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Kumar, Aniket, Saurabh Kumar Pal, Kumar Dhruv Roy, and Mr Ragunthar T. "Fake News Detection." International Journal of Scientific & Engineering Research 11, no. 12 (December 25, 2020): 575–80. http://dx.doi.org/10.14299/ijser.2020.12.09.
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Now-a-days it's exceedingly common in this digital world that someone for his or her benefit try to manipulate a mass with false information. With the massive use of social media by the population which is beneficial for the users most of the time, can also be used as a really good platform to spread a fake news and at worse try to create chaos in society. Fake death news of celebrities, fake news regarding wars and fake news related to politics are the day-to-day life examples.
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Chu, Samuel Kai Wah, Runbin Xie, and Yanshu Wang. "Cross-Language Fake News Detection." Data and Information Management 5, no. 1 (November 20, 2020): 100–109. http://dx.doi.org/10.2478/dim-2020-0025.
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AbstractWith increasing globalization, news from different countries, and even in different languages, has become readily available and has become a way for many people to learn about other cultures. As people around the world become more reliant on social media, the impact of fake news on public society also increases. However, most of the fake news detection research focuses only on English. In this work, we compared the difference between textual features of different languages (Chinese and English) and their effect on detecting fake news. We also explored the cross-language transmissibility of fake news detection models. We found that Chinese textual features in fake news are more complex compared with English textual features. Our results also illustrated that the bidirectional encoder representations from transformers (BERT) model outperformed other algorithms for within-language data sets. As for detection in cross-language data sets, our findings demonstrated that fake news monitoring across languages is potentially feasible, while models trained with data from a more inclusive language would perform better in cross-language detection.
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Karnyoto, Andrea, Chengjie Sun, Bingquan Liu, and Xiaolong Wang. "Transfer learning and GRU-CRF augmentation for COVID-19 fake news detection." Computer Science and Information Systems, no. 00 (2021): 53. http://dx.doi.org/10.2298/csis210501053k.
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The spread of fake news on online media is very dangerous and can lead to casualties, effects on psychology, character assassination, elections for political parties, and state chaos. Fake news that concerning Covid-19 massively spread during the pandemic. Detecting misinformation on the Internet is an essential and challenging task since humans face difficulty detecting fake news. We applied BERT and GPT2 as pre-trained using the BiGRU-Att-CapsuleNet model and BiGRU-CRF features augmentation to solve Fake News detection in Constraint @ AAAI2021 - COVID19 Fake News Detection in English Dataset. This research proved that our hybrid model with augmentation got better accuracy compared to our baseline model. It also showed that BERT gave a better result than GPT2 in all models; the highest accuracy we achieved for BERT is 0.9196, and GPT2 is 0.8986.
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Segura-Bedmar, Isabel, and Santiago Alonso-Bartolome. "Multimodal Fake News Detection." Information 13, no. 6 (June 2, 2022): 284. http://dx.doi.org/10.3390/info13060284.
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Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection.
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Nagalakshmi, E. V., E. Sai Vineeth, Y. Goutham, and T. Vamshi Krishna. "Fake News Detection using Machine Learning - A Working Model of Fake News Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1540–48. http://dx.doi.org/10.22214/ijraset.2023.51637.
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Abstract: This project aims to address the pressing issue of fake news, which has become increasingly prevalent in today's society. With the internet and social media making news more accessible than ever, the spread of fake news can have a significant impact on social, economic, and political environments. In response to this challenge, this project investigates the use of machine learning algorithms to accurately classify news as real or fake. The project utilizes KNN, Decision Tree, and Logistic Regression algorithms to analyze large datasets of news articles and learn the patterns and characteristics of real and fake news. The primary objective of this project is to provide users with a tool that can accurately detect fake news and help prevent its spread
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Oyeniyi, Samuel A., and Joseph A. Ojeniyi. "DEVELOPMENT OF A CONCEPTUAL FRAMEWORK AND A MEASUREMENT MODEL FOR THE DETECTION OF FAKE NEWS." International Journal of Innovative Research in Advanced Engineering 8, no. 7 (July 30, 2021): 138–47. http://dx.doi.org/10.26562/ijirae.2021.v0807.001.
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Fake news has been there since before the advent of the Internet. It has had an immense impact on our modern society. Detecting fake news is an important step. Although there are various ways and methods in which fake news can be detected and solved. In this research paper we discuss the various conceptual frameworks and how they affect fake news. It further shows the development of the conceptual framework and the measurement model used; showing which of the frameworks fake news is most likely to surface through. The objective of the research is to design a conceptual framework for fake news detection, whereby developing measurement model for fake news detection, and the framework and model are evaluated for fake news detection. Fake news detection approaches can be divided as: creator and user features, news content features and social context features. A survey was taken based on this feature via questionnaire to determine in which feature, fake news can be quickly spotted. Results: Results shows that fake news can be easily spotted in the creator and user feature, this feature was then used to perform a feature selection on a fake news dataset which gave better accuracy.
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Sharma, Udit. "Fake News Detection Using ML." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3668–74. http://dx.doi.org/10.22214/ijraset.2021.37209.
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Fake news is depicted as a story that is made up with an aim to mislead or to swindle the peruser. We have introduced a reaction for the undertaking of phony news disclosure by utilizing Deep Learning structures. Because of various number of instances of phony news the outcome has been an augmentation in the in the spread of phony news. Due to the wide impacts of the immense onsets of phony news, people are conflicting if not by huge helpless finders of phony news. The most liked of such exercises consolidate "boycotts" of sources and producers that are not trustworthy. While these instruments are used to make an inexorably unique complete beginning to end plan, we need to address continuously inconvenient situations where logically strong sources and makers discharge fake news. As, the objective of this endeavor was to make a mechanical assembly for perceiving the language designs that portray phony and confirmed news using AI, AI and customary language getting ready techniques. The consequences of this undertaking exhibit the breaking point with respect to AI and AI to be huge. We have developed a model that gets numerous no of normal indications of veritable and phony news and additionally an application that aides in the portrayal of the order decision.
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Naik, Samrudhi. "Fake News Detection Using NLP." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 2022–31. http://dx.doi.org/10.22214/ijraset.2021.39582.
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Abstract: The spreading of fake news has given rise to many problems in society. It is due to its ability to cause a lot of social and national damage with destructive impacts. Sometimes it gets very difficult to know if the news is genuine or fake. Therefore it is very important to detect if the news is fake or not. "Fake News" is a term used to represent fabricated news or propaganda comprising misinformation communicated through traditional media channels like print, and television as well as nontraditional media channels like social media. Techniques of NLP and Machine learning can be used to create models which can help to detect fake news. In this paper we have presented six LSTM models using the techniques of NLP and ML. The datasets in comma-separated values format, pertaining to political domain were used in the project. The different attributes like the title and text of the news headline/article were used to perform the fake news detection. The results showed that the proposed solution performs well in terms of providing an output with good accuracy, precision and recall. The performance analysis made between all the models showed that the models which have used GloVe and Word2vec method work better than the models using TF-IDF. Further, a larger dataset for better output and also other factors such as the author ,publisher of the news can be used to determine the credibility of the news. Also, further research can also be done on images, videos, images containing text which can help in improving the models in future. Keywords: Fake news detection, LSTM(long short term memory),Word2Vec,TF-IDF,Natural Language Processing.
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Zhou, Xinyi, Atishay Jain, Vir V. Phoha, and Reza Zafarani. "Fake News Early Detection." Digital Threats: Research and Practice 1, no. 2 (July 9, 2020): 1–25. http://dx.doi.org/10.1145/3377478.
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Shu, Kai, Deepak Mahudeswaran, Suhang Wang, and Huan Liu. "Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 626–37. http://dx.doi.org/10.1609/icwsm.v14i1.7329.
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Consuming news from social media is becoming increasingly popular. However, social media also enables the wide dissemination of fake news. Because of the detrimental effects of fake news, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection.In an attempt to understand the correlations between news propagation networks and fake news, first, we build hierarchical propagation networks for fake news and true news pieces; second, we perform a comparative analysis of the propagation network features from structural, temporal, and linguistic perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature importance analysis. We conduct extensive experiments on real-world datasets and demonstrate the proposed features can significantly outperform state-of-the-art fake news detection methods by at least 1.7% with an average F1>0.84. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.
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Fayez, Eslam, Amal Elsayed Aboutabl, and Sarah N. Abdulkader. "Automated detection of fake news." International Journal of Informatics and Communication Technology (IJ-ICT) 12, no. 1 (April 1, 2023): 79. http://dx.doi.org/10.11591/ijict.v12i1.pp79-84.
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During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.
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Panigrahi, Prof Sipra, Akash Kumar Rai, Akhil Kumar Rajput, and Ayush Bhardwaj. "Fake News Detection Using Blockchain." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 2442–45. http://dx.doi.org/10.22214/ijraset.2022.41132.
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Abstract: Due to fast growth in the data day by day it’s a very difficult task to find out original information from the content. Social media helps us a lot to get information and deliver us on time. As people are more habituated towardssocial media and find the news from different resources sometimes the fake news also impacts people a lot in their day-to-day life. Blockchain technology helps people to get the proper information in various sectors like as food sector, fashion world, supply chain, as well as banking sectors. The broadcast and transparent nature of blockchain can help in the above sector enhance the technology as well as helps to detect thefact news in the current situation. In this paper, we gave acomplete idea about the blockchain technology methods andtechniques used widely in fake news detection. We can acquire this technique by combining and modifying the blockchain technique by applying the Text Mining (TM) algorithm. The above research paper talks about a brief research method on blockchain technology, It’s an outcome of testing data that clearly defines and represents the importance of the blockchainmethod in the implementation technique. Here the main goal of the paper is to find a security system ledger. Keywords: Component; fake news detection; text mining;blockchain; detection algorithms words;
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Srivastava, Anurag. "Fake News Classification Using Outliner Detection and Trend Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3452–54. http://dx.doi.org/10.22214/ijraset.2021.37123.
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In modern era fake news is one of the major causes for disrupted social harmony, impact of fake news can lead to various unforeseen situations and thus affect the society as a whole. This paper proposed the use of anomaly detection and trend analysis for detecting fake news.
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Reddy, Vookanti Anurag, CH Vamsidhar Reddy, and Dr R. Lakshminarayanan. "Fake News Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 227–30. http://dx.doi.org/10.22214/ijraset.2022.41124.
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Abstract: This Project comes up with the applications of NLP (Natural Language Processing) techniques for detecting the 'fake news', that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they’re used in other articles in your dataset) can only get you so far. But these models do not consider the important qualities like word ordering and context. It is very possible that two articles that are similar in their word count will be completely different in their meaning. The data science community has responded by taking actions against the problem. There is a Kaggle competition called as the “Fake News Challenge” and Facebook is employing AI to filter fake news stories out of users’ feeds. Combatting the fake news is a classic text classification project with a straight forward proposition. Is it possible for you to build a model that can differentiate between “Real “news and “Fake” news? So a proposed work on assembling a dataset of both fake and real news and employ a Naive Bayes classifier in order to create a model to classify an article into fake or real based on its words and phrases
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Hansrajh, Arvin, Timothy T. Adeliyi, and Jeanette Wing. "Detection of Online Fake News Using Blending Ensemble Learning." Scientific Programming 2021 (July 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/3434458.
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The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.
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Chen, Xin, Shancheng Fang, Zhendong Mao, and Yongdong Zhang. "A data-driven model for social media fake news detection." JUSTC 52, no. 3 (2022): 7. http://dx.doi.org/10.52396/justc-2021-0215.
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The rapid development of social media leads to the spread of a large amount of false news, which not only affects people’s daily life but also harms the credibility of social media platforms. Therefore, detecting Chinese fake news is a challenging and meaningful task. However, existing fake news datasets from Chinese social media platforms have a relatively small amount of data and data collection in this field is relatively old, thus being unable to meet the requirements of further research. In consideration of this background, we release a new Chinese Weibo Fake News dataset, which contains 26320 fake news data collected from Weibo. In addition, we propose a fake news detection model based on data augmentation that can effectively solve the problem of a lack of fake news, and we improve the generalization ability and robustness of the model. We conduct numerous experiments on our Chinese Weibo Fake News dataset and successfully deploy the model on the web page. The experimental performance proves the effectiveness of the proposed end-to-end model for detecting fake news on social media platforms.
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Khan, Nazakat Farooq, and Ankur Gupta. "Fake News Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1353–60. http://dx.doi.org/10.22214/ijraset.2022.46838.
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Abstract: Social media news may be a double-edged sword. There are a number of benefits to utilizing it: It's simple to use, takes little time, and is user-friendly. It's also simple to share socially significant data with others. On the other hand, a number of social networking sites adapt the news based on personal opinions and interests. This sort of misinformation is spread over social media with the intent of causing harm to a person, organization, or institution. Because of the prevalence of fake news, computer tools are needed to detect it. Fake news detection aims to aid users in spotting various sorts of fake news. We can tell if the news is genuine or created if we have encountered fake or authentic news before. We may use a number of models to understand social media news. This is a donation in two ways. We must first give datasets containing both fake and accurate news and conduct multiple experiments before developing a false news detector. Various machine learning techniques are used to categorize the data. Random Forest, Logistic Regression, Naives Bayes, Gradient Boost and Decision Tree techniques are used and compared. It was found that Gradient Boost has the best accuracy.
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Tsai, Chih-Ming. "Stylometric Fake News Detection Based on Natural Language Processing Using Named Entity Recognition: In-Domain and Cross-Domain Analysis." Electronics 12, no. 17 (August 31, 2023): 3676. http://dx.doi.org/10.3390/electronics12173676.
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Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.
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Shree, Lakshmi, Navya K, Revathi M, Sahana K, and Trisha R. "FAKE NEWS DETECTION USING MACHINE LEARNING." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 217–21. http://dx.doi.org/10.26562/irjcs.2022.v0908.12.
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Fake news has been increasingly prevalent in recent years due to the quick growth of online social networks, which are used for a variety of political and commercial goals. Users of online social networks can easily become infected by these online fake news with deceptive language, and this has already had a significant impact on offline culture. Finding bogus news quickly is a crucial step in enhancing the reliability of information in online social networks. In order to identify false news pieces, creators, and subjects from online social networks and assess the performance of these methods and algorithms, this research looks at them. The report also discusses the difficulties posed by the varied linkages across news sources and the unknowable characteristics of fake news.
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Alonso, Miguel A., David Vilares, Carlos Gómez-Rodríguez, and Jesús Vilares. "Sentiment Analysis for Fake News Detection." Electronics 10, no. 11 (June 5, 2021): 1348. http://dx.doi.org/10.3390/electronics10111348.
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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.
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Sunil Kumar Aithal, S., Krishna Prasad Roa, and R. P. Puneeth. "Fake News Detection using Data Mining Techniques." Journal of Trends in Computer Science and Smart Technology 3, no. 4 (December 31, 2021): 263–73. http://dx.doi.org/10.36548/jtcsst.2021.4.002.
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Nowadays, internet has been well known as an information source where the information might be real or fake. Fake news over the web exist since several years. The main challenge is to detect the truthfulness of the news. The motive behind writing and publishing the fake news is to mislead the people. It causes damage to an agency, entity or person. This paper aims to detect fake news using semantic search.
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Ge, Xiaoyi, Mingshu Zhang, Xu An Wang, Jia Liu, and Bin Wei. "Emotion-Drive Interpretable Fake News Detection." International Journal of Data Warehousing and Mining 18, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijdwm.314585.
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Fake news has brought significant challenges to the healthy development of social media. Although current fake news detection methods are advanced, many models directly utilize unselected user comments and do not consider the emotional connection between news content and user comments. The authors propose an emotion-driven explainable fake news detection model (EDI) to solve this problem. The model can select valuable user comments by using sentiment value, obtain the emotional correlation representation between news content and user comments by using collaborative annotation, and obtain the weighted representation of user comments by using the attention mechanism. Experimental results on Twitter and Weibo show that the detection model significantly outperforms the state-of-the-art models and provides reasonable interpretation.
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Kulkarni, Prasad, Suyash Karwande, Rhucha Keskar, Prashant Kale, and Sumitra Iyer. "Fake News Detection using Machine Learning." ITM Web of Conferences 40 (2021): 03003. http://dx.doi.org/10.1051/itmconf/20214003003.
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Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.
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Dementieva, Daryna, Mikhail Kuimov, and Alexander Panchenko. "Multiverse: Multilingual Evidence for Fake News Detection." Journal of Imaging 9, no. 4 (March 27, 2023): 77. http://dx.doi.org/10.3390/jimaging9040077.
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The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. In this work, we propose Multiverse—a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. Our hypothesis that cross-lingual evidence can be used as a feature for fake news detection is supported by manual experiments based on a set of true (legit) and fake news. Furthermore, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset, showing that (in combination with linguistic features) it yields significant improvements over the baseline models, bringing additional useful signals to the classifier.
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Zhao, Jian, Zisong Zhao, Lijuan Shi, Zhejun Kuang, and Yazhou Liu. "Collaborative Mixture-of-Experts Model for Multi-Domain Fake News Detection." Electronics 12, no. 16 (August 14, 2023): 3440. http://dx.doi.org/10.3390/electronics12163440.
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With the widespread popularity of online social media, people have come to increasingly rely on it as an information and news source. However, the growing spread of fake news on the Internet has become a serious threat to cyberspace and society at large. Although a series of previous works have proposed various methods for the detection of fake news, most of these methods focus on single-domain fake-news detection, resulting in poor detection performance when considering real-world fake news with diverse news topics. Furthermore, any news content may belong to multiple domains. Therefore, detecting multi-domain fake news remains a challenging problem. In this study, we propose a multi-domain fake-news detection framework based on a mixture-of-experts model. The input text is fed to BertTokenizer and embeddings are obtained by jointly calling CLIP to obtain the fusion features. This avoids the introduction of noise and redundant features during feature fusion. We also propose a collaboration module, in which a sentiment module is used to analyze the inherent sentimental information of the text, and sentence-level and domain embeddings are used to form the collaboration module. This module can adaptively determine the weights of the expert models. Finally, the mixture-of-experts model, composed of TextCNN, is used to learn the features and construct a high-performance fake-news detection model. We conduct extensive experiments on the Weibo21 dataset, the results of which indicate that our multi-domain methods perform well, in comparison with baseline methods, on the Weibo21 dataset. Our proposed framework presents greatly improved multi-domain fake-news detection performance.
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Gereme, Fantahun, William Zhu, Tewodros Ayall, and Dagmawi Alemu. "Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting." Information 12, no. 1 (January 7, 2021): 20. http://dx.doi.org/10.3390/info12010020.
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The need to fight the progressive negative impact of fake news is escalating, which is evident in the strive to do research and develop tools that could do this job. However, a lack of adequate datasets and good word embeddings have posed challenges to make detection methods sufficiently accurate. These resources are even totally missing for “low-resource” African languages, such as Amharic. Alleviating these critical problems should not be left for tomorrow. Deep learning methods and word embeddings contributed a lot in devising automatic fake news detection mechanisms. Several contributions are presented, including an Amharic fake news detection model, a general-purpose Amharic corpus (GPAC), a novel Amharic fake news detection dataset (ETH_FAKE), and Amharic fasttext word embedding (AMFTWE). Our Amharic fake news detection model, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well.
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Gereme, Fantahun, William Zhu, Tewodros Ayall, and Dagmawi Alemu. "Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting." Information 12, no. 1 (January 7, 2021): 20. http://dx.doi.org/10.3390/info12010020.
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The need to fight the progressive negative impact of fake news is escalating, which is evident in the strive to do research and develop tools that could do this job. However, a lack of adequate datasets and good word embeddings have posed challenges to make detection methods sufficiently accurate. These resources are even totally missing for “low-resource” African languages, such as Amharic. Alleviating these critical problems should not be left for tomorrow. Deep learning methods and word embeddings contributed a lot in devising automatic fake news detection mechanisms. Several contributions are presented, including an Amharic fake news detection model, a general-purpose Amharic corpus (GPAC), a novel Amharic fake news detection dataset (ETH_FAKE), and Amharic fasttext word embedding (AMFTWE). Our Amharic fake news detection model, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well.
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Tyagi, Ms Sarika. "Fake News Detection Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 2739–43. http://dx.doi.org/10.22214/ijraset.2021.36604.
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Fake news always has been a problem. We, too, might have fallen for a false rumor at least once in our lifetime. Moreover, the fight against fake news over social networking media is intricate. Misinformation related to home remedies for COVID 19 that have not been verified, fake news for lockdown extension or release, casualties and damage in any riots, fake consultancies, and conspiracy were prevalent during the lockdown. Many Researchers have implemented several algorithms for the detection of Fake News. In this paper, we have used several past published research papers along with our research to compare the performances of three algorithms, i.e., Naive Bayes classifier, Logistic Regression, and Support Vector Machine. This provides an idea of the most practical and efficient algorithm, Support Vector Machine, that can be used for fake news detection.
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D’Ulizia, Arianna, Maria Chiara Caschera, Fernando Ferri, and Patrizia Grifoni. "Fake news detection: a survey of evaluation datasets." PeerJ Computer Science 7 (June 18, 2021): e518. http://dx.doi.org/10.7717/peerj-cs.518.
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Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods.
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Vo, Trung Hung, Thi Le Thuyen Phan, and Khanh Chi Ninh. "Development of a fake news detection tool for Vietnamese based on deep learning techniques." Eastern-European Journal of Enterprise Technologies 5, no. 2(119) (October 30, 2022): 14–20. http://dx.doi.org/10.15587/1729-4061.2022.265317.
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With the development of the Internet, social networks and different communication channels, people can get information quickly and easily. However, in addition to real and useful news, we also receive false and unreal information. The problem of fake news has become a difficult and unresolved issue. For languages with few users, such as Vietnamese, the research on fake news detection is still very limited and has not received much attention. In this paper, we present research results on building a tool to support fake news detection for Vietnamese. Our idea is to apply text classification techniques to fake news detection. We have built a database of 4 groups of 2 topics about politics (fake news and real news) and about Covid-19 (fake news and real news). Then use deep learning techniques CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to create the corresponding models. When there is new news that needs to be verified, we just need to apply the classification to see which of the four groups they label into to decide whether it is fake news or not. The tool was able to detect fake news quickly and easily with a correct rate of about 85 %. This result will be improved when getting a larger training data set and adjusting the parameters for the machine learning model. These results make an important contribution to the research on detecting fake news for Vietnamese and can be applied to other languages. In the future, besides using classification techniques (based on content analysis), we can combine many other methods such as checking the source, verifying the author's information, checking the distribution process to improve the quality of fake news detection.
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A, Hemalatha. "Fake News Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1472–78. http://dx.doi.org/10.22214/ijraset.2022.44048.
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Abstract: The role of social media in our day to day life has increased rapidly in recent years. Information quality in social media is an increasingly important issue, but web-scale data hinders experts’ ability to assess and correct much of the inaccurate content, or “fake news”, present in these platforms. It is now used not only for social interaction, but also as an important platform for exchanging information and news. Twitter, Facebook a micro-blogging service, connects millions of users around the world and allows for the real-time propagation of information and news. The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destruction impacts. A lot of research is already focused on detecting it. A human being is unable to detect all these fake news. Detecting fake news is an important step. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We aim to provide the user with the ability to classify the news as “fake” or “real”.
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Bandal, Adwait, and Tushar Rane. "A Review on Fake News Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3351–59. http://dx.doi.org/10.22214/ijraset.2023.52318.
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Abstract: The widespread increase of fake news, generated by both humans and machines, has negative impacts on both society and individuals, politically and socially. The fast-paced nature of social networks makes it difficult to promptly evaluate the reliability of news. Hence, there is a growing need for automated tools to detect fake news. Compared to traditional machine learning techniques, deep learning-based approaches have shown higher accuracy in detecting fake news. Attention and Bidirectional Encoder Representations for Transformers are some of the emerging deep learning-based methods used for this task. A hybrid Neural Network architecture, which combines CNN and LSTM, is also used along with two different dimensionality reduction techniques, PCA and Chi-Square. These techniques are compared with regular ML techniques such as Decision Tree, logistic Regression, K Nearest Neighbor, Random Forest, Support Vector Machine, and Naive Bayes, as well as RNN and LSTM, in terms of parameters like F1-score and accuracy. The goal is to identify the best approach for detecting fake news.
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Rathore, Miss Himanshi. "Detecting Fake Covid 19 News." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2651–55. http://dx.doi.org/10.22214/ijraset.2021.35271.
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The fake news Detection program exists to help its users distinguish between useful information and baseless rumours. It helps one to verify it themselves. In the current coronavirus disease (COVID-19) pandemic, misinformation is particularly prevalent, leading to people believing false and potentially harmful statements and posts. The spread of panic and misunderstanding among the public can be reduced if fake news is detected quickly. This covid 19 fake news detection model is specifically built to identify fake news.
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Fang, Kairui. "Deep Learning Techniques for Fake News Detection." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 511–18. http://dx.doi.org/10.54097/hset.v16i.2638.
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Social media has recently become the primary source for people to consume news. Plenty of users prefer to go to social media apps such as Twitter, Facebook, and Snapchat to obtain the latest social events and news. Meanwhile, traditional media is emulating the new media to post their news on the aforementioned apps. This prevalence is a double-edged sword, for the advantage is that users can easily gain access to the news articles they look for on social media. However, it also provides an ideal platform for fake news propagation. The spread of fake news is extremely fast on social media and can cause adverse effects in real life. The unregimented, incomplete censorship and the absence of fact-checking processes make fake news easy to propagate and hard to control. Therefore, fake news detection on social media has become a trending topic that draws tremendous attention, as shown in figure 1. Nevertheless, as pundits dig into the realm of deep learning, some of the studies utilize deep neural networks (DNN) to build frameworks that would help detect fake news. Although impressive progress on the topic has been made, the lack of a review dissertation that summarizes and synthesizes the overall development of the study would be problematic. Hence, this paper aims to summarize different models implemented in recent studies that improve the veracity of fake news detection.
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Jwa, Heejung, Dongsuk Oh, Kinam Park, Jang Kang, and Hueiseok Lim. "exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)." Applied Sciences 9, no. 19 (September 28, 2019): 4062. http://dx.doi.org/10.3390/app9194062.
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News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.
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Garg, Harshit, and Alisha Goyal. "Techniques of Fake News Detection." International Journal of Civil, Mechanical and Energy Science 6, no. 2 (2020): 6–9. http://dx.doi.org/10.22161/ijcmes.622.
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Zhou, Xinyi, and Reza Zafarani. "Network-based Fake News Detection." ACM SIGKDD Explorations Newsletter 21, no. 2 (November 26, 2019): 48–60. http://dx.doi.org/10.1145/3373464.3373473.
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Upadhayay, Bibek, and Vahid Behzadan. "Hybrid Deep Learning Model for Fake News Detection in Social Networks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13067–68. http://dx.doi.org/10.1609/aaai.v36i11.21670.
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The proliferation of fake news has grown into a global concern with adverse socio-political and economical impact. In recent years, machine learning has emerged as a promising approach to the automation of detecting and tracking fake news at scale. Current state of the art in the identification of fake news is generally focused on semantic analysis of the text, resulting in promising performance in automated detection of fake news. However, fake news campaigns are also evolving in response to such new technologies by mimicking semantic features of genuine news, which can significantly affect the performance of fake news classifiers trained on contextually limited features. In this work, we propose a novel hybrid deep learning model for fake news detection that augments the semantic characteristics of the news with features extracted from the structure of the dissemination network. To this end, we first extend the LIAR dataset by integrating sentiment and affective features to the data, and then use a BERT-based model to obtain a representation of the text. Moreover, we propose a novel approach for fake news detection based on Graph Attention Networks to leverage the user-centric features and graph features of news residing social network in addition to the features extracted in the previous steps. Experimental evaluation of our approach shows classification accuracy of 97% on the Politifact dataset. We also examined the generalizability of our proposed model on the BuzzFeed dataset, resulting in an accuracy 89.50%.
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Xing, Jian, Shupeng Wang, Xiaoyu Zhang, and Yu Ding. "HMBI: A New Hybrid Deep Model Based on Behavior Information for Fake News Detection." Wireless Communications and Mobile Computing 2021 (December 8, 2021): 1–7. http://dx.doi.org/10.1155/2021/9076211.
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Fake news can cause widespread and tremendous political and social influence in the real world. The intentional misleading of fake news makes the automatic detection of fake news an important and challenging problem, which has not been well understood at present. Meanwhile, fake news can contain true evidence imitating the true news and present different degrees of falsity, which further aggravates the difficulty of detection. On the other hand, the fake news speaker himself provides rich social behavior information, which provides unprecedented opportunities for advanced fake news detection. In this study, we propose a new hybrid deep model based on behavior information (HMBI), which uses the social behavior information of the speaker to detect fake news more accurately. Specifically, we model news content and social behavior information simultaneously to detect the degrees of falsity of news. The experimental analysis on real-world data shows that the detection accuracy of HMBI is increased by 10.41% on average, which is the highest of the existing model. The detection accuracy of fake news exceeds 50% for the first time.
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Ali, Ihsan, Mohamad Nizam Bin Ayub, Palaiahnakote Shivakumara, and Nurul Fazmidar Binti Mohd Noor. "Fake News Detection Techniques on Social Media: A Survey." Wireless Communications and Mobile Computing 2022 (August 22, 2022): 1–17. http://dx.doi.org/10.1155/2022/6072084.
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Social media platforms like Twitter have become common tools for disseminating and consuming news because of the ease with which users can get access to and consume it. This paper focuses on the identification of false news and the use of cutting-edge detection methods in the context of news, user, and social levels. Fake news detection taxonomy was proposed in this research. This study examines a variety of cutting-edge methods for spotting false news and discusses their drawbacks. It also explored how to detect and recognize false news, such as credibility-based, time-based, social context-based, and the substance of the news itself. Lastly, the paper examines various datasets used for detecting fake news and proposed an algorithm.
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Racherla, Nishant. "News Aggregator with Fake News Detection using Stacked LSTMs." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3421–26. http://dx.doi.org/10.22214/ijraset.2022.44623.
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Abstract: In a post-truth world, fake news has spread globally in equal proportion. From industrialized countries like the United States, Norway, and Ireland to emerging ones like India, Brazil, and others, no one appears to be immune. Because India is the world's largest democracy with the second largest population, it is particularly vulnerable to fake news. Low literacy rates, combined with an avalanche of fake news, make it difficult to carry out the true spirit of democratic decision-making, putting the country's democracy at risk. Using a Long Short-Term Memory (LSTM) network, our system presents a way of detecting and eliminating fake news from different sources. The news items are also tagged and delivered to the user according to their choices.
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Hosea, I. G., V. O. Waziri, I. Ismaila, J. Ojeniyi, M. Olalere, and O. Adebayo. "A Machine Learning Approach to Fake News Detection Using Support Vector Machine (SVM) and Unsupervised Learning Model." Advances in Multidisciplinary and scientific Research Journal Publication 11, no. 1 (July 11, 2023): 11–18. http://dx.doi.org/10.22624/aims/csean-smart2023p1.
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Blogging over the years have become a lucrative business, the bloggers main aim is to attract people to his or her blog. In the quest for that, many blogs or page post fake news by using enticing captions to captivate the minds of readers. The captions are mostly displayed on social media and by clicking on the captions, the reader will be redirected to the blog where the news is been posted. The posted fake news can sometimes lead to misinformation to the public, violence, inciting conflict and extreme cases, death. Many works have been done on fake news detection with good accuracy rate in terms of detecting fake news. This paper presents an effective way of detecting fake news using Support Vector Machine (SVM) and Lagrangian Duality which yielded an accuracy of 95.74%. Keywords: Machine Learning, Fake News, Detection, Support Vector Machine (SVM), Security, Unsupervised Learning Model, Bloggers, Readers Proceedings Citation Format Gungbias H.I., Waziri, V.O., Ismaila, I., Ojeniyi, J., Olalere, M. & Adebayo, O. (2022): A Machine Learning Approach to Fake News Detection Using Support Vector Machine (SVM) and Unsupervised Learning Model. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 11-18 https://cybersecurenigeria.org/conference-proceedings/volume-2-2023/ dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P1.
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Pandey, Bandana, Guarav Kumar, Leila O. Algavi, Manish Kumar, and Vishal Sharma. "Exposure of fake news to the Indian social media users." RUDN Journal of Studies in Literature and Journalism 28, no. 2 (December 15, 2023): 381–96. http://dx.doi.org/10.22363/2312-9220-2023-28-2-381-396.
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In the modern world, people are too techno-friendly and dependent on technology using the Internet for every work. The same goes for the news. People are shifting from traditional mass media to digital news platforms and getting news through websites, news portals, social media, etc. If you are dependent on the Internet for every kind of information, then you will face false information on the Internet. False or fake news is defined as any information that does not have any credible and reliable source behind it or any misleading information that is likely to mislead the public. The aim behind fake news transmission is to damage a person's or entity's reputation or advertising revenue. If you want not to fall into the fake news you should know about fake news detection and media literacy. The main purpose of the study is to check the exposure of fake news awareness and fake news detection methods among social media users. In the current scenario, this is much necessary to know that social media users have the advisable knowledge of fake news detection and media literacy because people easily fall into the rumors. Mob lynching is one of the biggest rumors on the Indian Internet. In this research, the survey method and questionnaire for data collection were used. The questionnaire was distributed randomly over different social media platforms and emails to the intended respondents. The findings obtained reveal that most fake or false news in India is transmitted through WhatsApp, but social media users have adequate knowledge of fake news and media literacy.
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Pehlivanoglu, Didem, Tian Lin, Kevin Chi, Eliany Perez, Rebecca Polk, Barian Cahill, Nichole Lighthall, and Natalie Ebner. "Fake News Detection in Aging During the Era of Infodemic." Innovation in Aging 5, Supplement_1 (December 1, 2021): 968–69. http://dx.doi.org/10.1093/geroni/igab046.3489.
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Abstract Increasing misinformation spread, including news about COVID-19, poses a threat to older adults but there is little empirical research on this population within the fake news literature. Embedded in the Changes in Integration for Social Decisions in Aging (CISDA) model, this study examined the role of (i) analytical reasoning; (ii) affect; and (iii) news consumption frequency, and their interplay with (iv) news content, in determining fake news detection in aging during the COVID-19 pandemic. Young (age range 18-35 years, M = 20.24, SD = 1.88) and older (age range 61-87 years, M = 70.51, SD = 5.88) adults were randomly assigned to view COVID or non-COVID news articles, followed by measures of analytical reasoning, affect, and news consumption frequency. Comparable across young and older adults, fake news detection accuracy was higher for news unrelated to COVID, and non-COVID fake news detection was predicted by individual differences in analytic reasoning. Examination of chronological age effects further revealed that detection of fake news among older adults aged over 70 years depended on interactions between individual CISDA components and news content. Collectively, these findings suggest that age-related susceptibility to fake news may only be apparent in later stages of older adulthood, but vulnerabilities are context dependent. Our findings advance understanding of psychological mechanisms in fake news evaluation and empirically support CISDA in its application to fake news detection in aging.
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Aslam, Nida, Irfan Ullah Khan, Farah Salem Alotaibi, Lama Abdulaziz Aldaej, and Asma Khaled Aldubaikil. "Fake Detect: A Deep Learning Ensemble Model for Fake News Detection." Complexity 2021 (April 14, 2021): 1–8. http://dx.doi.org/10.1155/2021/5557784.
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Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.
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Mengji, Samarth. "Fake News Detection using RNN-LSTM." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1731–37. http://dx.doi.org/10.22214/ijraset.2021.35687.
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Abstract: Fake news distribution is a social phenomenon that can't be avoided on a personal level or through web-based social media like Facebook and Twitter. We're interested in counterfeit news because it's one of many sorts of double dealing in online media, but it's a more severe one because it's designed to deceive people. We're concerned about this now that we've seen what's going on. We are concerned about this issue because we have seen how, through the usage of social correspondence, this marvel has recently caused a shift in the direction of society and people groupings, as well as their opinions. Along these lines, we chose to confront and decrease this wonder, which is as yet the principal factor to pick a large portion of our choices. Our objective in this study is to develop a detector that can predict if a piece of news is false based just on its content, and then attack the problem using RNN method models LSTMs and Bi-LSTMs to tackle the problem from a basic deep learning viewpoint. Keywords: RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), Fake news detection, Deep learning
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Divija, Amaram. "Fake News Classifier." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1716–22. http://dx.doi.org/10.22214/ijraset.2022.44117.
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Abstract: Fake news is incorrect information that is spread through a social network to harm individuals, authorities or organizations. The spread of fake news poses great challenges to society. Fake news is difficult to detect, but it is easy to spread and have widespread effects. Automated analysis of the reliability of articles is the subject of ongoing research. To address this issue, we offer a model that detects fake information and communications using deep learning and natural language processing. This paper presents a fake news detection model based on LSTM (Long Short-Term Memory) and Bi-LSTM (Bidirectional Long Short-Term Memory). In the first place, we want to present a dataset containing both fake news and genuine news, and perform various tests to sort out a fake news detector. The model was prepared and assessed utilizing a fake news dataset got from Kaggle.
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Saeed, Ramsha, Hammad Afzal, Haider Abbas, and Maheen Fatima. "Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–19. http://dx.doi.org/10.1145/3461614.
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Increased connectivity has contributed greatly in facilitating rapid access to information and reliable communication. However, the uncontrolled information dissemination has also resulted in the spread of fake news. Fake news might be spread by a group of people or organizations to serve ulterior motives such as political or financial gains or to damage a country’s public image. Given the importance of timely detection of fake news, the research area has intrigued researchers from all over the world. Most of the work for detecting fake news focuses on the English language. However, automated detection of fake news is important irrespective of the language used for spreading false information. Recognizing the importance of boosting research on fake news detection for low resource languages, this work proposes a novel semantically enriched technique to effectively detect fake news in Urdu—a low resource language. A model based on deep contextual semantics learned from the convolutional neural network is proposed. The features learned from the convolutional neural network are combined with other n-gram-based features and are fed to a conventional majority voting ensemble classifier fitted with three base learners: Adaptive Boosting, Gradient Boosting, and Multi-Layer Perceptron. Experiments are performed with different models, and results show that enriching the traditional ensemble learner with deep contextual semantics along with other standard features shows the best results and outperforms the state-of-the-art Urdu fake news detection model.
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Buzea, Marius Cristian, Stefan Trausan-Matu, and Traian Rebedea. "Automatic Fake News Detection for Romanian Online News." Information 13, no. 3 (March 14, 2022): 151. http://dx.doi.org/10.3390/info13030151.
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This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges.
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Hordiichuk, M. І. "THE CONCEPT OF FAKE NEWS AND AND ITS DETECTION BY MEANS OF COMMUNICATIVE THEORY." Linguistic and Conceptual Views of the World, no. 68 (1) (2021): 29–39. http://dx.doi.org/10.17721/2520-6397.2021.1.03.
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The article studies the concept of “fake news,” focusing on researching its etymology and the scope of its meaning. The meaning of the concept of “fake news” is studied based on the contrast with the word combination“false news”. The coinages “post-truth” and “fake news” have been recognized as the words of the year by different dictionaries in the recent years, which contributes to the image of the present-day media. Various definitions of fake news of different scholars were analyzed. It was found that fake news are relatively easy in making and spreading due to the advancement of modern technologies. However, they disrupt the faith of readers in media, which stands for the need for the detection of fake news in mass media. Consequently, the detection of fake news has been acknowledged as a crucial skill in the modern era. The paper analyzes the ways communicative theory can be used in the detection of fake news in the media. Four main strategies were identified, namely argumentative, appellative, evaluative, and the strategy of optimizing of perlocutionary effect. Each of the strategies is applied in various ways in fake news making with the use of appropriate tactics within their scope. The argumentative strategy operates with the tactics of manipulation and detalization. Tactics of ideologization, appeal to addressees’ needs and retrospection are used within the appellative strategy. Evaluative strategy includes the tactics of positioning, discrediting the opponent and distancing. And within the strategy of optimizing of perlocutionary effect the tactics of emotion evocation, mnemonization and visualization are identified. The paper provides a theoretical background to all of these strategies. Also, their practical application in the media was analyzed based on the examples taken from the independent fact-checking organization “StopFake”. It was found that all of them are frequently used in the hybrid war against Ukraine to disrupt its official government, treaties with European partners, and provoke chaos within the state.