This paper proposes a political bias discrimination method of news based on a heterogeneous neural network, with multiple information related to prejudice in the news as the nodes of a heterogeneous network. By enriching the representation of nodes through a heterogeneous graph neural network and ...
In graph-level representation learning tasks, graph neural networks have received much attention for their powerful feature learning capabilities. However,
zero-shot performance w.r.t. the amount of training data (right)The outcome outlines the following key observations: (see Sec. 4.3 for details)Generalizability of AnyGraph Follows the Scaling Law. Emergent Abilities of AnyGraph. Insufficient Training Data May Bring Bias.Ablation...
The widespread dissemination of fake news on social media has substantial economic and social implications. Although traditional propagation-based methods employing graph neural networks show promise for fake news detection, they disregard the influence of confirmation bias in the spread of fake news betwe...
The layer class NodeFormerConv implements one-layer feed-forward of NodeFormer (which contains MP on a latent graph, adding relational bias and computing edge-level reg loss from input graphs if available). The model class NodeFormer implements the model that adopts standard input (node features...
This work is supported by the European Union - FSE-REACT-EU, PON Research and Innovation 2014–2020 DM1062/2021 contract number 18-I-15350-2, and was partially supported by the Ministry of University and Research, PRIN research project “BRIO – BIAS, RISK, OPACITY in AI: design, verificat...
For instance, people get influenced by others, but also tend to search and recall information and facts that align with their already formed belief system (confirmation bias). Furthermore, users interact preferably with people of similar profiles and opinions (homophily), a tendency that greatly ...
Positive-negative bias-based ranking algorithmWe propose an unsupervised model to extract two types of summaries (positive, and negative) per document based on sentiment polarity. Our model builds a weighted polar digraph from the text, then evolves recursively until some desired properties converge. ...
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media.Yi-Ju Lu, Cheng-Te Li.ACL 2020[pdf] Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks.Nikhil Mehta, Maria Pacheco and Dan Goldwasser.ACL 2022[...
(0\right)}\) is X, that is, the feature matrix is the input of the first layer of GCN, \({W}^{\left(l-1\right)}\) and \({b}^{\left(l-1\right)}\) are the weight matrix and bias in the \(\left(l-1\right)\)-th GCN layer and \({\upsigma }\) is an activation ...