The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of Corona

Englmeier, Karl‐Hans

doi:10.1016/j.procs.2021.01.115

Cited by 9 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fake news is spreading across all spheres: science [32][33][34]; art [35,36]; finance [37][38][39]; marketing [40][41][42]; politics [43][44][45]; security [46][47][48]; defense [49,50]; civil protection [51][52][53]; and health [54][55][56][57], including recent sensitive topics such as vaccines [58,59] and COVID-19 [59][60][61][62]. "Fake news" was even named the word of the decade for 2011-2020 by the Macquarie dictionary in 2021 (https://www.macquariedictionary.com.au/blog/article/780/, accessed on 3 June 2021) and Word of the Year 2017 by both the American Dialect Society and Collins English Dictionary (https://en.wikipedia.org/wiki/Word_of_the_year, accessed on 3 June 2021).…”

Section: Fake Newsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

View full text Add to dashboard Cite

In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

show abstract

Section: Fake Newsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Indeed, with the fast spreading of online information, checking manually Fake News becomes an ineffective approach (not obvious, difficult, takes a long time). Therefore, automated Fake News detection based on Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning (DL) present an efficient, accurate and fast solution to support and improve manual methods [15].…”

Section: B Automatic Fake News Detection Based On Deep Neural Network (Dln)mentioning

confidence: 99%

Automatic Fake News Detection based on Deep Learning, FastText and News Title

Taher¹,

Moussaoui²,

Moussaoui³

2022

IJACSA

View full text Add to dashboard Cite

As a range of daily phenomena, Fake News is quickly becoming a longstanding issue affecting individuals, public and private sectors. This major challenge of the connected and modern world can cause many severe and real damages such as manipulating public opinion, damaging reputations, contributing to the loss in stock market value and representing many risks to the global health. With the fast spreading of online misinformation, checking manually Fake News becomes ineffective solution (not obvious, difficult and takes a long time). The improvement of Deep Learning Networks (DLN) can support with high degree of accuracy and efficiency the classical processes of Fake News spotting. One of the keys improvement strategies are optimizing the Word Embedding Layer (WEL) and finding relevant Fake News predicting features. In this context, and based on six DLN architectures, FastText process as WEL and Inverted Pyramid as News Articles Pattern (IPP), the present paper focuses on the assessment of the first news article feature that is hypothesized as affecting the performances of fake news predicting: News Title. By assessing the impact that the Embedding Vector Size (EVS), Window Size (WS) and Minimum Frequency of Words (MFW) in News Titles corpus can have on DLN, the experiments carried out in this paper showed that the News Title feature and FastText process can have a significant improvement on DLN fake news detection with accuracy rates exceeding 98%.

show abstract

“…Englmeier et al have developed the prototype of contexter system to check false news and wrong information. e model can present the blueprint of facts, abstract representation of facts, and look for similar facts, so as to avoid the threat to society and economy caused by wrong information (Englmeier) [14]. Wang et al built an urban real estate early warning model based on multi-classification support vector mechanism, analyzed the real estate market from all aspects by selecting early warning index data, and the results successfully predicted the market operation of the next year, providing an accurate and reliable method for market early warning (Wang et al) [15].…”

Section: Related Workmentioning

confidence: 99%

“…When text keywords and feature vectors are obtained, a news item is randomly selected. Select the 10 news items with the highest similarity through formula (14), and calculate the probability that the 10 news items and the selected news items are in the same category. In this experiment, 1000 samples are randomly selected and the average probability is taken.…”

Section: Experimental Design and Analysismentioning

confidence: 99%

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the rapid development of the information technology, mining news content efficiently is a difficult problem faced by the government and enterprises. The classification, clustering, and prediction of neural network are used in news mining. A keyword based on neural network and word vector are proposed as a text feature model, and the model is compared with other neural network models. This paper studies the vectorization of text for similarity recommendation and introduces two models: word vector model and text vector model doc2vec based on neural network. In the model with word vector as feature vector, the recommendation accuracy is about 75.35%, doc2vec model is about 44.5%, and the recommendation accuracy of the model with keyword as text is about 88.61%. The recommendation accuracy is about 91%, and the performance has greatly improved. The more the number of keywords, the better the recommendation effect of the model. When the number is about 20, the improvement of the recommendation effect tends to be gentle, and the continuous increase of the number of keywords will increase the operation time of the model. It is proposed that the keyword and word vector based on neural network together as the text vector model can more accurately mine news data, quickly obtain news information, and make prediction and early warning for many industries such as the real estate industry.

show abstract

The Role of Text Mining in Mitigating the Threats from Fake News and Misinformation in Times of Corona

Cited by 9 publications

References 17 publications

Sentiment Analysis for Fake News Detection

Sentiment Analysis for Fake News Detection

Automatic Fake News Detection based on Deep Learning, FastText and News Title

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Contact Info

Product

Resources

About