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Journal of Intelligent Information Systems最新文献

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Identifying multimodal misinformation leveraging novelty detection and emotion recognition. 利用新颖性检测和情绪识别识别多模式错误信息。
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-06-06 DOI: 10.1007/s10844-023-00789-x
Rina Kumari, Nischal Ashok, Pawan Kumar Agrawal, Tirthankar Ghosal, Asif Ekbal

With the growing presence of multimodal content on the web, a specific category of fake news is rampant on popular social media outlets. In this category of fake online information, real multimedia contents (images, videos) are used in different but related contexts with manipulated texts to mislead the readers. The presence of seemingly non-manipulated multimedia content reinforces the belief in the associated fabricated textual content. Detecting this category of misleading multimedia fake news is almost impossible without relevance to any prior knowledge. In addition to this, the presence of highly novel and emotion-invoking contents can fuel the rapid dissemination of such fake news. To counter this problem, in this paper, we first introduce a novel multimodal fake news dataset that includes background knowledge (from authenticate sources) of the misleading articles. Second, we design a multimodal framework using Supervised Contrastive Learning (SCL) based novelty detection and Emotion Prediction tasks for fake news detection. We perform extensive experiments to reveal that our proposed model outperforms the state-of-the-art (SOTA) models.

随着网络上多模式内容的日益增多，一类特定的假新闻在流行的社交媒体上泛滥。在这类虚假网络信息中，真实的多媒体内容（图像、视频）被用于不同但相关的上下文中，并被操纵文本以误导读者。看似未被操纵的多媒体内容的存在强化了人们对相关捏造文本内容的信念。如果没有任何先验知识，几乎不可能检测出这类误导性的多媒体假新闻。除此之外，高度新颖和情绪化的内容的出现会助长此类假新闻的快速传播。为了解决这个问题，在本文中，我们首先介绍了一个新的多模式假新闻数据集，该数据集包括误导性文章的背景知识（来自认证来源）。其次，我们使用基于监督对比学习（SCL）的新颖性检测和情绪预测任务设计了一个用于假新闻检测的多模式框架。我们进行了大量的实验，以表明我们提出的模型优于最先进的（SOTA）模型。
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引用次数: 3
A semiautomatic method for obtaining a predictive deep learning model and a rule-based system for abdominal aortic aneurysms 一种获得预测深度学习模型的半自动方法和基于规则的腹主动脉瘤系统
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-06-05 DOI: 10.1007/s10844-023-00781-5
Alberto Nogales, Fernando Gallardo, Miguel Pajares, Javier Martinez Gamez, José Moreno, Á. García-Tejedor
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引用次数: 0
A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method 一种基于SMOTE Tomek方法的基于CNN和GRU的软件缺陷预测新方法
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-05-16 DOI: 10.1007/s10844-023-00793-1
N. A. A. Khleel, K. Nehéz
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引用次数: 3
BERT-PG: a two-branch associative feature gated filtering network for aspect sentiment classification 面向方面情感分类的双分支关联特征门控过滤网络
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-05-16 DOI: 10.1007/s10844-023-00785-1
Jiamei Wang, Wei Wu, Jiansi Ren
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引用次数: 1
Multilingual deep learning framework for fake news detection using capsule neural network. 使用胶囊神经网络检测假新闻的多语言深度学习框架。
IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-05-09 DOI: 10.1007/s10844-023-00788-y
Rami Mohawesh, Sumbal Maqsood, Qutaibah Althebyan

Fake news detection is an essential task; however, the complexity of several languages makes fake news detection challenging. It requires drawing many conclusions about the numerous people involved to comprehend the logic behind some fake stories. Existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge these challenges and deal with multilingual fake news detection, we present a semantic approach to the identification of fake news based on relational variables like sentiment, entities, or facts that may be directly derived from the text. Our model outperformed the state-of-the-art methods by approximately 3.97% for English to English, 1.41% for English to Hindi, 5.47% for English to Indonesian, 2.18% for English to Swahili, and 2.88% for English to Vietnamese language reviews on TALLIP fake news dataset. To the best of our knowledge, our paper is the first study that uses a capsule neural network for multilingual fake news detection.

假新闻检测是一项重要任务；然而，多种语言的复杂性使得假新闻检测具有挑战性。要理解一些虚假故事背后的逻辑，需要对众多参与者得出许多结论。现有的作品无法从特定的多语言文本语料库中的文档中收集更多的语义和上下文特征。为了克服这些挑战并处理多语言假新闻检测，我们提出了一种基于关系变量（如情绪、实体或可能直接从文本中得出的事实）识别假新闻的语义方法。在TALLIP假新闻数据集上，我们的模型在英语到英语、英语到印地语、英语到印尼语、英语和斯瓦希里语的评论中分别比最先进的方法高出约3.97%、1.41%、5.47%、2.18%和2.88%。据我们所知，我们的论文是第一个使用胶囊神经网络进行多语言假新闻检测的研究。
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引用次数: 0
The more "similar" the happier: Augmenting text using similarity scoring with neural embeddings for happiness classification 越“相似”越快乐:使用相似度评分和神经嵌入来增加文本的快乐分类
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-05-02 DOI: 10.1007/s10844-023-00791-3
Yong Kuan Shyang, Jasy Suet Yan Liew
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引用次数: 0
Multi-perspective enriched instance graphs for next activity prediction through graph neural network 基于图神经网络的多视角丰富实例图下一个活动预测
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-05-01 DOI: 10.1007/s10844-023-00777-1
Andrea Chiorrini, C. Diamantini, Laura Genga, D. Potena
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引用次数: 2
Offensive language identification with multi-task learning 多任务学习下的攻击性语言识别
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-04-29 DOI: 10.1007/s10844-023-00787-z
Marcos Zampieri, Tharindu Ranasinghe, Diptanu Sarkar, Alex Ororbia
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引用次数: 0
Transformer-based Denoising Adversarial Variational Entity Resolution 基于变换的去噪对抗变分实体分解
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-04-17 DOI: 10.1007/s10844-022-00773-x
Shuaichao Li, Huaiguang Wu
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引用次数: 0
Bi-knowledge views recommendation based on user-oriented contrastive learning 基于面向用户的对比学习的双知识视图推荐
IF 3.4 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal of Intelligent Information Systems
Pub Date : 2023-04-13 DOI: 10.1007/s10844-023-00778-0
Yi Liu, Hongrui Xuan, Bohan Li
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引用次数: 0
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