Multimodal Fake News Video Explanation Generation

Multi-modal explanation involves the assessment of the veracity of a variety of different content, and relies on multiple information modalities to comprehensively consider the relevance and consistency between modalities. Most existing fake news video detection methods focus on improving accuracy while ignoring the importance of providing explanations. In this paper, we propose a novel problem - Fake News Video Explanation (FNVE) - Given a multimodal news containing both video and caption text, we aim to generate natural language explanations to reveal the truth of predictions. To this end, we develop FakeNVE, a new dataset of explanations for truthfully multimodal posts, where each explanation is a natural language (English) sentence describing the attribution of a news thread. We benchmark FakeNVE by using a multimodal transformer-based architecture. Subsequently, a BART-based autoregressive decoder is used as the generator. Empirical results show compelling results for various baselines (applicable to FNVE) across multiple evaluation metrics. We also perform human evaluation on explanation generation, achieving high scores for both adequacy and fluency.

Official-NV: A News Video Dataset for Multimodal Fake News Detection

News media, especially video news media, have penetrated into every aspect of daily life, which also brings the risk of fake news. Therefore, multimodal fake news detection has recently received more attention. However, the number of fake news detection data sets for video modal is small, and these data sets are composed of unofficial videos uploaded by users, so there is too much useless data. To solve this problem, we present in this paper a dataset named Official-NV, which consists of officially published news videos on Xinhua. We crawled videos on Xinhua, and then extended the data set using LLM generation and manual modification. In addition, we benchmarked the data set presented in this paper using a baseline model to demonstrate the advantage of Official-NV in multimodal fake news detection.

Interpretable Rumor Detection in Microblogs by Attending to User Interactions

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.