A Memory Network Information Retrieval Model for Identification of News Misinformation

Ebadi, Nima; Jozani, Mohsen; Choo, Kim‐Kwang Raymond; Rad, Paul

doi:10.1109/tbdata.2020.3048961

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…The use of recurrent neural networks in this area is quite intensive ( [18] [19]), but most of these solutions are based on the use of truncated text inputs due to efficiency issues, with the consequent loss of information. This problem has been faced by using different approaches as in [20], where authors apply a two-stage stance detection process based on a simple information retrieval process that is able to use full articles, providing a a high accuracy following a real-life process of fact-checking.…”

Section: State-of-the-art and Contributionsmentioning

confidence: 99%

Conspiracy or Not? A Deep Learning Approach to Spot It on Twitter

Galende¹,

Hernández-Peñaloza²,

Uribe³

et al. 2022

IEEE Access

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Sentiment analysis is an active topic in Natural Language Processing (NLP). It has attracted a significant interest of research community due to the wide range of applications, including social-media, fake news spotting and interactive applications. In this paper, we present a novel approach for semiautomatic background creation and conspiracy classification. For this purpose, a complete framework including novel recurrent models is proposed. The BORJIS: Best algorithm foR Joint conspiracy and sarcasm detection has been tested on twitter-crawled data and It is composed by: (a) the crawler and labelling module, (b) the features vector extraction and (c) the conspiracy classifier. BORJIS is established as a novel approach for processing variable length inputs to detect conspiracy. Both the data and the code are referenced in the article.

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Section: State-of-the-art and Contributionsmentioning

confidence: 99%

Conspiracy or Not? A Deep Learning Approach to Spot It on Twitter

Galende¹,

Hernández-Peñaloza²,

Uribe³

et al. 2022

IEEE Access

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“…Fact-checking websites examine the news source to check the authenticity and accuracy of the online news [16]. Real-life fact-checking websites and fact verification datasets offered practical solutions to display the originality of the web-based news [19,44]. Automatic fake news detectors were highly instrumental in the war against digital fake news [17].…”

Section: Fact-checking Sitesmentioning

confidence: 99%

“…Deep learning models and architectures, neural networks, and natural language processing facilitate in detecting fake news for stopping pernicious news on digital media [6,16,23]. Classification-based models, blockchain-based frameworks, machine learning, big data architectures, machine learning ensemble approach, and natural language processing technology are trending techniques for fake news prevention [5,7,13,19,24,29,38]. Machine learning, deep learning methods, and real-world datasets are a productive source to find out fake news from the flood of misinformation [31,32].…”

Section: Neural Networkmentioning

confidence: 99%

“…Big data analytics are a trending practice in the battle against fake news and to identify meaningful information [17,18]. Big data are useful to detect fake information [19] because traditional methods to identify correct information are not sufficient due to the volume and speed of false news in digital media [20]. Big data analytics assist in finding out correct information speedily from large stored data and reducing the harms of fake news being circulated on digital media [21].…”

Section: Introductionmentioning

confidence: 99%

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A Scoping Review of the Relationship of Big Data Analytics with Context-Based Fake News Detection on Digital Media in Data Age

et al. 2022

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The objectives of the study were to identify the relationship between big data analytics with context-based news detection on digital media in the data age, to find out the trending approaches to detect fake news on digital media, and to explore the challenges for constructing quality big data to detect misinformation on social media. Scoping review methodology was applied to carry out a content analysis of 42 peer-reviewed research papers published in 10 world-leading digital databases. Findings revealed a strong positive correlation between quality big data analytics and fake news detection on digital media. Additionally, it was found that artificial intelligence, fact-checking sites, neural networks, and new media literacy are trending techniques to identify correct information in the age of misinformation. Moreover, results manifested that hidden agenda, the volume of fake information on digital media, massive unstructured data, the fast spread of fake news on digital media, and fake user accounts are prevalent challenges to construct authentic big data for detecting false online information on digital media platforms. Theoretically, the study has added valuable literature to the existing body of knowledge by exploring the relationship between big data analytics and context-based fake news on digital media in the data age. This intellectual piece also contributes socially by offering practical recommendations to control the cancer of fake news in society for stopping horrific perils; hence, it has a societal impact. Current research has practical applications for generators of digital media applications, policy-makers, decision-takers, government representatives, civil societies, higher education bodies, media workforce, educationists, and all other stakeholders. Recommendations offered in the paper are a roadmap for framing impactful policies to stay away from the harms of fake digital news.

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“…Algorithmic misinformation detection can be composed of many sub-tasks, which some systems tackle independently while others attempt to solve in an end-to-end fashion. While the specifics of these tasks may evolve and change over time, we draw from Guo et al [34] to differentiate between three core (sequential) tasks: (1) Check-worthiness, which aims to spot factual claims that are worthy of fact-checking [11,31,39,45], (2) Evidence retrieval of potential evidence for identified claims [21,49,56,60,66,70,74] , and (3) verdict prediction, which aims to establish the veracity of a claim [60,63,74]. In a survey on the topic by Zhou and Zafarani [78], the authors identify how misinformation can be detected from four perspectives: (1) the false knowledge it carries; (2) its writing style; (3) its propagation patterns; and (4) the credibility of its source.…”

Section: Algorithmic Misinformation Detectionmentioning

confidence: 99%

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

Neumann,

De-Arteaga,

Fazelpour

2022

Preprint

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Faced with the scale and surge of misinformation on social media, many platforms and fact-checking organizations have turned to algorithms for automating key parts of misinformation detection pipelines. While offering a promising solution to the challenge of scale, the ethical and societal risks associated with algorithmic misinformation detection are not well-understood. In this paper, we employ and extend upon the notion of informational justice to develop a framework for explicating issues of justice relating to representation, participation, distribution of benefits and burdens, and credibility in the misinformation detection pipeline. Drawing on the framework: (1) we show how injustices materialize for stakeholders across three algorithmic stages in the pipeline; (2) we suggest empirical measures for assessing these injustices; and (3) we identify potential sources of these harms. This framework should help researchers, policymakers, and practitioners reason about potential harms or risks associated with these algorithms and provide conceptual guidance for the design of algorithmic fairness audits in this domain.

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A Memory Network Information Retrieval Model for Identification of News Misinformation

Cited by 15 publications

References 20 publications

Conspiracy or Not? A Deep Learning Approach to Spot It on Twitter

Conspiracy or Not? A Deep Learning Approach to Spot It on Twitter

A Scoping Review of the Relationship of Big Data Analytics with Context-Based Fake News Detection on Digital Media in Data Age

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

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