CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection

Gupta, Shashank; Thirukovalluru, Raghuveer; Sinha, Manjira; Mannarswamy, Sandya

doi:10.1109/asonam.2018.8508408

Cited by 35 publications

(10 citation statements)

References 25 publications

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“…Jin et al [23] build a stance graph based on user posts and detect fake news by mining the stance correlations within a graph optimization framework. By exploring relationships among news articles, publishers, users (spreaders), and user posts, PageRank-like algorithm [18], matrix and tensor factorization [19,51], or RNN [47,63] have been developed for fake news detection.…”

Section: Propagation-based Fake News Detectionmentioning

confidence: 99%

“…Unlike content-based fake news detection, propagation-based fake news detection aims to detect fake news by exploring how news propagates on social networks. Propagation-based methods have gained recent popularity where novel models have been proposed exhibiting reasonable performance [7,19,23,47,51,63,66]. However, propagation-based methods face a major challenge when detecting fake news.…”

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confidence: 99%

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Fake News Early Detection

et al. 2020

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Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theorydriven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Natural language processing; Machine learning; • Security and privacy → Social aspects of security and privacy; • Applied computing → Sociology; Computer forensics;

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Section: Propagation-based Fake News Detectionmentioning

confidence: 99%

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confidence: 99%

Fake News Early Detection

et al. 2020

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“…[4] Proposed a novel ML fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 4.8%. Second, they implemented method within a Facebook Messenger Chabot and validate it with a real-world application, obtaining a fake news detection accuracy of 81.7% [5] The authors describe a system in which they exploit the echo chamber communities i.e. the communities that share the same belief that exist in the social media.…”

Section: Literature Survey [1]mentioning

confidence: 99%

A Survey Paper on Fake News Detection Techniques

Babanne¹,

Thakur²,

Shinde³

2018

International Journal of Advanced Research in Computer and Comm

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Fake news is counterfeit information which is mostly not true or layered. It has been around for a long time and with the coming of online life and cutting edge reporting at its pinnacle, the discovery of fake news has been a well-known point in the exploration network. There are various difficulties associated with the detection of deceptive news. This paper describes and compares various fake news detection systems, techniques and approaches and gives comparative analysis on them.

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“…In real-world scenarios, however, it is more accurate to represent data in tensor form. As a result, the tensor representation of data has become a new research direction in the field of data mining and machine learning [1][2][3][4][5]. Recent years has seen a growing attention being paid to tensor representation and its application in fields like image classification, face recognition, scene classification and bioinformatics [6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Target class(1,4), Teat accuracy Target class(5,6), Teat accuracy Target class(3,15), Teat accuracy Target class(6,12), Teat accuracyFigures 2-…”

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A Nonlinear Tensor-Based Machine Learning Algorithm for Image Classification

Wang¹,

Chen²

2019

RIA

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In recent year, the tensor theory has been frequently incorporated to machine learning, because of the various advantages of tensor-based machine learning over vector-based machining learning: the ability to preserve the spatiotemporal information, allowing full utilization of the data, and the suitability for solving high-dimensional problems with a small sample size. Considering the suitability of tensor algorithm for classical high-dimensional, small-sample problems, this paper probes into the nonlinear classification problem with tensor representation, and designs a tensor-based nonlinear classification algorithm, namely, the kernel-based STM (KSTM). The maximum margin principle was adopted for the classification by the KSTM: the two types of samples are separated by the decision hyperplane as far as possible in the tensor space. Through numerical experiments, it is proved that the KSTM achieved better classification accuracy than the linear method, especially for the highdimensional problem with a small sample size.

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CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection

Abstract: Detecting whether a

Cited by 35 publications

References 25 publications

Fake News Early Detection

Fake News Early Detection

A Survey Paper on Fake News Detection Techniques

A Nonlinear Tensor-Based Machine Learning Algorithm for Image Classification

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