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Sentiment Analysis for Fake News Detection by Means of Neural Networks [chapter]

Sebastian Kula, Michał Choraś, Rafał Kozik, Paweł Ksieniewicz, Michał Woźniak
2020 Lecture Notes in Computer Science  
In this paper, we present an innovative solution for fake news detection that utilizes deep learning methods. Our experiments prove that the proposed approach allows us to achieve promising results.  ...  The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media.  ...  The main contribution of this paper is the proposition and evaluation of neural network-based approach to text analysis and fake news detection.  ... 
doi:10.1007/978-3-030-50423-6_49 fatcat:3q2ps7r4bzd4fd2e6qzvvlxtrq

Sentiment Aware Fake News Detection on Online Social Networks

Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari
2019 ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
This work aims to understand and analyse the characteristics of fake news especially in relation to sentiments, for the automatic detection of fake news and rumors.  ...  We verify our hypothesis by comparing with the state-of-the-art baseline text-only fake news detection methods that do not consider sentiments.  ...  [5] employed a hybrid of recurrent neural networks and convolutional neural networks to show that fake news and rumors could be predicted achieving hight accuracy without prior knowledge of the topic  ... 
doi:10.1109/icassp.2019.8683170 dblp:conf/icassp/AjaoBZ19 fatcat:lpzalpo6a5cp7nofufoox5kjl4

Sherlock: An Ensemble based Deep Learning Framework for Fake News Detection

Sameer Kulkarni, R. M., Atharva Bhusari
2020 International Journal of Computer Applications  
The technique of using pre-trained word vectors as word embeddings for semantic analysis has shown performance boost by 2-4%. Additionally, a scale for measuring fakeness of news is proposed.  ...  The best test accuracies of 94% for binary classification and 65.5% for multiclass classification were obtained for a GRU (Gated Recurrent Unit) based deep neural network model which has been incorporated  ...  INTRODUCTION Fake news analysis and detection has become an emerging field of research due its effect on the socio-economic and political factors of the world.  ... 
doi:10.5120/ijca2020920218 fatcat:263fvrjzvzakphpaltdt6tmcd4

Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements [article]

Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu
2020 arXiv   pre-print
The wide spread of disinformation and fake news can cause detrimental societal effects.  ...  Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation.  ...  Acknowledgements This material is based upon work supported by, or in part by, ONR N00014-17-1-2605, N000141812108, NSF grants #1742702, #1820609, #1915801. This work has been inspired by Dr.  ... 
arXiv:2001.00623v1 fatcat:zcmgzbudjvab3fckajrmrbppoy

Fake News and Phishing Detection Using a Machine Learning Trained Expert System [article]

Benjamin Fitzpatrick, Xinyu "Sherwin" Liang, Jeremy Straub
2021 arXiv   pre-print
This paper presents the use of a machine learning trained expert system (MLES) for phishing site detection and fake news detection.  ...  The fake news detection study uses a MLES rule-fact network to gauge news story truthfulness based on factors such as emotion, the speaker's political affiliation status, and job.  ...  Thanks is given to Logan Brown and Reid Pezewski for their feedback on network creation and a draft of this manuscript.  ... 
arXiv:2108.08264v1 fatcat:22zvtswnkrh4pkfccx5tdammzy

Deep Learning based Sentiment Analysis for Social Media Network

2019 International Journal of Engineering and Advanced Technology  
Sentiment evaluation addresses such need by way of detecting evaluations on the social media textual content. Product evaluations are valuable for upcoming shoppers in supporting them make choices.  ...  In recent, deep learning is loom as a powerful manner for fixing sentiment classification troubles. The neural network intrinsically learns a beneficial representation without the efforts of human.  ...  By using Rakuten Data the neural networks were examined. Overall performance opinions of deep learning classifiers for massive-scale sentiment analysis were done by using Rakuten Data.  ... 
doi:10.35940/ijeat.a1098.1291s419 fatcat:cylf5hfesfcsrbxgcmjq7hxpji

Fake or Real? A Study of Arabic Satirical Fake News [article]

Hadeel Saadany and Emad Mohamed and Constantin Orasan
2020 arXiv   pre-print
This type of fake news is often disseminated by individuals on their online platforms as it has a much stronger effect in delivering criticism than through a straightforward message.  ...  However, when the satirical text is disseminated via social media without mention of its source, it can be mistaken for real news.  ...  They acknowledge that hand-crafted analysis of these features might contribute to a robust classifier, but a neural network model such as a Convolutional Neural Network (CNN) or a Recurrent Neural Network  ... 
arXiv:2011.00452v1 fatcat:bvsn42c7kzcfffjfrzdgpherve

Sentiment Analysis for Fake News Detection

Miguel A. Alonso, David Vilares, Carlos Gómez-Rodríguez, Jesús Vilares
2021 Electronics  
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  ...  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  ...  [139] introduced a novel model to compute the sentiment of texts with a tree-recursive neural network.  ... 
doi:10.3390/electronics10111348 fatcat:p34nbmtkzrcqrowu24nmu4axnq

TI-CNN: Convolutional Neural Networks for Fake News Detection [article]

Yang Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li, Philip S. Yu
2022 arXiv   pre-print
For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks.  ...  With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world.  ...  According to the experimental results, the lexical diversity of real news is 2.2e-06, which is larger than 1.76e-06 for fake news. 4) Sentiment Analysis: The sentiment [26] in the real and fake news  ... 
arXiv:1806.00749v2 fatcat:fpcewq466jhfxd6uwckzlwsdga

Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection [article]

David Ifeoluwa Adelani, Haotian Mai, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen
2019 arXiv   pre-print
Three countermeasures, Grover, GLTR, and OpenAI GPT-2 detector, were found to be difficult to accurately detect fake review.  ...  In particular, we use the GPT-2 NLM to generate a large number of high-quality reviews based on a review with the desired sentiment and then using a BERT based text classifier (with accuracy of 96%) to  ...  This work was partially supported by a JST CREST Grant (JP-MJCR18A6) (VoicePersonae Project), Japan, and by MEXT KAKENHI Grants (16H06302, 17H04687, 18H04120, 18H04112, 18KT0051), Japan.  ... 
arXiv:1907.09177v2 fatcat:vjtumzimgjftnapn6ggufvkzqy

Credibility-based Fake News Detection [article]

Niraj Sitaula, Chilukuri K. Mohan, Jennifer Grygiel, Xinyi Zhou, Reza Zafarani
2019 arXiv   pre-print
In this paper, we emphasize the detection of fake news by assessing its credibility.  ...  Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users.  ...  .: Eann: Event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 849-857.  ... 
arXiv:1911.00643v1 fatcat:ma24ywa7crhphaywfwfkl5txqe

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
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.  ...  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.  ...  Fake news detection technologies using NLP, text classification, vector-space models, rhetoric structure theory, opinion mining, sentiment analysis, graph theory, deep neural networks, and others have  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4

FacTweet: Profiling Fake News Twitter Accounts [chapter]

Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso
2020 Lecture Notes in Computer Science  
We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features.  ...  Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently.  ...  Acknowledgment The work of Paolo Rosso was partially funded by the the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE  ... 
doi:10.1007/978-3-030-59430-5_3 fatcat:uofjtdkpxfg6rn3y75cjm7sfdq

Facebook Profile Credibility Detection using Machine and Deep Learning Techniques based on User's Sentiment Response on Status Message

Esraa A. Afify, Ahmed Sharaf, Ayman E.
2020 International Journal of Advanced Computer Science and Applications  
One of the most significant approaches is Sentiment Analysis (SA) which plays a major role in assessing and detecting the credibility degree of each user account behavior.  ...  Recently, the impact of online Social Network sites (SNS) has dramatically changed, and fake accounts became a vital issue that has rapidly evolved.  ...  .  Update -The centroid of the clusters becomes the new mean.  ... 
doi:10.14569/ijacsa.2020.0111273 fatcat:xmpfapi3gbabjgnjptpcghsdei

Calling to CNN-LSTM for Rumor Detection: A Deep Multi-channel Model for Message Veracity Classification in Microblogs [chapter]

Abderrazek Azri, Cécile Favre, Nouria Harbi, Jérôme Darmont, Camille Noûs
2021 Lecture Notes in Computer Science  
Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news.  ...  model called deepMONITOR that is based on deep neural networks and allows quite accurate automated rumor verification, by utilizing all three characteristics: post textual and image contents, as well  ...  [28] propose a neural-network-based method named SAFE that utilizes news multimodal information for fake news detection, where news representation is learned jointly by news textual and visual information  ... 
doi:10.1007/978-3-030-86517-7_31 fatcat:gfsumfrovraglpe5luvla225iy
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