Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning

Meesad, Phayung

doi:10.1007/s42979-021-00775-6

Cited by 42 publications

(18 citation statements)

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“…This model has attained lower computational requirements and higher accuracy compared to conventional research works. In 2021, Meesad (2021) has suggested a new framework for reliable detection of fake news in the Thai language, which has consisted of three major modules. It has also composed of two stages like data collection stage and the building phase of the machine learning model.…”

Section: Fake News Detection Model Based On Non-english Existing Appr...mentioning

confidence: 99%

A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

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Emerging of social media creates inconsistencies in online news, which causes confusion and uncertainty for consumers while making decisions regarding purchases. On the other hand, in existing studies, there is a lack of empirical and systematic examination observed in terms of inconsistency regarding reviews. The spreading of fake news and disinformation on social media platforms has adverse effects on stability and social harmony. Fake news is often emerging and spreading on social media day by day. It results in influencing or annoying and also misleading nations or societies. Several studies aim to recognize fake news from real news on online social media platforms. Accurate and timely detection of fake news prevents the propagation of fake news. This paper aims to conduct a review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms. The fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets. Further, the dataset utilized, simulation platforms, and recorded performance metrics are evaluated as an extended review model. Finally, the survey expedites the research findings and challenges that could have significant implications for the upcoming researchers and professionals to improve the trust worthiness of automated fake news detection models.

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Section: Fake News Detection Model Based On Non-english Existing Appr...mentioning

confidence: 99%

A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

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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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“…Therefore, we evaluate eight 1 ML methods based on: Support Vector Machines (SVM) [24], Random Forest (RF) [25], Multilayer Perceptron (MLP) [26], Naive Bayes (NB) [27], Extreme Gradient Boosting (XGB) [28], and k-Nearest Neighbors (KNN) [29]. We choose these methods due to their widespread use in practical NLP classification problems [30]- [32].…”

Section: Classificationmentioning

confidence: 99%

A Text Classification Methodology to Assist a Large Technical Support System

Ohata¹,

Mattos

Gomes³

et al. 2022

IEEE Access

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Text-based tools for reporting technical issues and receiving support are widespread in commercial applications, such as customer services and internal corporate communication. Past issues recorded in such systems may provide valuable knowledge for better handling future interactions. Nevertheless, the predominance of short messages and the presence of specific domain subjects constitute additional challenges. In this work, we aim to build an assistant for a system operating in a large company that provides asynchronous services for technical support. It is known that some repetitive technical issues can be handled with simple standard messages, named templates. Thus, we propose a modular pipeline based on natural language processing and machine learning algorithms to enable raw text processing, feature extraction, and supervised learning to recommend suitable templates from a given textual description of the incoming issue. In a real-world scenario, the proposed pipeline achieved an average accuracy of 72.7%, a promising result for a setup with 9 classes and few labeled training instances. Moreover, a post hoc analysis shows how our methodology is able to correctly identify the words more closely related to the corresponding templates. INDEX TERMS natural language processing, text classification, technical support, automated assistantThis article has been accepted for publication in IEEE Access.

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Thai Fake News Detection Based on Information Retrieval, Natural Language Processing and Machine Learning

Cited by 42 publications

References 50 publications

A systematic literature review and existing challenges toward fake news detection models

A systematic literature review and existing challenges toward fake news detection models

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

A Text Classification Methodology to Assist a Large Technical Support System

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