Filters








2,423 Hits in 5.9 sec

A Survey on Various Methods to Detect Rumors on Social Media

2020 Computer Engineering and Intelligent Systems  
This paper is an introduction to rumor recognition via social networking media which presents the essential wording and kinds of bits of rumor and the nonexclusive procedure of rumor detection.  ...  A cutting edge portraying the utilization of directed ML algorithms for rumor detection via Social networking media is introduced.  ...  The module named as 'capture' leverages Long Short Term Memory networks (LSTM). This module captures the temporal text and temporal activity of a user pertaining to a given article.  ... 
doi:10.7176/ceis/11-4-01 fatcat:u5i7p4wz2nfxbbmvedmekjjwim

Analysis of Techniques for Rumor Detection in Social Media

Ajeet Ram Pathak, Aditee Mahajan, Keshav Singh, Aishwarya Patil, Anusha Nair
2020 Procedia Computer Science  
Motivated by the same, this paper focuses on detailed discussion of datasets and state-of-the-art approaches of rumor detection.  ...  The openness and unrestricted way to share the information on social media platforms fosters information spread across the network regardless of its credibility.  ...  Long Short Term Memory RNN is employed to extract temporal representations of articles so userfeatures area unit fed into totally connected layer to reason a score.  ... 
doi:10.1016/j.procs.2020.03.281 fatcat:olei4mxf3rbhhgttbzyff4iz7a

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Md Rafiqul Islam, Shaowu Liu, Xianzhi Wang, Guandong Xu
2020 Social Network Analysis and Mining  
The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection.  ...  Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation  ...  However, due to differences in performance, we only discussed 12 models namely convolutional neural networks recurrent neural networks (CRNN), ensemble-based fusion (EBF), and long short-term memory (  ... 
doi:10.1007/s13278-020-00696-x pmid:33014173 pmcid:PMC7524036 fatcat:473ziygl7jffbhwvpav3hlmppu

Deep Learning Based Rumor Detection on Microblogging Platforms: A Systematic Review

Mohammed Al-Sarem, Wadii Boulila, Muna Al-Harby, Junaid Qadir, Abdullah Alsaeedi
2019 IEEE Access  
Recently, several research studies have been investigated to control online rumors automatically by mining rich text available on the open network with deep learning techniques.  ...  With the rapid increase in the popularity of social networks, the propagation of rumors is also increasing.  ...  LONG SHORT-TERM MEMORY Long Short-Term Memory (LSTM) uses feedback connections to process an entire sequence of data.  ... 
doi:10.1109/access.2019.2947855 fatcat:dltss2k2yjgpzcjwubbmhfrbbu

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

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
Early detection of online rumors is a challenging task, and studies relating to them are relatively few. It is the need of the hour to identify rumors as soon as they appear online.  ...  The proposed method, being lightweight, simple, and robust, offers the suitability of being adopted as a tool for online rumor identification.  ...  , Recurrent Neural Network (RNN), and Long Short-Term Memory network (LSTM) depending upon time-series representations of Twitter data.  ... 
arXiv:2111.11982v1 fatcat:okmuc5c7pzb43l6h432fu474qe

On Early-Stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners [chapter]

Tu Ngoc Nguyen, Cheng Li, Claudia Niederée
2017 Lecture Notes in Computer Science  
One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are -due to their accumulating characteristic -not very helpful in the early phase of  ...  In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights  ...  Using Long Short-Term Memory RNNs: RNN are able to propagate historical information via a chain-like neural network architecture.  ... 
doi:10.1007/978-3-319-67256-4_13 fatcat:dpbgb5aj3jd7njopxl7zji2bvm

A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems [article]

Mohammadsepehr Karimiziarani
2022 arXiv   pre-print
In recent years, social media has become one of the most popular platforms for communication.  ...  In this paper, a survey on the potential benefits and applications of event detection with social media data analysis will be presented.  ...  Recurrent neural networks (RNNs), which include a memory state capable of learning long-distance relationships, are the LONG SHORT-TERM MEMORY network (LSTM) [85] and the GATED RECURRENT UNITS (GRU)  ... 
arXiv:2207.03997v2 fatcat:nzymdx4l7veofdq33u7ugmnjfq

Fake Information Detection Techniques

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
In this paper we present survey on the specific techniques to computerized detection of fake news proposed in the latest literature. Particularly, this paper focus on five main aspects.  ...  Second, we highlight how the collection of applicable data for simulation of fake information detection is tough and we present the numerous approaches, which have been adopted to accumulate this information  ...  This structure makes RNNs, and mainly long short-term memory (LSTM) networks, especially powerful for modeling sequential facts, consisting of for example the human language, and capturing relevant features  ... 
doi:10.35940/ijitee.g1004.0597s20 fatcat:gpn46edlmzerxlmlrbazxifnxy

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

Qi Su, Mingyu Wan, Xiaoqian Liu, Chu-Ren Huang
2020 Natural Language Processing Research  
A B S T R A C T The rise of misinformation online and offline reveals the erosion of long-standing institutional bulwarks against its propagation in the digitized era.  ...  Advantages and disadvantages of the key techniques are also addressed with focuses on content-based analysis and predicative modeling.  ...  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

Early Detection of Rumours on Twitter via Stance Transfer Learning [chapter]

Lin Tian, Xiuzhen Zhang, Yan Wang, Huan Liu
2020 Lecture Notes in Computer Science  
Experiments on real-world rumour datasets show that our BERT-based model can achieve effective early rumour detection and significantly outperform start-of-the-art rumour detection models.  ...  In this paper we focus on early detection of rumours when data for information sources or propagation is scarce.  ...  The attitude representation for comments are then integrated into a CNN-biLSTM (bi-directional Long Short Term Memory) model for rumour prediction for tweets with comments.  ... 
doi:10.1007/978-3-030-45439-5_38 fatcat:45kaddfe7vbfncjcwnod2rxh24

Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights

Simone Raponi, Zeinab Khalifa, Gabriele Oligeri, Roberto Di Pietro
2022 ACM Transactions on the Web  
Fake news propagation is a complex phenomenon influenced by a multitude of factors whose identification and impact assessment is challenging.  ...  Although many models have been proposed in the literature, the one capturing all the properties of a real fake-news propagation phenomenon is inevitably still missing.  ...  The information and views set out in this publication are those of the authors and do not necessarily relect the oicial opinion of the QNRF.  ... 
doi:10.1145/3522756 fatcat:y7d7xvt2bnfd5mbunbcvqkwsou

Combating Fake News: A Survey on Identification and Mitigation Techniques [article]

Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, Yan Liu
2019 arXiv   pre-print
While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in  ...  The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion  ...  ACKNOWLEDGMENTS We thank the reviewers and moderators for their invaluable comments and inputs on earlier versions of this manuscript.  ... 
arXiv:1901.06437v1 fatcat:xa2ecuhp4fcy5jetoiz5qchg6a

Selected Ph.D. Thesis Abstracts

Xin Li
2017 The IEEE intelligent informatics bulletin  
To fix the gap in real-time situation, we propose an early detection mechanism to monitor and identify rumors in the online streaming social media as early as possible.  ...  In this thesis, we study multi-document summarization (MDS) on long texts and multi-document summarization on short texts, and propose several multi-document summarization algorithms based on patterns  ... 
dblp:journals/cib/Li17 fatcat:3rekqnvlozdwjcyt4hs3uyipom

Fully Automated Fact Checking Using External Sources [article]

Georgi Karadzhov, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Ivan Koychev
2017 arXiv   pre-print
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims  ...  The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.  ...  The features we use are dense representations of the claim, of the snippets and of related sentences from the Web pages, which we automatically train for the task using Long Short-Term Memory networks  ... 
arXiv:1710.00341v1 fatcat:y4o6yx5gdrak3omach2xujdlcm

Fully Automated Fact Checking Using External Sources

Georgi Karadzhov, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Ivan Koychev
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims  ...  The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.  ...  The features we use are dense representations of the claim, of the snippets and of related sentences from the Web pages, which we automatically train for the task using Long Short-Term Memory networks  ... 
doi:10.26615/978-954-452-049-6_046 dblp:conf/ranlp/KaradzhovNMBK17 fatcat:wrjpfmacu5hf7bxxdwpyfywhra
« Previous Showing results 1 — 15 out of 2,423 results