谣言检测《Rumor Detection with Self-supervised Learning on Texts and Social Graph》 _生活百科

论文信息

论文标题：Rumor Detection with Self-supervised Learning on Texts and Social Graph论文作者：Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang论文来源：2202，arXiv论文地址：download 论文代码：download

1 Introduction出发点：考虑异构信息；
本文的贡献描述：看看就行...............................
2 Methodology整体框架：

谣言检测《Rumor Detection with Self-supervised Learning on Texts and Social Graph》

文章插图
模块：
(1) propagation representation learning, which applies a GNN model on the propagation tree;
(2) semantic representation learning, which employs a text CNN model on the post contents;
(3) contrastive learning, which models the co-occurring relations among propagation and semantic representations;
(4) rumor prediction, which builds a predictor model upon the event representations.
2.1 Propagation Representation Learning考虑结构特征
对于帖子特征编码：
$\mathbf{H}^{(l)}=\sigma\left(\mathbf{D}^{-\frac{1}{2}} \hat{\mathbf{A}} \mathbf{D}^{-\frac{1}{2}} \mathbf{H}^{(l-1)} \mathbf{W}^{(l)}\right)\quad\quad\quad(2)$
帖子图级表示：
$\mathbf{g}=f_{\text {mean-pooling }}\left(\mathbf{H}^{(L)}\right)\quad\quad\quad(3)$
2.2 Semantic Representation Learning考虑语义特征首先：在帖子特征上使用多头注意力机制得到初始词嵌入 $\mathbf{Z} \in \mathbb{R}^{l \times d_{\text {model }}}$ （$l$ 代表着帖子数，$d_{\text {model }}$ 代表帖子的维度）：
$\boldsymbol{Z}_{i}=f_{\text {attention }}\left(\boldsymbol{Q}_{i}, \boldsymbol{K}_{i}, \boldsymbol{V}_{i}\right)=f_{\text {softmax }}\left(\frac{\boldsymbol{Q}_{i} \boldsymbol{K}_{i}^{T}}{\sqrt{d_{k}}}\right) \boldsymbol{V}_{i}\quad\quad\quad(4)$
$\boldsymbol{Z}=f_{\text {multi-head }}(\boldsymbol{Q}, \boldsymbol{K}, \boldsymbol{V})=f_{\text {concatenate }}\left(\boldsymbol{Z}_{1}, \ldots, \boldsymbol{Z}_{h}\right) \boldsymbol{W}^{O}\quad\quad\quad(5)$
接着：使用 CNN 进一步提取文本信息
考虑感受野大小为 $h$，得到 feature vector $\boldsymbol{v}_{i}$
$\boldsymbol{v}_{i}=\sigma\left(\boldsymbol{w} \cdot \boldsymbol{z}_{i: i+h-1}+\boldsymbol{b}\right)\quad\quad\quad(6)$
在 sentence 中遍历，得到词向量集合：
$\boldsymbol{v}=\left[\boldsymbol{v}_{1}, \boldsymbol{v}_{2}, \ldots, \boldsymbol{v}_{n-h+1}\right]$
在词向量集合 $\boldsymbol{v}$ 采用 max-pooling 得到全局表示 $\hat{\boldsymbol{v}}\quad\quad\quad(7)$：
$\hat{\boldsymbol{v}}=f_{\text {max-pooling }}(\boldsymbol{v})\quad\quad\quad(8)$
考虑使用 $n$ 个 feature map，并拼接表示得到文本表示 $\mathbf{t}$：
$\mathbf{t}=f_{\text {concatenate }}\left(\hat{\boldsymbol{v}}_{1}, \hat{\boldsymbol{v}}_{2}, \ldots, \hat{\boldsymbol{v}}_{n}\right)\quad\quad\quad(4)$
2.3 Contrastive Learning本文认为同一帖子的基于结构的表示 $\boldsymbol{g}_{i}$ 和基于语义 $\boldsymbol{t}_{i}$ 的表示是正对：
2.3.1 Propagation-Semantic Instance Discrimination (PSID)${\large \mathcal{L}_{\mathrm{ssl}}=\sum\limits_{i \in C}-\log \left[\frac{\exp \left(s\left(\boldsymbol{g}_{i}, \boldsymbol{t}_{i}\right) / \tau\right)}{\sum\limits _{j \in C} \exp \left(s\left(\boldsymbol{g}_{i}, \boldsymbol{t}_{j}\right) / \tau\right)}\right]} \quad\quad\quad(10)$
2.3.2 Propagation-Semantic Cluster Discrimination (PSCD)聚类级对比学习：
$\begin{array}{l}\underset{\mathbf{S}_{G}}{\text{min}}\quad \sum\limits _{c \in C} \underset{\mathbf{a}_{1}}{\text{min}} \left\|E_{1}(\mathbf{g})-\mathbf{S}_{G} \mathbf{a}_{1}\right\|_{2}^{2}+\underset{\mathbf{S}_{T}}{\text{min}} \sum\limits _{c \in C} \underset{\mathbf{a}_{2}}{\text{min}} \left\|E_{2}(\mathbf{t})-\mathbf{S}_{T} \mathbf{a}_{2}\right\|_{2}^{2}\\\text { s.t. } \quad \mathbf{a}_{1}^{\top} \mathbf{1}=1, \quad \mathbf{a}_{2}^{\top} \mathbf{1}=1\end{array}\quad\quad\quad(11)$