Identity alignment algorithm across social networks based on attention mechanism

2021 ◽  
Author(s):  
Xinlan Wang ◽  
Xiaodong Cai ◽  
Qingsong Zhou ◽  
Tao Hong
Keyword(s):  
Social Networks ◽  
Attention Mechanism ◽  
Alignment Algorithm

scholarly journals Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 171
Author(s):  
Yong Fang ◽  
Shaoshuai Yang ◽  
Bin Zhao ◽  
Cheng Huang
Keyword(s):  
Social Networks ◽  
Attention Mechanism ◽  
The Self ◽  
Complete Model ◽  
Model Combining ◽  
Proposed Model ◽  
Classification Tasks ◽  
Intelligent Models ◽  
Cyberbullying Detection ◽  
Gated Recurrent Unit

With the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying detection because it is the commonly used information carrier in social networks and is the widely used feature in this regard studies. Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism. In detail, we introduce the design of a GRU cell and Bi-GRU’s advantage for learning the underlying relationships between words from both directions. Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks. The proposed model could address the limitation of the vanishing and exploding gradient problems. We avoid using oversampling or downsampling on experimental data which could result in the overestimation of evaluation. We conduct a comparative assessment on two commonly used datasets, and the results show that our proposed method outperformed baselines in all evaluation metrics.

scholarly journals Detection of Misinformation about COVID-19 in Brazilian Portuguese WhatsApp Messages Using Deep Learning

2021 ◽  
Author(s):  
Antônio Diogo Forte Martins ◽  
Lucas Cabral ◽  
Pedro Jorge Chaves Mourão ◽  
José Maria Monteiro ◽  
Javam Machado
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Developing Countries ◽  
Deep Learning ◽  
Brazilian Portuguese ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Primary Sources ◽  
New Approach ◽  
Health Advice

During the COVID-19 pandemic, the misinformation problem arose once again through social networks, like a harmful health advice and false solutions epidemic. In Brazil, as well as in many developing countries, one of the primary sources of misinformation is the messaging application WhatsApp. Thus, the automatic misinformation detection (MID) about COVID-19 in Brazilian Portuguese WhatsApp messages becomes a crucial challenge. Still, due to WhatsApp's private messaging nature, there are still few methods of misinformation detection developed specifically for the WhatsApp platform. In this paper, we propose a new approach, called MIDeepBR, based on BiLSTM neural networks, pooling operations and attention mechanism, which is able to automatically detect misinformation in Brazilian Portuguese WhatsApp messages. Experimental results evidence the suitability of the proposed approach to automatic misinformation detection. Our best results achieved an F1 score of 0.834, while in previous works, the best results achieved an F1 score of 0.778. Thus, MIDeepBR outperforms the previous works.

scholarly journals A Multifeature Complementary Attention Mechanism for Image Topic Representation in Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Shi ◽  
Jia Luo ◽  
Gang Cheng ◽  
Xia Liu ◽  
Gang Xie
Keyword(s):  
Social Networks ◽  
Semantic Information ◽  
Image Data ◽  
Image Features ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Sina Weibo ◽  
Evaluation Measures ◽  
Optimal Accuracy ◽  
Object Features

Image topic representation in social networks is vital for people to get significant and valuable content. However, this task is difficult and challenging due to the complexity of image features. This paper proposes a multifeature complementary attention mechanism for image topic representation named CATR. CATR uses scene-level and instance-level object detection methods to obtain the object information on social networks. Here, the image features are divided into focused features and unfocused features. Focused features are used to learn and express semantic information, while unfocused features are used to filter out noise information in focused feature extraction. The attention mechanism is constructed by combining the object features and the features of the image itself, while the image topic representation in social networks is realized by the complementary attention mechanism. Based on the real image data of Sina Weibo and Mir-Flickr 25K, several groups of comparative experiments are constructed to verify the performance of the proposed CATR by leveraging different evaluation measures. The experimental results demonstrate that the proposed CATR obtains an optimal accuracy and significantly outperforms the other comparison methods in image topic representation.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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