With the booming rise of the Internet and social media, semantically rich multimodal data has gradually become the mainstream carrier of news dissemination. Among them, multi-modal fake news with illustrations and text has attracted widespread attention due to its greater deceptiveness. However, existing research methods are mainly limited to the analysis of images and text within the news itself, failing to fully consider the consistency and discrepancy characteristics between different modalities, which hinders the full exploitation of the advantages of multi-modal fusion. To address this issue, this study proposes a multi-modal fake news detection method with large language models(LLMs) and Knowledge-Aligned Attention Networks(MFDnet). This method first leverages the powerful semantic understanding capabilities of large language models to generate detailed text descriptions for images, serving as a knowledge supplement for the image model. Subsequently, by constructing a Knowledge-Aligned Attention Networks, it achieves efficient semantic fusion between the knowledge-supplemented image model and text modal information, thereby effectively extracting the consistency and complementary features between different modalities. Experimental results demonstrate that this model exhibits excellent performance on multiple public fake news detection datasets.

Community question answering (CQA) becomes increasingly prevalent in recent years, providing platforms for users with various backgrounds to obtain information and share knowledge. However, the redundancy and lengthiness issues of crowd-sourced answers limit the performance of answer selection, thus leading to difficulties in reading or even misunderstandings for community users. To solve these problems, we propose the dual graph question-answer attention networks (DGQAN) for answer selection task. Aims to fully understand the internal structure of the question and the corresponding answer, firstly, we construct a dual-CQA concept graph with graph convolution networks using the original question and answer text. Specifically, our CQA concept graph exploits the correlation information between question-answer pairs to construct two sub-graphs (QSubject-Answer and QBody-Answer), respectively. Further, a novel dual attention mechanism is incorporated to model both the internal and external semantic relations among questions and answers. More importantly, we conduct experiment to investigate the impact of each layer in the BERT model. The experimental results show that DGQAN model achieves state-of-the-art performance on three datasets (SemEval-2015, 2016, and 2017), outperforming all the baseline models.

In recent years, sarcasm detection on social media has become one of the major challenges in natural language processing, due to the figurative characteristics of sarcasm. With the rise of social media platforms, especially Twitter, which allows users to post textual content while attaching a complement (such as image and video). A prevailing trend is to achieve a satire effect via combinations of text and image on Twitter. Therefore, to overcome the above-mentioned difficulty of sarcasm detection on social media, the key is to establish a multi-modal framework that can synchronically capture the information contained in text and vision. In this research, we propose Coupled-Attention Networks (CANs), which can effectively integrate information of text and image into a unified framework, thus realizing the fusion of different forms of resources. We conduct our experiments on a real-world dataset. Experimental results prove that our method achieves excellent results.

Most of the existed fake news detection works on social media driven-fake news mainly focused on text. However, more and more social media platforms like Twitter, facebook, etc, allow users to create multi-modal contents, including text, image and video. Hence, it is obvious that only investigating text contents is insufficient to achieve solid detection. In this paper, we study the fake news on social media platforms composed of multimodal contents (text and images), and propose Dual Attention Fusion Networks for fake news detection on social media. We explore three modalities, (text modality, image modality and image attributes modality), and further propose a Dual Attention Fusion Networks (DAFN) model for this task. First, our proposed model extracts text modality and image modality, respectively. We then pass combinations of image attributes modality and text modality through BERT to extract text features. Finally, we reconstruct features of three modalities and fuse them into a feature vector for prediction. Our method is verified on realworld datasets consisting of collected social media platforms. Experiments show that the our method achieves promising results on real world datasets. outperforming all baseline models.