scholarly journals Face representation learning and its applications on social media

2018 ◽  
Author(s):  
Wang
Keyword(s):  
Social Media ◽  
Representation Learning ◽  
Face Representation

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 Social Media-based User Embedding: A Literature Review

2019 ◽  
Author(s):  
Shimei Pan ◽  
Tao Ding
Keyword(s):  
Social Media ◽  
High Performance ◽  
Large Scale ◽  
Ground Truth ◽  
Representation Learning ◽  
Success Stories ◽  
Recent Success ◽  
User Data ◽  
Low Dimensional ◽  
And Behavior

Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions.

Deep Face-Representation Learning for Kinship Verification

2018 ◽  
pp. 127-152
Author(s):  
Naman Kohli ◽  
Daksha Yadav ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
Afzel Noore
Keyword(s):  
Representation Learning ◽  
Kinship Verification ◽  
Face Representation

EXPRESS: Identifying Market Structure: A Deep Network Representation Learning of Social Engagement

2021 ◽  
pp. 002224292110335
Author(s):  
Yi Yang ◽  
Kunpeng Zhang ◽  
P.K. Kannan
Keyword(s):  
Social Media ◽  
Market Structure ◽  
Social Engagement ◽  
Dimensional Space ◽  
Product Market ◽  
Representation Learning ◽  
Deep Network ◽  
Network Representation ◽  
External Data ◽  
Technological Developments

With rapid technological developments, product-market boundaries have become more dynamic. Consequently, competition for products and services is emerging outside the product-market boundaries traditionally defined based on SIC and NAICS classification codes. Identifying these fluid product-market boundaries is critical for firms not only to compete effectively within a market, but also to identify lurking threats and latent opportunities outside market boundaries. Newly available big data on social media engagement presents such an opportunity. We propose a deep network representation learning framework to capture latent relationships among thousands of brands and across many categories, using millions of social media users' brand engagement data. We build a brand-user network and then compress the network into a lower dimensional space using a deep Autoencoder technique. We evaluate our approach quantitatively and qualitatively, and visually display the market structure using the learned representations of brands. We validate the learned brand relationships using multiple external data sources. We also illustrate how our method can capture the dynamic changes of product market boundaries using two well-known events – the acquisition of Whole Foods by Amazon and the introduction of the Model 3 by Tesla – and how managers can use the insights that emerge from our analysis.

scholarly journals Depth as Attention for Face Representation Learning

2021 ◽  
Vol 16 ◽  
pp. 2461-2476
Author(s):  
Hardik Uppal ◽  
Alireza Sepas-Moghaddam ◽  
Michael Greenspan ◽  
Ali Etemad
Keyword(s):  
Representation Learning ◽  
Face Representation

Doppelganger Mining for Face Representation Learning

2017 ◽  
Author(s):  
Evgeny Smirnov ◽  
Aleksandr Melnikov ◽  
Sergey Novoselov ◽  
Eugene Luckyanets ◽  
Galina Lavrentyeva
Keyword(s):  
Representation Learning ◽  
Face Representation

scholarly journals Social behavior prediction with graph U-Net+

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyue Yan ◽  
Wenming Cao ◽  
Jianhua Ji
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Feature Extraction ◽  
Binary Classification ◽  
Representation Learning ◽  
Graph Representation ◽  
Behavior Prediction ◽  
Graph Node ◽  
Future Behavior ◽  
Decoder Architecture

AbstractWe focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.

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