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Ant Colony Decision Tree Method to Detect the Suspicious News

2019 International journal of recent technology and engineering  
The results of the proposed ACSTDSN framework are accessed with the performance evaluation measures.  ...  The escalation of these suspicious news has open a lot of research ventures to detect and attenuate the impact on human. These suspicious activities have become a nuisance for legitimate users.  ...  The research on fake news is still growing and expanding for the better prediction of such kind of fake news.  ... 
doi:10.35940/ijrte.d6794.118419 fatcat:2y3gnxg5mjhmphd2wk5pnvpyhu

DALF: An AI Enabled Adversarial Framework for Classification of Hyperspectral Images

Tatireddy Subba Reddy, Jonnadula Harikiran
2021 Indonesian Journal of Electrical Engineering and Informatics (IJEEI)  
By training GAN with associated deep learning models, the framework leverages classification performance.  ...  , Generative Adversarial Network (GAN) for generating new Hyperspectral Imaging (HSI) samples that are to be verified by a discriminator in a non-cooperative game setting besides using a classifier.  ...  Network (GAN) for generating new Hyperspectral Imaging (HSI) samples that are to be verified by a discriminator in a non-cooperative game setting besides using CNN for classification.  ... 
doi:10.52549/ijeei.v9i4.3339 fatcat:qfyt6zatqjgehi7wtcrnoxmbza

Spam Detection using Sentiment Analysis of Text

Vignesh N
2019 International Journal for Research in Applied Science and Engineering Technology  
In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification  ...  The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests.  ...  In this work we present a novel solution toward spam filtering by using a new set of features for classification models.  ... 
doi:10.22214/ijraset.2019.3413 fatcat:fpu7clgop5b7nmqt6pcaf4y5gi

NITK_NLP at CheckThat! 2021: Ensemble Transformer Model for Fake News Classification

Hariharan RamakrishnaIyer LekshmiAmmal, Anand Kumar Madasamy
2021 Conference and Labs of the Evaluation Forum  
In this paper, we have proposed a model for Fake News Classification as a part of CLEF2021 Checkthat!  ...  Lab 1 shared task, which had Multi-class Fake News Detection and Topical Domain Classification of News Articles.  ...  [14] have proposed a transformer model for fake news classification of a specific domain dataset, including human justification and metadata for added performance.  ... 
dblp:conf/clef/LekshmiAmmalM21 fatcat:itzgypqdrba7zlsvsmh7dpekgm

Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

Despoina Mouratidis, Maria Nefeli Nikiforos, Katia Lida Kermanidis
2021 Computation  
In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for  ...  Our main results show high overall accuracy performance in fake news detection.  ...  They evaluated their approach on a variety of parallel classification tasks for sentiment analysis, and showed that, when the learning framework utilizes the ranker scores, the classification system outperforms  ... 
doi:10.3390/computation9020020 fatcat:p7ciykp6kzbp3dw45snrgehaje

Predicting image credibility in fake news over social media using multi-modal approach

Bhuvanesh Singh, Dilip Kumar Sharma
2021 Neural computing & applications (Print)  
The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.  ...  The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis.  ...  The model can be used by fact-checking websites across the world to move towards automated marking of fake news, fake images for the posts shared over microblogging websites.  ... 
doi:10.1007/s00521-021-06086-4 pmid:34054227 pmcid:PMC8143443 fatcat:h2uxvbdyljgxhgneaidvzdle6i

Prediction of Fake Tweets Using Machine Learning Algorithms [chapter]

M. Sreedevi, G. Vijay Kumar, K. Kiran Kumar, B. Aruna, Arvind Yadav
2021 Advances in Parallel Computing  
In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.  ...  Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time.  ...  Text performance on integrated cluster classification performed better results than SVM classification.  ... 
doi:10.3233/apc210195 fatcat:fnofoeveszcdtpgoqj6c2k5suq

Testing the Robustness of a BiLSTM-based Structural Story Classifier [article]

Aftab Hussain and Sai Durga Prasad Nanduri and Sneha Seenuvasavarathan
2022 arXiv   pre-print
While several machine learning techniques for this purpose have emerged, we observe that there is a need to evaluate the impact of noise on these techniques' performance, where noise constitutes news articles  ...  This work takes a step in that direction, where we examine the impact of noise on a state-of-the-art, structural model based on BiLSTM (Bidirectional Long-Short Term Model) for fake news detection, Hierarchical  ...  In addition, HDSF provides a path towards better understanding the language of fake news.  ... 
arXiv:2201.02733v2 fatcat:e5sppszp25bkpkbhq3xdymx3ey

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment [article]

Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
2021 arXiv   pre-print
The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society.  ...  We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM).  ...  Conclusions In this paper, we propose an explainable and interpretable Tsetlin Machine (TM) framework for fake news classification.  ... 
arXiv:2105.09114v1 fatcat:edw4zmbmgvhgzdaucsgzzcogwm

Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings [article]

Arkadipta De, Venkatesh E, Kaushal Kumar Maurya, Maunendra Sankar Desarkar
2021 arXiv   pre-print
We view this hostility detection as a multi-label multi-class classification problem. We propose an effective neural network-based technique for hostility detection in Hindi posts.  ...  Our best performing neural classifier model includes One-vs-the-Rest approach where we obtained 92.60%, 81.14%,69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive, and defamation labels  ...  Ruchansky et. al. [16] consider text, response and source of a news in a deep learning framework for fake news detection.  ... 
arXiv:2101.04998v1 fatcat:z4bdmqg7mzdlbggjh7am472o2q

An Improved Classification Model for Fake News Detection in Social Media

Bodunde Akinyemi, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, Oluwakemi Adewusi, Adedoyin Oyebade
2020 International Journal of Information Technology and Computer Science  
This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.  ...  Existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate.  ...  , thus has a better performance in detecting fake news instances accurately.  ... 
doi:10.5815/ijitcs.2020.01.05 fatcat:dhk7guecfzcnnmiu4qopp3etyi

Approaches to the Profiling Fake News Spreaders on Twitter Task in English and Spanish

Jacobo López Fernández, Juan Antonio López Ramírez
2020 Conference and Labs of the Evaluation Forum  
We briefly describe how we combined author tweets to create samples that do or do not represent a Fake News Spreader.  ...  This paper discusses the decisions made approaching PANs Profiling Fake News Spreaders on Twitter Task at CLEF 2020.  ...  So, it focuses on identifying possible fake news spreaders on social media as a first step towards preventing fake news from being propagated among online users.  ... 
dblp:conf/clef/FernandezR20 fatcat:m332t44i4ncdpmmyf4j5qz4dam

Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset

Adamu Ali-Gombe, Eyad Elyan, Chrisina Jayne
2019 2019 International Joint Conference on Neural Networks (IJCNN)  
We introduce a new Multiple Fake Class Generative Adversarial Networks (MFC-GAN) and generate additional samples to rebalance the dataset.  ...  We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class.  ...  The new dataset was evaluated using a CNN and results obtained showed that the classifier performs better when the synthetic samples were added.  ... 
doi:10.1109/ijcnn.2019.8851953 dblp:conf/ijcnn/Ali-GombeEJ19 fatcat:chvzwiulmzferovfrrwccu2ugm

A Survey on Detection of Fake and Biased News using Machine Learning Techniques

Dr Deepak N R, Akshaya R, Anju Krishnan R, Archana Krishnan R, Arya Prasad, Tabassum Ara
2021 Zenodo  
For months, the Corona virus and its associated impact has given rise to many fake news worldwide, including India.  ...  With fierce competition in news outlets and social media, it has become a threat to consumers.  ...  They have performed "News Content Classification" and "User Profile Classification" for detecting fake user profiles by combining the social information of the user and news .  ... 
doi:10.5281/zenodo.5406872 fatcat:cpgnm26rknaazbajqbipgbibo4

Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News [article]

Kai Shu, Guoqing Zheng, Yichuan Li, Subhabrata Mukherjee, Ahmed Hassan Awadallah, Scott Ruston, Huan Liu
2020 arXiv   pre-print
Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.  ...  However, this unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation.  ...  comparison for early fake news classification.  ... 
arXiv:2004.01732v1 fatcat:djfuown67zbxpcjygi7mv2rqde
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