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 ...
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...
Speaking ofgraph query languages, however, Xu also referred toGQL. GQL is thestandardization effort for graph query languagescurrently underway under the auspices of ISO, with support from many vendors. Since we have not had much news from that front for a while, we wonde...
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 ...
(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 ...
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. ...
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. ...
2.1. Current Status of Rumor Detection Research Initially, rumor detection research relied on traditional manual annotation methods, which were not only inefficient but also prone to subjectivity and bias. With the continuous development of computer science research in areas such as Machine Learning (ML...