Convolutional Neural Networks (CNNs) have demonstrated promising performance in many NLP tasks owing to their excellent local feature-extraction capability. Many previous works have made word-level 2D CNNs deeper to capture global representations of text. Three-dimensional CNNs perform excellently in CV tasks through spatiotemporal feature learning, though they are little utilized in text classification task. This paper proposes a simple, yet effective, approach for hierarchy feature learning using 3D CNN in text classification tasks, named Text3D. Text3D efficiently extracts rich information through text representations structured in three dimensions produced by pretrained language model BERT. Specifically, our Text3D utilizes word order, word embedding and hierarchy information of BERT encoder layers as features of three dimensions. The proposed model with 12 layers outperforms the baselines on four benchmark datasets for sentiment classification and topic categorization. Text3D with a different hierarchy of output from BERT layers demonstrates that the linguistic features from different layers have varied effects on text classification.

The rapid spread of online misinformation poses a serious threat to public trust and social stability, making automatic fake news detection increasingly critical. In this work, we propose a novel retrieval-augmented fake news detection framework, Triple-R (Rewriting, Retrieval, and iterative Refinement), which emphasizes optimizing the retrieval query. Unlike prior retrieval-augmented methods that adapt either the retriever or the verification module, often overlooking the gap between news text and the evidence needed for verification, our approach focuses on adapting the search query to retrieve the most relevant evidence. Specifically, we employ a small language model as a trainable query rewriter, optimized via reinforcement learning with feedback from a frozen LLM-based fake news detector, to transform the original news text into effective retrieval queries. To further enhance evidence relevance, we introduce an iterative query refinement mechanism, which progressively updates rewritten queries based on previously retrieved results. Finally, the original news text and the evidence retrieved through refined queries are integrated for verification. Experiments on two real-world datasets demonstrate consistent improvements, validating the effectiveness of our approach.