Text-mining-based Fake News Detection Using Ensemble Methods

Reddy, Harita; Raj, Namratha; Gala, Manali; Annappa, B.

doi:10.1007/s11633-019-1216-5

Cited by 91 publications

(43 citation statements)

References 27 publications

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“…Many have proposed a solution to the fake news detection problem using hand-crafted feature engineering and applying Machine Learning models like Naïve Bayes, Random Forest, Boosting, Support Vector Machine, Decision Tree, Logistic Regression. Reddy et al (2020) reported an accuracy of 95% with a gradient boosting algorithm on the combination of stylometric and CBOW (Word2Vec) features. Bali et al (2019) used Sentiment polarity, Readability, Count of words, Word Embedding, and Cosine similarity as the features to discriminate fake news with machine learning models.…”

Section: Resultsmentioning

confidence: 99%

Stance detection with BERT embeddings for credibility analysis of information on social media

Karande

Walambe

Benjamin

et al. 2021

PeerJ Computer Science

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The evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers the societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e., when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.

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Section: Resultsmentioning

confidence: 99%

Stance detection with BERT embeddings for credibility analysis of information on social media

Karande

Walambe

Benjamin

et al. 2021

PeerJ Computer Science

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“…In this paper, we focus on content-based models that enable early fake news detection before the spread of fake news. Recent content-based fake news detection models (Reddy et al, 2020;Shu et al, 2019;Zhang et al, 2020) generally formulates the problem as a text classification problem. However, many news articles are very long, which hinders the application of state-of-the-art pre-trained language models such as BERT (Devlin et al, 2018) because their maximum context length is generally 512.…”

Section: Related Workmentioning

confidence: 99%

Connecting the Dots Between Fact Verification and Fake News Detection

Li¹,

Zhou²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Fact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such as FEVER. However, the challenging problem of fake news detection has not benefited from the improvement of fact verification models, which is closely related to fake news detection. In this paper, we propose a simple yet effective approach to connect the dots between fact verification and fake news detection. Our approach first employs a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. Then we use a fact verification model pre-trained on the FEVER dataset to detect whether the input news article is real or fake. Our approach makes use of the recent success of fact verification models and enables zero-shot fake news detection, alleviating the need of large scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demonstrate the effectiveness of our proposed approach.

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“…For example, one study introduced a method called Event Adversarial Neural Network (EANN) that extracts features from multi-modal data and used both textual and visual features to detect fake news 24 . In another work, used sentiment analysis in twitter posts for rumor and fake news detection 4 , or in the other work used combined stylometric features with word vector representations to predict fake news 6 .…”

Section: Related Workmentioning

confidence: 99%

“…The final dataset, called Fake News Inference Dataset (FNID) 51 , is publicly available for future research 6 . Some statistics of this dataset are presented in Table 3.…”

Section: Preprocessingmentioning

confidence: 99%

“…The features used in these methods may fall into two categories: 1) content-based features, and 2) context-based features. Content-based features refer to those features that are extracted from the text or the content of the news itself [4][5][6] . In contrast, context-based features are based on news context such as the publisher, the stance of other individuals in the network, and propagation structure to indicate whether the news is fake or not.…”

Section: Introductionmentioning

confidence: 99%

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Fake News Detection on Social Media Using A Natural Language Inference Approach

Sadeghi

Bidgoly

Amirkhani

2020

Preprint

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Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

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Text-mining-based Fake News Detection Using Ensemble Methods

Cited by 91 publications

References 27 publications

Stance detection with BERT embeddings for credibility analysis of information on social media

Stance detection with BERT embeddings for credibility analysis of information on social media

Connecting the Dots Between Fact Verification and Fake News Detection

Fake News Detection on Social Media Using A Natural Language Inference Approach

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