Fake news detection: A survey of graph neural network methods
The emergence of various social networks has generated vast volumes of data. Efficient
methods for capturing, distinguishing, and filtering real and fake news are becoming …
methods for capturing, distinguishing, and filtering real and fake news are becoming …
Graph lifelong learning: A survey
Graph learning is a popular approach for perfor ming machine learning on graph-structured
data. It has revolutionized the machine learning ability to model graph data to address …
data. It has revolutionized the machine learning ability to model graph data to address …
Multiple features based approach for automatic fake news detection on social networks using deep learning
SR Sahoo, BB Gupta - Applied Soft Computing, 2021 - Elsevier
In recent years, the rise of Online Social Networks has led to proliferation of social news
such as product advertisement, political news, celebrity's information, etc. Some of the social …
such as product advertisement, political news, celebrity's information, etc. Some of the social …
User preference-aware fake news detection
Disinformation and fake news have posed detrimental effects on individuals and society in
recent years, attracting broad attention to fake news detection. The majority of existing fake …
recent years, attracting broad attention to fake news detection. The majority of existing fake …
Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection
Many recent studies have demonstrated that the propagation patterns of news on social
media can facilitate the detection of fake news. Most of these studies rely on the complete …
media can facilitate the detection of fake news. Most of these studies rely on the complete …
Cglb: Benchmark tasks for continual graph learning
Continual learning on graph data, which aims to accommodate new tasks over newly
emerged graph data while maintaining the model performance over existing tasks, is …
emerged graph data while maintaining the model performance over existing tasks, is …
A combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting
Accurate and real-time forecasting of the price of oil plays an important role in the world
economy. Research interest in forecasting this type of time series has increased …
economy. Research interest in forecasting this type of time series has increased …
Tackling fake news detection by continually improving social context representations using graph neural networks
N Mehta, ML Pacheco… - Proceedings of the 60th …, 2022 - aclanthology.org
Easy access, variety of content, and fast widespread interactions are some of the reasons
making social media increasingly popular. However, this rise has also enabled the …
making social media increasingly popular. However, this rise has also enabled the …
Unreliable users detection in social media: Deep learning techniques for automatic detection
Since the harmful consequences of the online publication of fake news have emerged
clearly, many research groups worldwide have started to work on the design and creation of …
clearly, many research groups worldwide have started to work on the design and creation of …
Attacking fake news detectors via manipulating news social engagement
Social media is one of the main sources for news consumption, especially among the
younger generation. With the increasing popularity of news consumption on various social …
younger generation. With the increasing popularity of news consumption on various social …