Identifying multimodal misinformation leveraging novelty detection and emotion recognition.

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rina Kumari, Nischal Ashok, Pawan Kumar Agrawal, Tirthankar Ghosal, Asif Ekbal
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引用次数: 3

Abstract

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.

Abstract Image

Abstract Image

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利用新颖性检测和情绪识别识别多模式错误信息。
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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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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