WebNov 23, 2024 · This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, … Web2 days ago · The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task. View

Dynamic graph convolutional networks with attention mechanism for rumor ...

Webconsider the event-level rumor detection task. There is a set of posts in each event and the objective is to identify whether the event is a rumor by leverage the posts in it. Below we summarize the related work on rumor detection based on the information they utilize. Most content-based methods leverage the characteristics WebSpatial structure based rumor detection. Diffusion pat-terns modeled as propagation trees or graph structures can provide useful clues for distinguishing rumors from non-rumors. Early methods rely on hand-crafted feature engi-neering to extract spatial structure features (Wu, Yang, and Zhu 2015; Ma, Gao, and Wong 2024). Recently, a line of early flowering bulbs uk

Dynamic Features Based Rumor Detection Method

Websentiment features into rumor detection. Wu et al. [10] proposed to capture the high-order propagation patterns to improve rumor detection. Most of these feature-based methods are biased, time-consuming and limited. They are usually designed for specific scenarios and hence cannot be easily generalized for other appli-cations. WebMay 6, 2024 · Most existing methods learn event-specific features that can not be transferred to unseen events. This paper proposed an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features with adversarial learning and thus benefit the detection of fake news on newly arrived … WebAug 18, 2024 · In Fig 3, we illustrated the two different methods of snapshot generations. Here on the index i for the claim ci will be omitted. S(t) is the graph snapshot at the time step t. Each graph snapshot in S will have separate adjacency matrices A = { A(1), A(2), , A(T) } with S(t) = V(t), E(t). Fig 3. csteamelsafty