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Semi-supervised learning and graph neural networks for fake news detection

Adrien Benamira, Benjamin Devillers, Etienne Lesot, Ayush K. Ray, Manal Saadi, Fragkiskos D. Malliaros
2019 Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining  
To this extend, we opted for semi-supervised learning approaches. In particular, our work proposes a graph-based semi-supervised fake news detection method, based on graph neural networks.  ...  In this work, we focus on content-based methods for detecting fake news -casting the problem to a binary text classification one (an article corresponds to either fake news or not).  ...  In particular, we propose a graph-based semi-supervised fake news detection framework, building upon network representation learning techniques [21] .  ... 
doi:10.1145/3341161.3342958 dblp:conf/asunam/BenamiraDLRSM19 fatcat:llv66xw5bvcjbfb7n7ijb4s6ce

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

Qi Su, Mingyu Wan, Xiaoqian Liu, Chu-Ren Huang
2020 Natural Language Processing Research  
However, it remains a challenging problem for the Natural Language Processing community.  ...  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  ...  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

AMUSED: An Annotation Framework of Multi-modal Social Media Data [article]

Gautam Kishore Shahi
2021 arXiv   pre-print
In this paper, we present a semi-automated framework called AMUSED for gathering multi-modal annotated data from the multiple social media platforms.  ...  AMUSED can be applied in multiple application domains, as a use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media platforms.  ...  To build a supervised or semi-supervised model on social media data, researchers face two challenges-timely data collection and data annotation [30] .  ... 
arXiv:2010.00502v2 fatcat:7nbgyjox4bhwhio5yn32wspumi

Preface to the Special Issue on Graph Data Management in Online Social Networks

Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, Junping Du
2020 World wide web (Bussum)  
Li et al. in "Semi-supervised Clustering with Deep Metric Learning and Graph Embedding" propose a novel semi-supervised clustering approach based on deep metric learning and graph embedding, which enhances  ...  in modelling, storing, querying, and learning graph data has been found particular useful in online social network (OSN) analysis, such as expert finding, social community mining and social position detection  ...  Yin et al. in "Matching of Social Events and Users: A Two-Way Selection Perspective" propose a novel two-stage framework for social event participation analysis by adapting the classic Gale-Shapley algorithm  ... 
doi:10.1007/s11280-019-00771-0 fatcat:ee7b2an4xzc77clj2w36oyl4zq

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

Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu
2020 arXiv   pre-print
; (2) describing important and emerging tasks to combat disinformation for characterization, detection and attribution; and (3) discussing a weak supervision approach to detect disinformation with limited  ...  We hope this book to be a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly  ...  In the Chapter 7, it proposes to detect fake news and misinformation using semi-supervised learning.  ... 
arXiv:2001.00623v1 fatcat:zcmgzbudjvab3fckajrmrbppoy

Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media [article]

Hamidreza Alvari, Elham Shaabani, Soumajyoti Sarkar, Ghazaleh Beigi, Paulo Shakarian
2019 arXiv   pre-print
In this paper, we propose a semi-supervised causal inference PSM detection framework, SemiPsm, to compensate for the lack of labeled data.  ...  Evidence from empirical experiments on a real-world ISIS-related dataset from Twitter suggests promising results of utilizing unlabeled instances for detecting PSMs.  ...  CONCLUSION We presented a semi-supervised Laplacian SVM to detect PSM users in social media who are promoters of misinformation spread.  ... 
arXiv:1903.01693v1 fatcat:qfqeeiknmndtlbyukdcxwh37ra

VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media [article]

Yizhou Zhang, Karishma Sharma, Yan Liu
2021 arXiv   pre-print
Experimental results on a real-world dataset show the effectiveness of our proposed method compared to the SOTA model in both unsupervised and semi-supervised settings.  ...  Consequently, there is an urgent need to develop an effective methodology for coordinated group detection to combat the misinformation on social media.  ...  We sincerely thank Professor Emilio Ferrara and his group for sharing the IRA dataset with us. Also, we are very thankful for the comments and suggestions from our anonymous reviewers.  ... 
arXiv:2110.15454v1 fatcat:ugt5djpvzffy3gbnqgirpfbq6e

Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition [article]

Sara Abdali, Neil Shah, Evangelos E. Papalexakis
2021 arXiv   pre-print
The vast majority of the state-of-the-art in detecting misinformation is fully supervised, requiring a large number of high-quality human annotations.  ...  However, the availability of such annotations cannot be taken for granted, since it is very costly, time-consuming, and challenging to do so in a way that keeps up with the proliferation of misinformation  ...  In this work, we leverage belief propagation to address the semi-supervised problem of misinformation detection.  ... 
arXiv:2005.04310v2 fatcat:bhll6g5zznhqxjsdjs2kez7s5i

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

Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, Zhiwen Yu
2019 arXiv   pre-print
Misinformation detection (MID) has thus become a surging research topic in recent years.  ...  The massive spread of misinformation in social networks has become a global risk, implicitly influencing public opinion and threatening social/political development.  ...  However, in many cases we can only have a small number of labels. Semi-supervised models are o en leveraged for dealing with the label sparsity issue. For example, Guacho et al.  ... 
arXiv:1909.03654v1 fatcat:34h2os2pzrbm3kqluk5uajtr6i

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
We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets.  ...  Additionally, we propose Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model, which leverages the large amount of unlabelled data.  ...  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

Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives [article]

Sajad Dadgar, Mehdi Ghatee
2021 arXiv   pre-print
To tackle this problem, we present two COVID-19 related misinformation datasets on Twitter and propose a misinformation detection system comprising network-based and content-based processes based on machine  ...  Finally, we develop a fact-checking website called Checkovid that uses each process to detect misinformative and informative claims in the domain of COVID-19 from different perspectives.  ...  As a result of limited labeled datasets, most studies developed semi-supervised machine learning models for misinformation classification.  ... 
arXiv:2107.09768v1 fatcat:qfwp6grs6zab5glchyx34bnyxy

A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection [article]

Fahim Belal Mahmud, Mahi Md. Sadek Rayhan, Mahdi Hasan Shuvo, Islam Sadia, Md.Kishor Morol
2022 arXiv   pre-print
Therefore, in this paper, we present a comparative analysis among some commonly used machine learning algorithms and Graph Neural Networks for detecting the spread of false news on social media platforms  ...  Most of the existing fake news detection algorithms are solely focused on the news content only but engaged users prior posts or social activities provide a wealth of information about their views on news  ...  On social network datasets with a large number of training graphs, GINs shine. Benamira et al. [21] Proposed a combined algorithm by GNN & Semi-supervised algorithm.  ... 
arXiv:2203.14132v1 fatcat:okudbjyu65enjhxk4ss67f65uu

Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation [article]

Arkin Dharawat and Ismini Lourentzou and Alex Morales and ChengXiang Zhai
2022 arXiv   pre-print
In this work, we frame health misinformation as a risk assessment task.  ...  Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts.  ...  To alleviate the need for large training sets, future research could focus on weakly-supervised, semi-supervised, and selfsupervised algorithms.  ... 
arXiv:2010.08743v2 fatcat:nktwimcqwfc4dm6a5umgmjubra

A Survey Paper on Fake Review Detection System [chapter]

Nikita V. Khairnar, Pimpri Chinchwad College of Engineering, Nigdi, Pune, India, Shruti L. Mankar, Mrunali R. Pandav, Hitesh Kotecha, Manjiri Ranjanikar
2021 New Frontiers in Communication and Intelligent Systems  
Fake reviews can be used to demote a good product or to promote a bad product, so there is a need for robust and reliable techniques to detect fake reviews which can be beneficial to the customer as well  ...  This research presents a systematic review on methods to detect spam review using different Deep Learning (DL) Approaches, Machine Learning (ML) Methods, Natural Language Processing (NLP), and Sentiment  ...  For machine learning-based proposed solutions we have discussed supervised, unsupervised, semi-supervised as well as ensemble learning-based methodologies.  ... 
doi:10.52458/978-81-95502-00-4-64 fatcat:whu5r3v5hrdknc2kbwyyv3wlyi

Human-Misinformation interaction: Understanding the interdisciplinary approach needed to computationally combat false information [article]

Alireza Karduni
2019 arXiv   pre-print
I adopt a framework for studying misinformation that suggests paying attention to the source, content, and consumers as the three main elements involved in the process of misinformation and I provide an  ...  Using the framework, I overview the existing computational methods that deal with 1) misinformation detection and fact-checking using Content 2) Identifying untrustworthy Sources and social bots, and 3  ...  A FRAMEWORK FOR STUDYING MISINFORMATION In this section, I propose a framework for studying and categorizing misinformation.  ... 
arXiv:1903.07136v1 fatcat:4xvcnuvqobag7o7pknadnrhymi
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