PLAN 谣言检测——《Interpretable Rumor Detection in Microblogs by Attending to User Interactions》 _生活百科

论文信息

论文标题：Interpretable Rumor Detection in Microblogs by Attending to User Interactions论文作者：Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang论文来源：2020,论文地址：download 论文代码：download

Background基于群体智能的谣言检测：Figure 1

PLAN 谣言检测——《Interpretable Rumor Detection in Microblogs by Attending to User Interactions》

文章插图
本文观点：基于树结构的谣言检测模型，往往忽略了 Branch 之间的交互。
1 IntroductionMotivation：a user posting a reply might be replying to the entire thread rather than to a specific user.
Mehtod：We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network.
【PLAN 谣言检测——《Interpretable Rumor Detection in Microblogs by Attending to User Interactions》】We investigated variants of this model:

- a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network;
- a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention.

Contributions：

- We utilize the attention weights from our model to provide both token-level and post-level explanations behind the model’s prediction. To the best of our knowledge, we are the first paper that has done this.　
- We compare against previous works on two data sets - PHEME 5 events and Twitter15 and Twitter16 . Previous works only evaluated on one of the two data sets.
- Our proposed models could outperform current state-ofthe-art models for both data sets.

目前谣言检测的类型：
(i) the content of the claim.
(ii) the bias and social network of the source of the claim.
(iii) fact checking with trustworthy sources.
(iv) community response to the claims.
2 Approaches2.1 Recursive Neural Networks观点：谣言传播树通常是浅层的，一个用户通常只回复一次 source post，而后进行早期对话。
Dataset
Twitter15
Twitter16
PHEME
Tree-depth
2.80
2.77
3.12
2.2 Transformer NetworksTransformer 中的注意机制使有效的远程依赖关系建模成为可能。
Transformer 中的注意力机制：
$\alpha_{i j}=\operatorname{Compatibility}\left(q_{i}, k_{j}\right)=\operatorname{softmax}\left(\frac{q_{i} k_{j}^{T}}{\sqrt{d_{k}}}\right)\quad\quad\quad(1)$
$z_{i}=\sum_{j=1}^{n} \alpha_{i j} v_{j}\quad\quad\quad(2)$
2.3 Post-Level Attention Network (PLAN)框架如下：

文章插图
首先：将 Post 按时间顺序排列；
其次：对每个 Post 使用 Max pool 得到sentence embedding ；
然后：将 sentence embedding $X^{\prime}=\left(x_{1}^{\prime}, x_{2}^{\prime}, \ldots, x_{n}^{\prime}\right)$ 通过 $s$ 个多头注意力模块 MHA 得到 $U=\left(u_{1}, u_{2}, \ldots, u_{n}\right)$；
最后：通过 attention 机制聚合这些输出并使用全连接层进行预测：
$\begin{array}{l}\alpha_{k}=\operatorname{softmax}\left(\gamma^{T} u_{k}\right)&\quad\quad\quad(3)\\v=\sum\limits _{k=0}^{m} \alpha_{k} u_{k} &\quad\quad\quad(4)\\p=\operatorname{softmax}\left(W_{p}^{T} v+b_{p}\right) &\quad\quad\quad(5)\end{array}$

文章插图
where $\gamma \in \mathbb{R}^{d_{\text {model }}}, \alpha_{k} \in \mathbb{R}$，$W_{p} \in \mathbb{R}^{d_{\text {model }}, K}$，$b \in \mathbb{R}^{d_{\text {model }}}$，$u_{k}$ is the output after passing through $s$ number of MHA layers，$v$ and $p$ are the representation vector and prediction vector for $X$