An Ensemble Technique to Detect Fabricated News Article Using Machine Learning and Natural Language Processing Techniques

Sangamnerkar, Saarthak; Srinivasan, R.; Christhuraj, M.R.; Sukumaran, Rajeev

doi:10.1109/incet49848.2020.9154053

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…First, in the list, Sangamnerkar et al [44] address the pressing issue of fake news, exploring ensemble techniques for binary news classification. It highlights source diversity to enhance accuracy and evaluates models using metrics like accuracy, precision, and recall.…”

Section: Plos Onementioning

confidence: 99%

FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection

Keya,

Shajeeb,

Rahman

et al. 2023

PLoS ONE

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False news articles pose a serious challenge in today’s information landscape, impacting public opinion and decision-making. Efforts to counter this issue have led to research in deep learning and machine learning methods. However, a gap exists in effectively using contextual cues and skip connections within models, limiting the development of comprehensive detection systems that harness contextual information and vital data propagation. Thus, we propose a model of deep learning, FakeStack, in order to identify bogus news accurately. The model combines the power of pre-trained Bidirectional Encoder Representation of Transformers (BERT) embeddings with a deep Convolutional Neural Network (CNN) having skip convolution block and Long Short-Term Memory (LSTM). The model has been trained and tested on English fake news dataset, and various performance metrics were employed to assess its effectiveness. The results showcase the exceptional performance of FakeStack, achieving an accuracy of 99.74%, precision of 99.67%, recall of 99.80%, and F1-score of 99.74%. Our model’s performance was extended to two additional datasets. For the LIAR dataset, our accuracy reached 75.58%, while the WELFake dataset showcased an impressive accuracy of 98.25%. Comparative analysis with other baseline models, including CNN, BERT-CNN, and BERT-LSTM, further highlights the superiority of FakeStack, surpassing all models evaluated. This study underscores the potential of advanced techniques in combating the spread of false news and ensuring the dissemination of reliable information.

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Section: Plos Onementioning

confidence: 99%

FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection

Keya,

Shajeeb,

Rahman

et al. 2023

PLoS ONE

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“…Ensemble or combination of machine learning models is a technique used by researchers to deal with complex machine learning tasks. Authors in [7] analyzed different ensembles of different machine learning models and finally came with the an ensemble of Decision Tree, Logistic Regression, Bagging Classifier used with hardvoting ensemble technique which gave accuracy of 88%. For other NLP tasks, ensemble techniques give nice results.…”

Section: Imentioning

confidence: 99%

Analysis of Text Feature Extractors using Deep Learning on Fake News

Ahmed

Ghazanfar

Hussain

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Social media and easy internet access have allowed the instant sharing of news, ideas, and information on a global scale. However, rapid spread and instant access to information/news can also enable rumors or fake news to spread very easily and rapidly. In order to monitor and minimize the spread of fake news in the digital community, fake news detection using Natural Language Processing (NLP) has attracted significant attention. In NLP, different text feature extractors and word embeddings are used to process the text data. The aim of this paper is to analyze the performance of a fake news detection model based on neural networks using 3 feature extractors: TD-IDF vectorizer, Glove embeddings, and BERT embeddings. For the evaluation, multiple metrics, namely accuracy, precision, F1, recall, AUC ROC, and AUC PR were computed for each feature extractor. All the transformation techniques were fed to the deep learning model. It was found that BERT embeddings for text transformation delivered the best performance. TD-IDF has been performed far better than Glove and competed the BERT as well at some stages.

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“…In the sequential ensemble, basepredictors are trained sequentially, where a model attempts to correct its predecessor (Pham et al, 2021). Ensemble learning methods have shown good performance in various applications, including solar irradiance prediction (Lee et al, 2020), slope stability analysis (Pham et al, 2021), natural language processing (Sangamnerkar et al, 2020), malware detection (Gupta & Rani, 2020), traffic incident detection (Xiao, 2019). In the past, several studies explored machine learning models for fake news detection task in a few languages like Portuguese (Monteiro et al, 2018;Silva et al, 2020), Spanish (Posadas-Durán et al, 2019;Abonizio et al, 2020), Urdu , Arabic (Alkhair et al, 2019), Slavic (Faustini & Covões, 2020;Kapusta & Obonya, 2020), and English (Kaur, Kumar & Kumaraguru, 2020;Ozbay & Alatas, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In the past, several studies explored machine learning models for fake news detection task in a few languages like Portuguese (Monteiro et al, 2018;Silva et al, 2020), Spanish (Posadas-Durán et al, 2019;Abonizio et al, 2020), Urdu , Arabic (Alkhair et al, 2019), Slavic (Faustini & Covões, 2020;Kapusta & Obonya, 2020), and English (Kaur, Kumar & Kumaraguru, 2020;Ozbay & Alatas, 2020). As compared to machine learning, a few efforts have been made to explore ensemble learning for fake news detection like Indonesian (Al-Ash & Wibowo, 2018;Al-Ash et al, 2019), English (Kaur, Kumar & Kumaraguru, 2020;Sangamnerkar et al, 2020). Therefore, this study aims to investigate ensemble learning methods for the fake news detection task.…”

Section: Introductionmentioning

confidence: 99%

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Akhter

Zheng

Afzal

et al. 2021

PeerJ Computer Science

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The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors’ predictions to improve the fake news detection system’s overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.

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An Ensemble Technique to Detect Fabricated News Article Using Machine Learning and Natural Language Processing Techniques

Cited by 10 publications

References 9 publications

FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection

FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection

Analysis of Text Feature Extractors using Deep Learning on Fake News

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

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