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Advancements in Fake News Detection Using Machine and Deep Learning Models: Comprehensive Literature Review

Advancements in Fake News Detection Using Machine and Deep L...

引用

International conference on Computational Science and Computational Intelligence (CSCI)

作者： Alkomah, Bushra Sheldon, Frederick Univ Idaho Dept Comp Sci Moscow ID 83843 USA

ISBN: (纸本)9798350361513;9798350372304

In the age of digital technology, the exponential spread of fake news has become a significant issue for society. In response to this issue, considerable advances have been made to identify fake news using machine learning (ML) models. This literature review investigates the current state of research on detecting fake news. It emphasizes the use of ML models such as TF-LIP, Naive Bayes, and Random Forest, as well as deep learning (DL) models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models such as BERT. The review concisely summarizes the essential findings and discusses the potential future implications offake news identification. It also emphasizes the need for additional research to address numerous challenges, such as effective multimedia content management, protection against adversarial attacks, attainment of model generalizability, facilitation of real-time detection, and adherence to ethical standards when developing detection systems. This review is a resource for researchers and practitioners seeking to develop effective methods for addressing the perpetually expanding problem of detecting fake news.

关键词： Fake new detection machine learning deep learning natural language processing dataset feature engineering

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Ensemble Learning for Heterogeneous Large language Models with Deep Parallel Collaboration 38

Ensemble Learning for Heterogeneous Large Language Models wi...

引用

38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Huang, Yichong Feng, Xiaocheng Li, Baohang Xiang, Yang Wang, Hui Liu, Ting Qin, Bing Harbin Institute of Technology China Peng Cheng Laboratory China

Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling. However, existing work focuses on training an extra reward model or fusion model to select or combine all candidate answers, posing a great challenge to the generalization on unseen data distributions. Besides, prior methods use textual responses as communication media, ignoring the valuable information in the internal representations. In this work, we propose a training-free ensemble framework DEEPEN, fusing the informative probability distributions yielded by different LLMs at each decoding step. Unfortunately, the vocabulary discrepancy between heterogeneous LLMs directly makes averaging the distributions unfeasible due to the token misalignment. To address this challenge, DEEPEN maps the probability distribution of each model from its own probability space to a universal relative space based on the relative representation theory, and performs aggregation. Next, we devise a search-based inverse transformation to transform the aggregated result back to the probability space of one of the ensembling LLMs (main model), in order to determine the next token. We conduct extensive experiments on ensembles of different number of LLMs, ensembles of LLMs with different architectures, and ensembles between the LLM and the specialist model. Experimental results show that (i) DEEPEN achieves consistent improvements across six benchmarks covering subject examination, reasoning, and knowledge, (ii) a well-performing specialist model can benefit from a less effective LLM through distribution fusion, and (iii) DEEPEN has complementary strengths with other ensemble methods such as voting. © 2024 Neural information processing systems foundation. All rights reserved.

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A Data Mining Method Based on natural language processing and Machine Learning 4

A Data Mining Method Based on Natural Language Processing an...

引用

4th International conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Tang, Zhihang Lin, Rong Zhou, Zikai Tang, Zipei Hunan Institute of Engineering School of Computer and Communication Xiangtan China Ningbo University of Finance and Economics School of Finance and Information Ningbo China

ISBN: (纸本)9798350308082

The data source and data collection method are described, and the results of initial data are presented. And, the method of word segmentation and stop words removal in the preprocessing process is used to clean the data to obtain the data text before clustering. Then, the application scenarios and main characteristics of K-means algorithm are introduced. The algorithm process and calculation methods of the traditional K means algorithm and the improved dichotomous K-means algorithm are introduced in two sections, finally, the experimental text clustering effects of the two algorithms are tested through experiments, and the experimental results are briefly analyzed. It is found that the improved dichotomous K means clustering algorithm has more stable clustering results, and can get more accurate keywords. © 2023 IEEE.

关键词： K-means clustering

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Divert More Attention to Vision-language Tracking 36

Divert More Attention to Vision-Language Tracking

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Guo, Mingzhe Zhang, Zhipeng Fan, Heng Jing, Liping Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China DiDi Chuxing Beijing Peoples R China Univ North Texas Dept Comp Sci & Engn Denton TX USA

ISBN: (纸本)9781713871088

Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-reliance is not necessary and the pure ConvNets are still competitive and even better yet more economical and friendly in achieving SOTA tracking. Our solution is to unleash the power of multimodal vision-language (VL) tracking, simply using ConvNets. The essence lies in learning novel unified-adaptive VL representations with our modality mixer (ModaMixer) and asymmetrical ConvNet search. We show that our unified-adaptive VL representation, learned purely with the ConvNets, is a simple yet strong alternative to Transformer visual features, by unbelievably improving a CNN-based Siamese tracker by 14.5% in SUC on challenging LaSOT (50.7%.65.2%), even outperforming several Transformer-based SOTA trackers. Besides empirical results, we theoretically analyze our approach to evidence its effectiveness. By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer. Code and models are released at https://***/JudasDie/SOTS.

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Named Entity Recognition for Ancient Chinese Based on Knowledge Embedding 3

Named Entity Recognition for Ancient Chinese Based on Knowle...

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3rd International conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Su, Wen Zhao, Dandan Meng, Jiana Zhang, Zhihao Xu, Yingao Science Education and Preparatory Dalian Minzu University Dalian116600 China

ISBN: (纸本)9798350331387

para>With the acceleration of the digitization process of ancient literature, the automatic extraction of entity information in ancient literature can enable researchers to study ancient history and literature deeply, which is of great significance to the study of ancient literature. Currently exist insufficient characterization of Chinese character information features in ancient literature entity recognition and boundary recognition problem. According to the problem, this paper propose an ancient Named Entity Recognition (NER) model based on knowledge embedded. Through the construction of entity recognition model based on training, in which the lexicon knowledge and PinYin knowledge, and explore a variety of fusion mechanism make the input information to learn the corresponding external knowledge. The model was trained in the ancient ZuoZhuan dataset and the GWPC2023 dataset with other methods. The F1 value reached the optimum compared with the baseline model and other methods. The F1 value of ZuoZhuan and GWPC2023 datasets increased by 1.7% and 1.8%, respectively. The results verified the superiority and validity of the model. © 2023 IEEE.

关键词： natural language processing systems

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OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation 38

OmniTokenizer: A Joint Image-Video Tokenizer for Visual Gene...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Wang, Junke Jiang, Yi Yuan, Zehuan Peng, Binyue Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Bytedance Inc. China

Tokenizer, serving as a translator to map the intricate visual data into a compact latent space, lies at the core of visual generative models. Based on the finding that existing tokenizers are tailored to image or video inputs, this paper presents OmniTokenizer, a transformer-based tokenizer for joint image and video tokenization. OmniTokenizer is designed with a spatial-temporal decoupled architecture, which integrates window and causal attention for spatial and temporal modeling. To exploit the complementary nature of image and video data, we further propose a progressive training strategy, where OmniTokenizer is first trained on image data on a fixed resolution to develop the spatial encoding capacity and then jointly trained on image and video data on multiple resolutions to learn the temporal dynamics. OmniTokenizer, for the first time, handles both image and video inputs within a unified framework and proves the possibility of realizing their synergy. Extensive experiments demonstrate that OmniTokenizer achieves state-of-the-art (SOTA) reconstruction performance on various image and video datasets, e.g., 1.11 reconstruction FID on ImageNet and 42 reconstruction FVD on UCF-101, beating the previous SOTA methods by 13% and 26%, respectively. Additionally, we also show that when integrated with OmniTokenizer, both language model-based approaches and diffusion models can realize advanced visual synthesis performance, underscoring the superiority and versatility of our method. © 2024 Neural information processing systems foundation. All rights reserved.

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Caskow: Context-Aware Stance Detection Using External Knowledge-Augmented LLM

Caskow: Context-Aware Stance Detection Using External Knowle...

引用

International conference on Web Research (ICWR)

作者： Maryam Shafiei Hossein Rahmani Amirhossein Derakhshan Milad Allahgholi School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Stance detection in texts is an important task in natural language processing with diverse applications in political, economic and marketing. The research conducted in this area is categorized into traditional methods, deep learning methods and language model-based methods. However, to the best of our knowledge, none of these methods have effectively addressed context-aware stance detection. In this paper, we propose a method called CASKOW, which first collects context for a given text from Wikipedia as an external resource using information retrieval techniques and then employs the context alongside a large language model (LLM) to detect the stance. Experimental results show that CASKOW achieves a 7% improvement in F1-measure when the label “none” is included and a 10 % improvement when the label “none” is excluded, compared to models that do not utilize contextual information. Additionally, non-numerical interpretation of results reveals key insights, allowing for the identification of errors and challenges in stance detection datasets.

关键词： Economics Deep learning Large language models Biological system modeling Encyclopedias Context awareness natural language processing Internet Online services Context modeling

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Incorporating Residual and Normalization Layers into Analysis of Masked language Models

Incorporating Residual and Normalization Layers into Analysi...

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2021 conference on empirical methods in natural language processing, EMNLP 2021

作者： Kobayashi, Goro Kuribayashi, Tatsuki Yokoi, Sho Inui, Kentaro Tohoku University Japan Langsmith Inc. RIKEN Japan

ISBN: (纸本)9781955917094

Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively analyzed. However, the Transformer architecture is not only composed of the multi-head attention;other components can also contribute to Transformers' progressive performance. In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed. These results provide new intuitive explanations of existing reports;for example, discarding the learned attention patterns tends not to adversely affect the performance. The codes of our experiments are publicly available. © 2021 Association for Computational Linguistics

关键词： Computational linguistics

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Efficient language Identification for All-language Internet News

Efficient Language Identification for All-Language Internet ...

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25th International conference on Asian language processing (IALP)

作者： Tang, Jian Chen, Xiaojiang Liu, Wuying Guangdong Univ Foreign Studies Lab Language Engn & Comp Guangzhou 510420 Peoples R China

ISBN: (纸本)9781665483117

The rapid development of language science and computing technology, especially the popularization of broadband Internet, has caused the explosion of all-language news to spread and communicate faster and faster. Among multi-modal news such as text, image, audio, and video, text news still accounts for the largest proportion of Internet news. In the face of more than 7,000 existing human languages, efficiently identifying the language of text news has become the most basic natural language processing technology, which can select accurate language processing methods for subsequent in-depth content processing and network public opinion analysis. Based on the idea of N-Gram, we designed and implemented a set of language identification methods suitable for all-language Internet news from two aspects: language training and language identification, and applied it to actual text news preprocessing. The language identification results of all-language Internet news show that our method has good recognition accuracy and efficiency.

关键词： language Identification Internet News N-Gram All-language

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Which Words Pillar the Semantic Expression of a Sentence? 35

Which Words Pillar the Semantic Expression of a Sentence?

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35th IEEE International conference on Tools with Artificial Intelligence (ICTAI)

作者： Zhang, Cheng Cao, Jingxu Yan, Dongmei Song, Dawei Lv, Jinxin Tianjin Univ Finance & Econ Tianjin Peoples R China Open Univ Milton Keynes Bucks England

ISBN: (纸本)9798350342734

In the realm of machine learning, a profound understanding of sentence semantics holds paramount importance for various applications, notably text classification. Traditionally, this comprehension has been entrusted to deep learning models, despite their computationally intensive nature, particularly when dealing with lengthy sequences. The nuanced impact of individual words within a sentence on semantic expression necessitates a strategic removal of less pertinent words to alleviate the computational burden of the model. Presently, prevailing approaches for word removal predominantly employ methods such as truncation, stop-word elimination and attention mechanisms. Regrettably, these techniques often lack a robust theoretical foundation concerning semantics and interpretability. To bridge this conceptual gap, our study introduces the concept of 'Semantic Pillar Words' (SPW) within a sentence, anchored in a Semantic Euclidean space. Here, the semantics of a word are represented as a constellation of semantic points, with a text sequence encapsulating the convex hull of these semantic points of words. We propose a novel method for Semantic Pillar Word extraction, known as 'SPW-Conv', which dynamically and interpretably prunes text segments, striving to preserve the semantic pillars inherent in the original text. Our extensive experimentation encompasses three diverse text classification datasets, revealing that SPW-Conv outperforms existing methods. Remarkably, it becomes evident that retaining less than 80% of the words within a sentence suffices to capture its semantics adequately, all while achieving classification accuracy levels comparable to those obtained using the entire original text.

关键词： natural language processing Semantic Pillar Words Convex hull

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