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Multi-Source Multi-Class Fake News Detection

Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, Jiliang Tang
2018 International Conference on Computational Linguistics  
In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection  ...  In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources.  ...  The Proposed Framework Multi-source multi-class fake news detection faces three challenges.  ... 
dblp:conf/coling/KarimiRST18 fatcat:byjvpg5hkbcshpjhlyq376fhga

An Emotion-Based Multi-Task Approach to Fake News Detection (Student Abstract)

Arjun Choudhry, Inder Khatri, Minni Jain
Social media, blogs, and online articles are instant sources of news for internet users globally. But due to their unmoderated nature, a significant percentage of these texts are fake news or rumors.  ...  In this work, we hypothesize that legitimacy of news has a correlation with its emotion, and propose a multi-task framework predicting both the emotion and legitimacy of news.  ...  We expand sentiment analysis for fake news detection by treating it as a multi-class emotion classification task.  ... 
doi:10.1609/aaai.v36i11.21601 fatcat:o2tge36mrjdlzkgpnv7o4fjfji

NITP-AI-NLP@UrduFake-FIRE2020: Multi-layer Dense Neural Network for Fake News Detection in Urdu News Articles

Abhinav Kumar, Sunil Saumya, Jyoti Prakash Singh
2020 Forum for Information Retrieval Evaluation  
Therefore, the early detection of fake news from the online platform is extremely important.  ...  The current work utilizes the dataset of Urdu language for fake news detection. Two different models have been proposed in the paper.  ...  Introduction Social media and blogs have become a major source of news across the world [1, 2, 3] .  ... 
dblp:conf/fire/KumarSS20b fatcat:vk7qd2bg7zayxbnuja6f4vmium

Learning Efficient Representations for Fake Speech Detection

Nishant Subramani, Delip Rao
2) How can we rapidly adapt detection models to new sources of fake speech?  ...  We show how the fake speech detection task naturally lends itself to a novel multi-task problem further improving F1 scores for a mere 0.5% increase in model parameters.  ...  have two classification blocks, one for each task -fake speech detection and speech source detection.  ... 
doi:10.1609/aaai.v34i04.6044 fatcat:huhjgvwquve2jcq537lkyquyka

M82B at CheckThat! 2021: Multiclass Fake News Detection Using BiLSTM

Sohel Siddique Ashik, Abdur Rahman Apu, Nusrat Jahan Marjana, Md. Sanzidul Islam, Md. Arid Hassan
2021 Conference and Labs of the Evaluation Forum  
Meanwhile, fake news detection using machine learning is becoming a prominent area in the research to identify the credibility of the news instantaneously.  ...  To present this work, we used Bidirectional Long Short-Term Memory (BiLSTM) to predict the news is either fake or true. We participated in the fake news classification shared task of CheckThat!  ...  Recently deep learning techniques have been applied in identifying fake news widely. Karimiha et al. Proposed a Multi-source Multi-class Fake news Detection framework (MMFD).  ... 
dblp:conf/clef/AshikAMIH21 fatcat:fj32a7hqgzdinl2d4my2ejrogi

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  ...  In [12] , the authors propose methods to combine information from different available sources to tackle the problem of Multi-source Multiclass Fake-news Detection.  ... 
arXiv:2101.04998v1 fatcat:z4bdmqg7mzdlbggjh7am472o2q

Multi-Label Classification of Fake News on Social-Media

Ashwini Deshmukh, Garima Mahto, Harsh Dedhia, Jyoti Sharma
2022 Zenodo  
What is fake information, as we've all heard? Where did this data come from? Counterfeit news is only a bogus explanation or deluding data introduced as news.  ...  Our undertaking's significant objective will be to utilize news information to gauge whether anything is fake or genuine in our framework  ...  The datasets are as follows:  Fake News Source  LIARS  ISOT Fake News We focused on multi-label categorization in the second stage, which we described in the research methods section.  ... 
doi:10.5281/zenodo.6539309 fatcat:7waxw3rryvhcxgrkqxkack6k3e

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

Qi Su, Mingyu Wan, Xiaoqian Liu, Chu-Ren Huang
2020 Natural Language Processing Research  
Alternative solutions to anti-misinformation imply a trend of hybrid multi-modal representation, multi-source data and multi-facet inference, e.g., leveraging the language complexity.  ...  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  ...  any labelled data.Karimi et al. (2018) propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of  ... 
doi:10.2991/nlpr.d.200522.001 fatcat:vwwspvaexbga3kn5mxtdo6ke6u

A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials [article]

Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Luc Van Gool
2022 arXiv   pre-print
Our study provides new insights on these essentials in the context of continual deepfake detection.  ...  Our benchmark dataset and the source code will be made publicly available.  ...  " " " # Multi-class loss … … … ! ! ! " ! # " ! " " " # Binary- The problem can be relaxed to a CMC problem, once each real/fake source is regarded as an independent class.  ... 
arXiv:2205.05467v2 fatcat:zhlszty5gbbvdnwozuc4on7lci

A Multi-Policy Framework for Deep Learning-Based Fake News Detection [article]

João Vitorino, Tiago Dias, Tiago Fonseca, Nuno Oliveira, Isabel Praça
2022 arXiv   pre-print
The obtained results demonstrate that a multi-policy analysis reliably identifies suspicious statements, which can be advantageous for fake news detection.  ...  This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection by using deep learning techniques to analyze a statement itself and its related news articles,  ...  Even though nearly all fake news could be detected, 40% of true news were misclassified as fake news.  ... 
arXiv:2206.11866v1 fatcat:vurhvq4lknccpby63kpjlmfbii

Using a Word Analysis Method and GNNs to Classify Misinformation Related to 5G-Conspiracy and the COVID-19 Pandemic

Ferdinand Schaal, Jesper Phillips
2020 MediaEval Benchmarking Initiative for Multimedia Evaluation  
RELATED WORK Fake news detection has been widely recognized in research in the recent past. There are numerous ways of tackling the challenging problem of fake news.  ...  Zhou et al. [12] divides the main methods used for fake news detection into four categories; Knowledge-based,  ...  RESULTS AND ANALYSIS Results: NLP-based Fake News Detection For the NLP subtask, we achieved a score of 0.372 for multi-class classification and 0.385 for binary classification.  ... 
dblp:conf/mediaeval/SchaalP20 fatcat:ea3cyjtfsrg2dbnm3btgb3azx4

AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset [article]

Mohamed Seghir Hadj Ameur, Hassina Aliane
2021 arXiv   pre-print
This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset.  ...  Though the dataset is mainly designed for fake news detection, it can also be used for hate speech detection, opinion/news classification, dialect identification, and many other tasks.  ...  Our dataset uses multi-label classes (tasks) that consider multiple aspects of each tweet, thus increasing its utility and allowing more accurate Arabic fake news detection models to be built.  ... 
arXiv:2105.03143v1 fatcat:5w7nbjvvzjeorfvbgnjmxe2h2a

FNR: A Similarity and Transformer-Based Approachto Detect Multi-Modal FakeNews in Social Media [article]

Faeze Ghorbanpour, Maryam Ramezani, Mohammad A. Fazli, Hamid R. Rabiee
2021 arXiv   pre-print
Therefore, verifying social media news and spotting fakes is crucial. This work aims to analyze multi-modal features from texts and images in social media for detecting fake news.  ...  The results show the proposed method achieves higher accuracies in detecting fake news compared to the previous works.  ...  Then, we focus on the recent multi-modals fake news methods.  ... 
arXiv:2112.01131v1 fatcat:mdnofls76jbdnakq7gu2jcargu

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

Sara Abdali
2022 arXiv   pre-print
field of multi-modal misinformation detection.  ...  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  ...  fake news detection.  ... 
arXiv:2203.13883v3 fatcat:ari4onbo45ejfnwdnjgdti5daq

NoFake at CheckThat! 2021: Fake News Detection Using BERT [article]

Sushma Kumari
2021 arXiv   pre-print
Much research has been done for debunking and analysing fake news. Many researchers study fake news detection in the last year, but many are limited to social media data.  ...  Also, multiple fact-checkers use different labels for the fake news, making it difficult to make a generalisable classifier.  ...  The task is divided into two sub-task, they are: Subtask 3A: Multi-class fake news detection of news articles (English): Sub-task would be the classification of news articles in four classes as defined  ... 
arXiv:2108.05419v1 fatcat:7csh3ufgczgwbj5pfyas3mevaq
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