scholarly journals GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dawei Xu ◽  
Qing Liu ◽  
Liehuang Zhu ◽  
Zhonghua Tan ◽  
Feng Gao ◽  
...  
Keyword(s):  
Social Network ◽  
Mobile Computing ◽  
Network Topology ◽  
Resource Sharing ◽  
New Technology ◽  
Convolutional Network ◽  
Detection Model ◽  
Mobile Computing Environment ◽  
Other Information ◽  
Rumor Detection

Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.

scholarly journals Multilevel Attention Residual Neural Network for Multimodal Online Social Network Rumor Detection

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuang Wang ◽  
Jie Sui
Keyword(s):  
Neural Network ◽  
Social Network ◽  
State Of The Art ◽  
Online Social Network ◽  
Image Representation ◽  
Rapid Rise ◽  
Text And Image ◽  
Detection Model ◽  
Fused Image ◽  
Rumor Detection

In recent years, with the rapid rise of social networks, such as Weibo and Twitter, multimodal social network rumors have also spread. Unlike traditional unimodal rumor detection, the main difficulty of multimodal rumor detection is in avoiding the generation of noise information while using the complementarity of different modal features. In this article, we propose a multimodal online social network rumor detection model based on the multilevel attention residual neural network (MARN). First, the features of text and image are extracted by Bert and ResNet-18, respectively, and the cross-attention residual mechanism is used to enhance the representation of images with a text vector. Second, the enhanced image vector and text vector are concatenated and fused by the self-attention residual mechanism. Finally, the fused image–text vectors are classified into two categories. Among them, the attention mechanism can effectively enhance the image representation and further improve the fusion effect between the image and the text, while the residual mechanism retains the unique attributes of each original modal feature while using different modal features. To assess the performance of the MARN model, we conduct experiments on the Weibo dataset, and the results show that the MARN model outperforms the state-of-the-art models in terms of accuracy and F1 value.

scholarly journals An End-to-End Rumor Detection Model Based on Feature Aggregation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Aoshuang Ye ◽  
Lina Wang ◽  
Run Wang ◽  
Wenqi Wang ◽  
Jianpeng Ke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Language Processing ◽  
Domain Knowledge ◽  
Learning Ability ◽  
Feature Engineering ◽  
Learning Technology ◽  
Detection Model ◽  
Feature Aggregation ◽  
Rumor Detection

The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works.

Experience with Disconnected Operation in a Mobile Computing Environment

1993 ◽  
Author(s):  
M. Satyanarayanan ◽  
James J. Kistler ◽  
Lily B. Mummert ◽  
Maria R. Ebling ◽  
Puneet Kumar
Keyword(s):  
Mobile Computing ◽  
Computing Environment ◽  
Disconnected Operation ◽  
Mobile Computing Environment

scholarly journals A Study of Rumor Detection based on Social Network Topic Models Relationship

2020 ◽  
Author(s):  
Diogo Nolasco ◽  
Jonice Oliveira
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Community ◽  
Topic Model ◽  
Fake News ◽  
Detection Problem ◽  
Model Method ◽  
Show Evidence ◽  
Rumor Detection ◽  
Scientific Topic

The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a scientific topic is a rumor. We propose the use of a topic model method on social and scientific domains and correlate the topics found to detect the most prone to be rumors. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions.

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