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On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification [article]

Gaurav Bhatt, Aman Sharma, Shivam Sharma, Ankush Nagpal, Balasubramanian Raman, Ankush Mittal
2017 arXiv   pre-print
We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this problem.  ...  In this paper, we explore a subtask to fake news identification, and that is stance detection. Given a news article, the task is to determine the relevance of the body and its claim.  ...  Creating a dataset for a complex NLP problems such as fake news identification is indeed a cumbersome task, and we appreciate the work by the FNC organizers, yet, a more detailed and elaborate dataset  ... 
arXiv:1712.03935v1 fatcat:cmmm47fru5a35jzdlbfibbx3dm

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.  ...  This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development.  ...  [14] presented a novel approach combining neural, external, and statistical features.  ... 
doi:10.3390/app11199292 fatcat:5x5ctzziizgipo6al7moxgu34m

Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)

Muhammad Umer, Zainab Imtiaz, Saleem Ullah, Arif Mehmood, Gyu Sang Choi, Byung-Won On
2020 IEEE Access  
Thus, it goes without saying that fake news identification is undeniably a grave challenge for the news industry and journalists and the tools for detection of fake news have become dire necessity.  ...  The use of pre-training and the combination of neural representations together with external similarity features produces 83.8% accuracy.  ...  For more information, see https://creativecommons.org/licenses/by/4.0/. CONFLICT OF INTEREST Authors declare no conflict of interest exists.  ... 
doi:10.1109/access.2020.3019735 fatcat:mqbb4exyuvfmbnycqbwal4okje

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

Hema Karande, Rahee Walambe, Victor Benjamin, Ketan Kotecha, TS Raghu
2021 PeerJ Computer Science  
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  ...  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.  ...  RESULTS AND DISCUSSION In this work, we have proposed an approach to upgrade the fake news identification system with the inclusion of an additional feature termed ''stance''.  ... 
doi:10.7717/peerj-cs.467 pmid:33954243 pmcid:PMC8053013 fatcat:a22nc4r4rzeafjl4r2ynguxvzm

Combating Fake News: A Survey on Identification and Mitigation Techniques [article]

Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, Yan Liu
2019 arXiv   pre-print
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion  ...  While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in  ...  ACKNOWLEDGMENTS We thank the reviewers and moderators for their invaluable comments and inputs on earlier versions of this manuscript.  ... 
arXiv:1901.06437v1 fatcat:xa2ecuhp4fcy5jetoiz5qchg6a

Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection [article]

William Scott Paka, Rachit Bansal, Abhay Kaushik, Shubhashis Sengupta, Tanmoy Chakraborty
2021 arXiv   pre-print
At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts.  ...  This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets.  ...  Chakraborty would like to thank the generous support of the Ramanujan Fellowship (SERB) and Infosys Centre for AI, IIIT Delhi.  ... 
arXiv:2102.08924v3 fatcat:kywf6pc24zgwxosnz2r56cabz4

The Future of Misinformation Detection: New Perspectives and Trends [article]

Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, Zhiwen Yu
2019 arXiv   pre-print
As a promising and rapid developing research field, we find that many efforts have been paid to new research problems and approaches of MID.  ...  We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion  ...  In [6] , Bha acharjee et al. propose a human-machine collaborative learning system for fast identification of fake news.  ... 
arXiv:1909.03654v1 fatcat:34h2os2pzrbm3kqluk5uajtr6i

Motivations, Methods and Metrics of Misinformation Detection: An NLP Perspective

Qi Su, Mingyu Wan, Xiaoqian Liu, Chu-Ren Huang
2020 Natural Language Processing Research  
This paper discusses the main issues of misinformation and its detection with a comprehensive review on representative works in terms of detection methods, feature representations, evaluation metrics and  ...  However, it remains a challenging problem for the Natural Language Processing community.  ...  ACKNOWLEDGMENTS We are grateful to the anonymous reviewers for their valuable and constructional advices on the previous versions of this article; all remaining errors are our own.  ... 
doi:10.2991/nlpr.d.200522.001 fatcat:vwwspvaexbga3kn5mxtdo6ke6u

Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey [article]

Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini
2022 arXiv   pre-print
Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.  ...  We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news.  ...  Existing stance detection approaches based on embedding features on individual posts to predict stance of that particular content.  Meta-data: We can analyze fake news differently with different measure  ... 
arXiv:2201.08032v1 fatcat:riy3ywveijb2fjmeewccjx2aiy

Complex Network and Source Inspired COVID-19 Fake News Classification on Twitter

Khubaib Ahmed Qureshi, Rauf Ahmed Shams Malick, Muhammad Sabih, Hocine Cherifi
2021 IEEE Access  
One uses their respective reply for stance identification. Once the news is posted through a tweet, then each reply has a different stance.  ...  There are many successful studies for fake news detection based on stance classification [47] . 2.  ...  Figure 13 shows a single fake news article's propagators community and figure 14 is for the true news. The difference between both community's structure is com-  ... 
doi:10.1109/access.2021.3119404 fatcat:x6bfbpq2cjbdhg3fx7uiw55jgm

FakeBERT: Fake news detection in social media with a BERT-based deep learning approach

Rohit Kumar Kaliyar, Anurag Goswami, Pratik Narang
2021 Multimedia tools and applications  
In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a  ...  Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale.  ...  They have used weighted n-gram bag of word model using statistical features and other external features with the help of featuring engineering.  ... 
doi:10.1007/s11042-020-10183-2 pmid:33432264 pmcid:PMC7788551 fatcat:whzio3hjvfh2dctmc7zspxujr4

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
This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text.  ...  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.  ...  of fake news among networks and people, geolocation features those study areas of fake news generation and propagation and other external features.  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4

Fake News Detection on Social Media: A Data Mining Perspective [article]

Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, Huan Liu
2017 arXiv   pre-print
The extensive spread of fake news has the potential for extremely negative impacts on individuals and society.  ...  We also discuss related research areas, open problems, and future research directions for fake news detection on social media.  ...  Recently, various visual and statistical features has been extracted for news verification [38] .  ... 
arXiv:1708.01967v3 fatcat:b727omad7rfcvpk6glmhr5djg4

Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation [article]

Kai Shu, Deepak Mahudeswaran, Suhang Wang, Huan Liu
2019 arXiv   pre-print
these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection.  ...  In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection.  ...  We compare these features to see if they are different or not for fake and real news with statistical analysis. RQ2 Can we use the extracted features to detect fake news and how?  ... 
arXiv:1903.09196v1 fatcat:yllkw2ev3zafddy3cmlobzgpwi

Fake News Detection of COVID-19 on Twitter Platform: A Review

Dr. Hamid Ghous Khansa Rana
2021 Zenodo  
In fake news identification, the effect of linguistic features and contextual characteristics areanalysed and some techniques such as Naive Bayes, Decision tree, Hybrid CNN, KNN, and SVMare compared.  ...  and to achieve enhanced classification results.The main purpose of this paper is to review literature for COVID-19 fake news detection on Twitter using machine learning and deep learning method  ...  A semantic false news identification system developed on contextual features such as expectations, identities, or statistics derived directly from the text.  ... 
doi:10.5281/zenodo.4536673 fatcat:vqci2b66ovg5znmfzbcvigh3tm
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