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A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles [article]

Sourya Dipta Das, Ayan Basak, Saikat Dutta
2021 arXiv   pre-print
We have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet dataset to show the effectiveness of the proposed algorithm on detecting fake news in short news content as well as in news  ...  In this paper, we describe a novel Fake News Detection system that automatically identifies whether a news item is "real" or "fake", as an extension of our work in the CONSTRAINT COVID-19 Fake News Detection  ...  proposed algorithm on detecting fake news in short news content as well as in news articles.  ... 
arXiv:2104.01791v2 fatcat:w4f3svjym5hpxfbdun4axl4x7q

Fake Job Detection Using Machine Learning

Priya Khandagale, Akshata Utekar, Anushka Dhonde, Prof. S. S. Karve
2022 International Journal for Research in Applied Science and Engineering Technology  
Keywords: Fake Job, Online Recruitment, Machine Learning, Ensemble Approach.  ...  Abstract: The research proposes an automated solution based on machine learning-based classification approaches to prevent fraudulent job postings on the internet.  ...  Fake news identification is based on three perspectives: how fake news is written, how fake news spreads, and how a user is connected to fake news.  ... 
doi:10.22214/ijraset.2022.41641 fatcat:r7k4fea5mreqtk575gnn5up3ce

An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques

T V Divya, Barnali Gupta Banik
2021 International Journal of Advanced Computer Science and Applications  
The second one deals with the reposting of the old happenings with new fake content injected into it.  ...  With the revolution that happened in electronic gadgets in the past few years, information sharing has evolved into a new era that can spread the news globally in a fraction of minutes, either through  ...  In [15] , SawinderKaur et al. used the concept of voting at multi-levels to identify fake news.  ... 
doi:10.14569/ijacsa.2021.0121219 fatcat:mlpsu4xpe5dmhhuyiryiak7bcm

WELFake: Word Embedding Over Linguistic Features for Fake News Detection

Pawan Kumar Verma, Prateek Agrawal, Ivone Amorim, Radu Prodan
2021 IEEE Transactions on Computational Social Systems  
To address this issue, this article proposes a two-phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine learning classification  ...  Index Terms-Bidirectional encoder representations from transformer (BERT), convolutional neural network (CNN), fake news, linguistic feature, machine learning (ML), text classification, voting classifier  ...  3) Which classification method is the most appropriate for fake news detection on available data sets? 4) Does ensemble voting classifier improve the fake news detection results?  ... 
doi:10.1109/tcss.2021.3068519 fatcat:zmq6sguvanduvaxyjucxsoe4bu

Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities [article]

Sara Abdali
2022 arXiv   pre-print
Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to the users, and textual contents are sometimes skimmed carelessly, misinformation spreaders  ...  In this work, we aim to analyze, categorize and identify existing approaches in addition to challenges and shortcomings they face in order to unearth new opportunities in furthering the research in the  ...  Acknowledgements This material is based upon work supported by the National Science Foundation under Grant #: 2127309 to the Computing Research Associate for the CIFellows Project.  ... 
arXiv:2203.13883v2 fatcat:gudzmly7hvacjlxop7r5lxaaku

Ensemble Machine Learning Model for Classification of Spam Product Reviews

Muhammad Fayaz, Atif Khan, Javid Ur Rahman, Abdullah Alharbi, M. Irfan Uddin, Bader Alouffi, Dan Selisteanu
2020 Complexity  
), and Random Forest (RF) and predicts the outcome of the review as spam or real (nonspam), based on the majority vote of the contributing models.  ...  The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy.  ...  Once the Voting Classifier has been trained, it can be used to predict the label of new instance based on majority vote of contributing models.  ... 
doi:10.1155/2020/8857570 fatcat:sccra2ooyfblnhxaywrlq5xfqi

A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base.  ...  To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model.  ...  The researcher proposed a novel multi-level voting ensemble model using twelve classifiers combined to predict based on their false prediction ratio [19] .  ... 
doi:10.35940/ijitee.a8176.1110120 fatcat:uxpf23jjtrbbnijkuotqqz6qwe

Ternion: An Autonomous Model for Fake News Detection

Noman Islam, Asadullah Shaikh, Asma Qaiser, Yousef Asiri, Sultan Almakdi, Adel Sulaiman, Verdah Moazzam, Syeda Aiman Babar
2021 Applied Sciences  
For this study, the fake news dataset was taken from Kaggle.  ...  Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity  ...  An ensemble-based technique for fake news detection is presented in [33] . Ensemble-based approaches combined various weak classifiers to achieve better accuracy for combined classification tasks.  ... 
doi:10.3390/app11199292 fatcat:5x5ctzziizgipo6al7moxgu34m

Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives [article]

Sajad Dadgar, Mehdi Ghatee
2021 arXiv   pre-print
On the other hand, we classify misinformation using the content of the tweets directly in the content-based process, which contains text classification models (paragraph-level and sentence-level) and similarity  ...  In addition, in the text classification models, the best result was achieved using the stacking ensemble-learning model by obtaining an F1 score of 95.18%.  ...  Hamid et al. (2020) proposed two independent approaches: content-based and structure-based for fake news detection.  ... 
arXiv:2107.09768v1 fatcat:qfwp6grs6zab5glchyx34bnyxy

Stance Detection

Dilek Küçük, Fazli Can
2020 ACM Computing Surveys  
Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/  ...  veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications.  ...  detection competition for fake news detection is established in 2017 (named Fake News Challenge), 2 based on Definition 1.5.  ... 
doi:10.1145/3369026 fatcat:5fviqvi6o5by7p6pdfrgxub2vi

Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning

Alyssa Anne Ubing, Syukrina Kamilia, Azween Abdullah, NZ Jhanjhi, Mahadevan Supramaniam
2019 International Journal of Advanced Computer Science and Applications  
Hence, a feature selection algorithm is employed and integrated with an ensemble learning methodology, which is based on majority voting, and compared with different classification models including Random  ...  We start with a set of 177 features of which 38 are content-based and the rest are URL-based.  ...  Boosting repeatedly applies a base learner to modified versions of a dataset. Each boosting iteration fits the weighted training data to a base learner.  ... 
doi:10.14569/ijacsa.2019.0100133 fatcat:bzc7ogrn45c2bbd42hut5ee5ou

A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [article]

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
Fake news and misinformation are a matter of concern for people around the globe. Users of the internet and social media sites encounter content with false information much frequently.  ...  The combination of various modalities has resulted in efficient fake news detection. At present, there is an abundance of surveys consolidating textual fake news detection algorithms.  ...  While referring to data modalities, fake news spreads through text, images, videos, audios, hyperlinks, embedded content, and hybrids.  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4

Video Face Manipulation Detection Through Ensemble of CNNs [article]

Nicolò Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro
2020 arXiv   pre-print
., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance.  ...  In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models.  ...  The proposed method is based on the concept of ensembling. Indeed, it is well-known that model ensembling may lead to better prediction performance.  ... 
arXiv:2004.07676v1 fatcat:dbiookgizfgxlkbzol54kjfc3e

Clickbait Detection in YouTube Videos [article]

Ruchira Gothankar, Fabio Di Troia, Mark Stamp
2021 arXiv   pre-print
This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail.  ...  They used TF-IDF and Word2Vec with a dense neural network based on the news headline. In another paper on fake news detection, Jwa et al.  ...  Fake News Detection Fake news is a type of misinformation that has received considerable attention in recent years.  ... 
arXiv:2107.12791v1 fatcat:tyzpwuvhxvcwlgutmsvqrzqspu

Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution [article]

Michela Fazzolari and Francesco Buccafurri and Gianluca Lax and Marinella Petrocchi
2020 arXiv   pre-print
Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers.  ...  Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can  ...  So, the work in [16] proposed an unsupervised learning approach based on fuzzy logic, developing a new deductive algorithm able to obtain about 80% accuracy on the classification of a group of reviewers  ... 
arXiv:2012.13905v1 fatcat:ht2hc76gubb4xbqtln2uia3qla
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