of opinions by each modality according to specific rules, we design a diffusion model to facilitate interaction among the various modal opinions including the generated implicit opinion prompts. finally, we employ the evolved opinions for the purpose of classification. the contributions of this paper can be summarized as follows: ## conclusion we leverage the analytical capabilities of llms to assist in fake news detection tasks and devise opinion evolution based on a diffusion model to achieve cross-modal interaction. the proposed model can better strengthen the interaction of opinion in each modality in detecting fake videos. experimental results demonstrate that our model outperforms existing fake news detection methods on a publicly available dataset for short video fake news detection. we analyze the effectiveness of opinion evolution and discuss how to utilize llms to assist in fake news detection tasks.