Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present Coco, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, Coco leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, Coco employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that Coco can discover numerous data constraints from code analysis and improve real-world application performance significantly.

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novelKnowledge-guided Dual-consistency Networkis proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions.

Detecting rumors on social media has become particular important due to the rapid dissemination and adverse impacts on our lives. Though a set of rumor detection models have exploited the message propagation structural or temporal information, they seldom model them altogether to enjoy the best of both worlds. Moreover, the dynamics of knowledge information associated with the comments are not involved, either. To this end, we propose a novel Dual-Dynamic Graph Convolutional Networks, termed as DDGCN, which can model the dynamics of messages in propagation as well as the dynamics of the background knowledge from Knowledge graphs in one unified framework. Specifically, two Graph Convolutional Networks are adopted to capture the above two types of structure information at different time stages, which are then combined with a temporal fusing unit. This allows for learning the dynamic event representations in a more fine-grained manner, and incrementally aggregating them to capture the cascading effect for better rumor detection. Extensive experiments on two public real-world datasets demonstrate that our proposal yields significant improvements compared to strong baselines and can detect rumors at early stages.

Nowadays, with the development of multimedia technology, rumor spreaders tend to produce false information with multi-modal content to attract the attention of the news readers. However, it is challenging to capture implicit clues among multi-modal data to produce effective representations of false information detection. Moreover, as they tend to evade the detector, it is necessary to develop a robust detection modal that can resist multi-modal adversarial attacks, which is less studied in existing works. To address these issues, in this paper, we propose a novel multi-modal false information detection framework with adversarial training (MFAT). By adopting a pretrained multi-modal model and a cross-modal attention mechanism, MFAT is able to capture fine-grained element-level relationships and coarse-grained modal-level relationships simultaneously, and thus can better capture various multi-modal clues. Additionally, MFAT is also enhanced in robustness and generalization by defending against the adversarial attacks on multi-modal features. Experiments on two real-world datasets demonstrate that MFAT can significantly outperform state-of-the-art baselines. We also show the impacts of three types of multi-modal attacks, and verify that the invulnerability of the model is improved. Codes will be released upon acceptance.