Rumor Detection on Social Media with Out-In-Degree Graph Convolutional Networks

2021 ◽  
Author(s):  
Shihui Song ◽  
Yafan Huang ◽  
Hongwei Lu
Keyword(s):  
Social Media ◽  
Convolutional Networks ◽  
Degree Graph ◽  
Rumor Detection

scholarly journals Dynamic graph convolutional networks with attention mechanism for rumor detection on social media

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256039
Author(s):  
Jiho Choi ◽  
Taewook Ko ◽  
Younhyuk Choi ◽  
Hyungho Byun ◽  
Chong-kwon Kim
Keyword(s):  
Social Media ◽  
Real World ◽  
Structural Information ◽  
Attention Mechanism ◽  
Temporal Information ◽  
Detection Methods ◽  
Dynamic Graph ◽  
Convolutional Networks ◽  
Real World Datasets ◽  
Rumor Detection

Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. However, these methods are limited to represent rumor propagation as a static graph, which isn’t optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, named Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task.

scholarly journals Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

2020 ◽  
Vol 34 (01) ◽  
pp. 549-556 ◽  
Author(s):  
Tian Bian ◽  
Xi Xiao ◽  
Tingyang Xu ◽  
Peilin Zhao ◽  
Wenbing Huang ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Directed Graph ◽  
Graph Model ◽  
Top Down ◽  
Learning Methods ◽  
Convolutional Networks ◽  
Rumor Propagation ◽  
New Information ◽  
Rumor Detection

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation; and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

scholarly journals Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

2020 ◽  
Author(s):  
Liang Zhang ◽  
Jingqun Li ◽  
Bin Zhou ◽  
Yan Jia
Keyword(s):  
Neural Networks ◽  
Numerical Experiments ◽  
State Of The Art ◽  
Fake News ◽  
Wide Spread ◽  
Convolutional Networks ◽  
The Media ◽  
Graph Neural Networks ◽  
Rumor Detection ◽  
Simple Network

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed to detect rumors. In this work, we study the application of graph neural networks for the task of rumor detection, and present a simplified new architecture to classify rumors. Numerical experiments show that the proposed simple network has comparable to or even better performance than state-of-the art graph convolutional networks, while having significantly reduced the computational complexity.

scholarly journals Rumor Detection on Social Media: Datasets, Methods and Opportunities

2019 ◽  
Author(s):  
Quanzhi Li ◽  
Qiong Zhang ◽  
Luo Si ◽  
Yingchi Liu
Keyword(s):  
Social Media ◽  
Rumor Detection

scholarly journals Rumor detection in social media based on a hierarchical attention network

2018 ◽  
Vol 48 (11) ◽  
pp. 1558-1574 ◽  
Author(s):  
Xueqi CHENG ◽  
Xiangwen LIAO ◽  
Zhi HUANG ◽  
Guolong CHEN ◽  
Dingda YANG
Keyword(s):  
Social Media ◽  
Attention Network ◽  
Rumor Detection

Rumor Detection on Twitter Using a Supervised Machine Learning Framework

2021 ◽  
pp. 452-465
Author(s):  
Hardeo Kumar Thakur ◽  
Anand Gupta ◽  
Ayushi Bhardwaj ◽  
Devanshi Verma
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Supervised Machine Learning ◽  
Learning Framework ◽  
Online Social Media ◽  
Rumor Propagation ◽  
Multiple Classification ◽  
The Government ◽  
Rumor Detection

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work, analyzing temporal and linguistic characteristics of rumors seems to give ample time for rumor propagation. Meanwhile, with the huge outburst of data on social media, studying these characteristics for each tweet becomes spatially complex. Therefore, in this article, a two-fold supervised machine-learning framework is proposed that detects rumors by filtering and then analyzing their linguistic properties. This method attempts to automate filtering by training multiple classification algorithms with accuracy higher than 81.079%. Finally, using textual characteristics on the filtered data, rumors are detected. The effectiveness of the proposed framework is shown through extensive experiments on over 10,000 tweets.

Multimodal Disentangled Domain Adaption for Social Media Event Rumor Detection

2020 ◽  
pp. 1-1
Author(s):  
Huaiwen Zhang ◽  
Shengsheng Qian ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Domain Adaption ◽  
Rumor Detection ◽  
Media Event

scholarly journals An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Meicheng Guo ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Yujun Zhang
Keyword(s):  
Social Media ◽  
Parameter Tuning ◽  
Training Data ◽  
Detection Methods ◽  
Transfer Model ◽  
Detection Model ◽  
Adaptive Parameter ◽  
Social Media Platforms ◽  
Real World Datasets ◽  
Rumor Detection

With the extensive usage of social media platforms, spam information, especially rumors, has become a serious problem of social network platforms. The rumors make it difficult for people to get credible information from Internet and cause social panic. Existing detection methods always rely on a large amount of training data. However, the number of the identified rumors is always insufficient for developing a stable detection model. To handle this problem, we proposed a deep transfer model to achieve accurate rumor detection in social media platforms. In detail, an adaptive parameter tuning method is proposed to solve the negative transferring problem in the parameter transferring process. Experiments based on real-world datasets demonstrate that the proposed model achieves more accurate rumor detection and significantly outperforms state-of-the-art rumor detection models.

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