Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

Abstract

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve stateof-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively.

Debunking Rumors on Twitter with Tree Transformer

“…Rumor indicative features can be captured from propagation structures, e.g., the stances expressed in responsive tweets can further reinforce the stances of that tweet is replying to (Ma et al, 2018;Kumar and Carley, 2019), the posts with strong stance based on the tree branch is more important when determining the rumor veracity (Li et al, 2019), and inaccurate information might be "self-checked" by making comparison with correlative tweets . However, such relation is not fully exploited by previous work.…”

“…For automated approaches, prior studies focus on engineering or learning features from sequential microblog streams (Castillo et al, 2011;Yang et al, 2012;Kwon et al, 2013;Liu et al, 2015;Ma et al, 2015;Ma et al, 2016;Yu et al, 2017). More recently, structure-based learning based on structured neural networks are proposed to capture the interactive characteristics of rumor diffusion, such as tree kernel (Ma et al, 2017), recursive neural network (Ma et al, 2018) and tree LSTM model (Kumar and Carley, 2019). Khoo et al (2020) proposed to model potential dependencies between any two microblog posts with the post-level self-attention networks (PLAN), which has achieved the state-of-the-art detection performance.…”

“…Nevertheless, we argue that such full connection which ignores the specific targets of replies in the hierarchy could create salient issues on post representation learning, especially in the vein of relatively deep conversation or argument. Meanwhile, other existing tree-structured models based on propagation trees (Wu et al, 2015;Ma et al, 2017) or recursive trees (Ma et al, 2018;Kumar and Carley, 2019) tend to oversimplify user interaction by genuinely following the tree edges for post matching or encoding. To illustrate our intuition, Figure 1 exemplifies the propagation structure of a (rumor) claim "Mike Pence caught on hot mic delivering empty boxes of PPE for a PR stunt".…”

A State-independent and Time-evolving Network for Early Rumor Detection in Social Media

“…In addition, there is another line of researches focusing on modeling the post sequences with tree structures, which aims to useful relations among the responsive posts (Nadamoto et al, 2013;Wu et al, 2015;Ma et al, 2017Ma et al, , 2018Kumar and Carley, 2019). Among them, the representative studies are Ma et al (2018) and Kumar and Carley (2019), which respectively proposed a recursive neural network and a Tree-LSTM architecture to explicitly model the tree structure. Different from all the studies mentioned above, a recent study by Ma et al (2019) proposed to leverage Generative Adversarial Networks (GAN) to improve the robustness of rumor detection, where a generative model is trained to confuse the rumor detection discriminator by generating pseudo real examples.…”

Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations

“…This line of work has attracted increasing attention in recent years. A number of multi-task learning (MTL) methods have been proposed to jointly perform stance classification (SC) and rumor verification (RV) over conversation threads, including Sequential LSTM-based methods (Li et al, 2019), Tree LSTM-based methods (Kumar and Carley, 2019), and Graph Convolutional Network-based methods . These MTL approaches are mainly constructed upon the MTL2 framework proposed in Kochkina et al (2018), which aims to first learn shared representations with shared layers in the low level, followed by learning task-specific representations with separate stance-specific layers and rumor-specific layers in the high level.…”