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Transformer-Graph Neural Network with Global-Local Attention for Multimodal Rumour Detection with Knowledge Distillation [article]

Tsun-hin Cheung, Kin-man Lam
2022 arXiv   pre-print
Then, we extend the proposed TGNN for multimodal rumour detection, by considering the latent relationship between the multimodal feature and node feature to form a more comprehensive graph representation  ...  To verify the effectiveness of our proposed method for multimodal rumour detection, we extend the existing PHEME-2016, PHEME-2018, and Weibo data sets, by collecting available and relevant images for training  ...  This is likely due to the fact that the handcrafted features cannot provide a robust representation for detecting rumours.  ... 
arXiv:2206.04832v1 fatcat:modpwsu54nbzpii4kfokqpd3ve

No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection [article]

Debanjana Kar, Mohit Bhardwaj, Suranjana Samanta, Amar Prakash Azad
2020 arXiv   pre-print
In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection.  ...  We also propose a zero-shot learning approach to alleviate the data scarcity issue for such low resource languages.  ...  The details are described below. A.  ... 
arXiv:2010.06906v1 fatcat:fyfih46qcrh3teazrf6dahtlhq

Bank distress in the news: Describing events through deep learning [article]

Samuel Rönnqvist, Peter Sarlin
2016 arXiv   pre-print
We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events.  ...  Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data.  ...  Acknowledgment The authors are grateful to Filip Ginter, József Mezei, Tuomas Peltonen and Niko Schenk for their helpful comments.  ... 
arXiv:1603.05670v1 fatcat:dm5pcjrltjaithufjp2f2mwhfy

Detect & Describe: Deep learning of bank stress in the news [article]

Samuel Rönnqvist, Peter Sarlin
2015 arXiv   pre-print
The method offers insight that models based on other types of data cannot provide, while offering a general means for interpreting this type of semantic-predictive model.  ...  Through unsupervised training, we learn semantic vector representations of news articles as predictors of distress events.  ...  ACKNOWLEDGMENT The authors are grateful to Filip Ginter, József Mezei and Tuomas Peltonen for their helpful comments.  ... 
arXiv:1507.07870v1 fatcat:vkwqgtyj3nfkdawoxuwoyj357i

Detect & Describe: Deep Learning of Bank Stress in the News

Samuel Ronnqvist, Peter Sarlin
2015 2015 IEEE Symposium Series on Computational Intelligence  
The method offers insight that models based on other types of data cannot provide, while offering a general means for interpreting this type of semantic-predictive model.  ...  Through unsupervised training, we learn semantic vector representations of news articles as predictors of distress events.  ...  ACKNOWLEDGMENT The authors are grateful to Filip Ginter, József Mezei and Tuomas Peltonen for their helpful comments.  ... 
doi:10.1109/ssci.2015.131 dblp:conf/ssci/RonnqvistS15 fatcat:s6fdai4iabeqjawpkfrqeq4geq

A hybrid model for fake news detection: Leveraging news content and user comments in fake news

Marwan Albahar
2021 IET Information Security  
Nowadays, social media platforms such as Twitter have become a popular medium for people to spread and consume news because of their easy access and the rapid proliferation of news.  ...  Detecting such news on social media platforms has become a challenging task. One of the main challenges is identifying useful information that is exploited as a way to detect fake news.  ...  The combination of a multilayer perceptron representation and handcrafted features from the FNC-1 dataset was proposed in [23] for stance detection.  ... 
doi:10.1049/ise2.12021 fatcat:kz2oi5dkqzfnjcdfozhsfse4si

We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!

Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
Let us face it: everybody hates them for three simple reasons. Reason #2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind!  ...  So, we did this research on fake news/click-bait detection and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever!  ...  Acknowledgements We would like to thank Lachezar Bozhkov, who was part of our team in the Hack the Fake News hackathon, for his insight. This work is supported by the NSF of Bulgaria under Grant No.  ... 
doi:10.26615/978-954-452-049-6_045 dblp:conf/ranlp/KaradzhovGNK17 fatcat:lxfe3sujtjcjbo7icxpfocpkoq

NewsBag: A Benchmark Multimodal Dataset for Fake News Detection

Sarthak Jindal, Raghav Sood, Richa Singh, Mayank Vatsa, Tanmoy Chakraborty
2020 AAAI Conference on Artificial Intelligence  
Fake news detection is an arduous task, marred by the lack of a robust ground truth database for training classification models.  ...  We propose two novel benchmark multimodal datasets, consisting of text and images, to enhance the quality of fake news detection.  ...  Rumour detection on microblogs (Jin et al. 2017 ) is another form of fake news detection.  ... 
dblp:conf/aaai/JindalS0V020 fatcat:ajthnxda4zecdnqyj6dq6mvvxe

Is Dynamic Rumor Detection on social media Viable? An Unsupervised Perspective [article]

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques.  ...  There is a lack of credibility assessment techniques for online posts to identify rumors as soon as they arrive.  ...  In section 5, we conclude by discussing future prospects. [16] analyze a list of 81 handcrafted features for multimedia-based rumor classification.  ... 
arXiv:2111.11982v1 fatcat:okmuc5c7pzb43l6h432fu474qe

A unified approach for detection of Clickbait videos on YouTube using cognitive evidences

Deepika Varshney, Dinesh Kumar Vishwakarma
2021 Applied intelligence (Boston)  
The scheme leverages to learn three sets of latent features based on User Profiling, Video-Content, and Human Consensus, these are further used to retrieve cognitive evidence for the detection of clickbait  ...  The achieved result is compared with other state-of-the-art methods and demonstrates superior performance.  ...  In contrast, the authors of [24] , learn a hidden representation of the input, by employing a recurrent neural network, without the need for hand-crafted features for rumour classification.  ... 
doi:10.1007/s10489-020-02057-9 pmid:34764575 pmcid:PMC7778503 fatcat:4kxqidmfszcsfetaam4phlnpae

Where to apply dropout in recurrent neural networks for handwriting recognition?

Theodore Bluche, Christopher Kermorvant, Jerome Louradour
2015 2015 13th International Conference on Document Analysis and Recognition (ICDAR)  
Repeating the procedure for each training example, it is equivalent to sample a network from an exponential number of architectures that share weights.  ...  We recently proposed a way to use dropout in MDLSTM-RNNs for handwritten word and line recognition.  ...  Recurrent Neural Networks For the two types of input features (handcrafted and pixels), we trained Bidirectional LSTM-RNNs.  ... 
doi:10.1109/icdar.2015.7333848 dblp:conf/icdar/BlucheKL15 fatcat:g3r72wudtffsdkmfiefkdjyvii

Following the Trail of Fake News Spreaders in Social Media: A Deep Learning Model

Antonela Tommasel, Juan Manuel Rodriguez, Filippo Menczer
2022 User Modeling, Adaptation, and Personalization  
In this context, this paper presents an initial proof of concept of a deep learning model for identifying fake news spreaders in social media, focusing not only on the characteristics of the shared content  ...  For more than ten years, there have been concerns regarding the manipulation of public opinion through the social Web.  ...  Sharma and Sharma [22] combined user features with an average word2vec representation of content for rumour spreader detection.  ... 
doi:10.1145/3511047.3536410 dblp:conf/um/TommaselRM22 fatcat:u55c7bfpcjdt7dhsx3ci44oi6e

The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers [chapter]

Anastasia Giachanou, Esteban A. Ríssola, Bilal Ghanem, Fabio Crestani, Paolo Rosso
2020 Lecture Notes in Computer Science  
Fake news are written in a way to confuse readers and therefore understanding which articles contain fabricated information is very challenging for non-experts.  ...  Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally.  ...  The work of the first author is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441).  ... 
doi:10.1007/978-3-030-51310-8_17 fatcat:u6anynvfrvhzjkbb52244nouj4

Adversarial Domain Adaptation for Stance Detection [article]

Brian Xu, Mitra Mohtarami, James Glass
2019 arXiv   pre-print
Stance detection is a major component of automated fact checking.  ...  This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim.  ...  Conclusion We present a model that uses adversarial domain adaptation for the task of stance detection.  ... 
arXiv:1902.02401v1 fatcat:sgdbi4d5t5hh5nql3nnx36ma34

A Novel Stance based Sampling for Imbalanced Data

Isha Agarwal, Dipti Rana, Aemie Jariwala, Sahil Bondre
2022 International Journal of Advanced Computer Science and Applications  
In this paper, we propose a stance based sampling method for balancing news data.  ...  However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant.  ...  There are two main models of BERT -BERT Base and BERT Large and mBERT is the BERT representation for multilingual representation.  ... 
doi:10.14569/ijacsa.2022.0130157 fatcat:slvhdhok7zf3dccscghb5xtdeq
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