Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity

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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 4787–4798,

4787

Modeling Conversation Structure and Temporal Dynamics for Jointly

Predicting Rumor Stance and Veracity

Penghui Wei, Nan Xu, Wenji Mao

†_{SKL-MCCS, Institute of Automation, Chinese Academy of Sciences (CASIA)}

‡_{University of Chinese Academy of Sciences}

{weipenghui2016,xunan2015,wenji.mao}@ia.ac.cn

Abstract

Automatically verifying rumorous informa-tion has become an important and challeng-ing task in natural language processchalleng-ing and so-cial media analytics. Previous studies reveal that people’s stances towards rumorous mes-sages can provide indicative clues for identi-fying the veracity of rumors, and thus deter-mining the stances of public reactions is a cru-cial preceding step for rumor veracity predic-tion. In this paper, we propose a hierarchical multi-task learning framework for jointly pre-dicting rumor stance and veracity on Twitter, which consists of two components. The bot-tom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network. The top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution. Experimental results on two bench-mark datasets show that our method outper-forms previous methods in both rumor stance classification and veracity prediction.

1 Introduction

Social media websites have become the main plat-form for users to browse inplat-formation and share opinions, facilitating news dissemination greatly. However, the characteristics of social media also accelerate the rapid spread and dissemination of

un-verified information, i.e., rumors (Shu et al.,2017).

The definition of rumoris “items of information

that are unverified at the time of posting”

(Zubi-aga et al., 2018). Ubiquitous false rumors bring

about harmful effects, which has seriously affected public and individual lives, and caused panic in

society (Zhou and Zafarani,2018;Sharma et al.,

2019). Because online content is massive and de-bunking rumors manually is time-consuming, there is a great need for automatic methods to identify

false rumors (Oshikawa et al.,2018).

Timeline

1

2 3 4 5

6

Source tweet (originating a rumor): 1. Reply tweets: 2, 3, 4, 5, and 6.

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Comment

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Stance

1

2 3 4 5

6

Sequential based:

Temporal based:

Aggregation based:

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2

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!

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Figure 1: A conversation thread discussing the rumor-ous tweet “1”. Three different perspectives for learning the stance feature of the reply tweet “2” are illustrated.

Previous studies have observed that public stances towards rumorous messages are crucial sig-nals to detect trending rumors (Qazvinian et al.,

2011;Zhao et al.,2015) and indicate the veracity

of them (Mendoza et al.,2010;Procter et al.,2013;

Liu et al., 2015; Jin et al., 2016; Glenski et al.,

2018). Therefore, stance classification towards ru-mors is viewed as an important preceding step of rumor veracity prediction, especially in the context

of Twitter conversations (Zubiaga et al.,2016a).

The state-of-the-art methods for rumor stance classification are proposed to model the sequential

property (Kochkina et al.,2017) or the temporal

property (Veyseh et al., 2017) of a Twitter

con-versation thread. In this paper, we propose a new perspective based on structural property: learning tweet representations through aggregating informa-tion from their neighboring tweets. Intuitively, a tweet’s nearer neighbors in its conversation thread are more informative than farther neighbors be-cause the replying relationships of them are closer, and their stance expressions can help classify the

stance of the center tweet (e.g., in Figure1, tweets

(2)

Stance distribution

Time (hour)

Time (hour) Time (hour)

[image:2.595.102.490.63.172.2]

True Rumors False Rumors Unverified Rumors

Figure 2: Stance distributions of tweets discussingtruerumors,f alserumors, andunverif iedrumors,

respec-tively (Better viewed in color). The horizontal axis is the spreading time of the first rumor. It is visualized based

on SemEval-2017 task 8 dataset (Derczynski et al.,2017). All tweets are relevant to the event “Ottawa Shooting”.

tweet contents and conversation structures into a latent space using a graph convolutional network

(GCN) (Kipf and Welling,2017), aiming to learn

stance feature for each tweet by aggregating its neighbors’ features. Compared with the sequential and temporal based methods, our aggregation based method leverages the intrinsic structural property in conversations to learn tweet representations.

After determining the stances of people’s re-actions, another challenge is how we can utilize public stances to predict rumor veracity accurately. We observe that the temporal dynamics of public

stances can indicate rumor veracity. Figure2

illus-trates the stance distributions of tweets discussing

truerumors, f alserumors, and unverif ied

ru-mors, respectively. As we can see, supporting

stance dominates the inception phase of spread-ing. However, as time goes by, the proportion of

denying tweets towardsf alse rumors increases

quite significantly. Meanwhile, the proportion of

queryingtweets towardsunverif iedrumors also

shows an upward trend. Based on this observa-tion, we propose to model the temporal dynamics of stance evolution with a recurrent neural network (RNN), capturing the crucial signals containing in stance features for effective veracity prediction.

Further, most existing methods tackle stance classification and veracity prediction separately, which is suboptimal and limits the generalization of models. As shown previously, they are two closely related tasks in which stance classification can pro-vide indicative clues to facilitate veracity predic-tion. Thus, these two tasks can be jointly learned to make better use of their interrelation.

Based on the above considerations, in this pa-per, we propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity, which achieves deep integration between the preceding task (stance classification) and the

subsequent task (veracity prediction). The bot-tom component of our framework classifies the stances of tweets in a conversation discussing a rumor via aggregation-based structure modeling, and we design a novel graph convolution operation customized for conversation structures. The top component predicts rumor veracity by exploiting the temporal dynamics of stance evolution, taking both content features and stance features learned by the bottom component into account. Two compo-nents are jointly trained to utilize the interrelation between the two tasks for learning more powerful feature representations.

The contributions of this work are as follows.

•We propose a hierarchical framework to tackle

rumor stance classification and veracity prediction jointly, exploiting both structural characteristic and temporal dynamics in rumor spreading process.

•We design a novel graph convolution operation

customized to encode conversation structures for learning stance features. To our knowledge, we are the first to employ graph convolution for modeling the structural property of Twitter conversations.

• Experimental results on two benchmark

datasets verify that our hierarchical framework per-forms better than existing methods in both rumor stance classification and veracity prediction.

2 Related Work

Rumor Stance Classification Stance analysis

has been widely studied in online debate

fo-rums (Somasundaran and Wiebe,2009;Hasan and

Ng, 2013), and recently has attracted increasing

attention in different contexts (Mohammad et al.,

2016; Augenstein et al., 2016;Ferreira and

Vla-chos, 2016; Mohtarami et al., 2018). After the

pioneering studies on stance classification towards

(3)

Qazvinian et al.,2011;Procter et al.,2013),

linguis-tic feature (Hamidian and Diab,2016;Zeng et al.,

2016) and point process based methods (Lukasik

et al.,2016,2019) have been developed.

Recent work has focused on Twitter

conversa-tions discussing rumors.Zubiaga et al.(2016a)

pro-posed to capture the sequential property of con-versations with linear-chain CRF, and also used a tree-structured CRF to consider the conversation

structure as a whole.Aker et al.(2017) developed

a novel feature set that scores the level of users’

confidence.Pamungkas et al.(2018) designed

af-fective and dialogue-act features to cover various

facets of affect.Giasemidis et al.(2018) proposed a

semi-supervised method that propagates the stance labels on similarity graph. Beyond feature-based

methods,Kochkina et al.(2017) utilized an LSTM

to model the sequential branches in a conversation, and their system ranked the first in SemEval-2017

task 8.Veyseh et al. (2017) adopted attention to

model the temporal property of a conversation and achieved the state-of-the-art performance.

Rumor Veracity Prediction Previous studies

have proposed methods based on various features such as linguistics, time series and propagation

structures (Castillo et al.,2011;Kwon et al.,2013;

Wu et al.,2015;Ma et al.,2017). Neural networks

show the effectiveness of modeling time series (Ma

et al.,2016;Ruchansky et al.,2017) and

propaga-tion paths (Liu and Wu,2018). Ma et al.(2018b)’s

model adopted recursive neural networks to incor-porate structure information into tweet representa-tions and outperformed previous methods.

Some studies utilized stance labels as the input feature of veracity classifiers to improve the

per-formance (Liu et al.,2015;Enayet and El-Beltagy,

2017).Dungs et al.(2018) proposed to recognize

the temporal patterns of true and false rumors’ stances by two hidden Markov models (HMMs). Unlike their solution, our method learns discrim-inative features of stance evolution with an RNN. Moreover, our method jointly predicts stance and veracity by exploiting both structural and temporal characteristics, whereas HMMs need stance labels as the input sequence of observations.

Joint Predictions of Rumor Stance and

Ve-racity Several work has addressed the problem of

jointly predicting rumor stance and veracity. These studies adopted multi-task learning to jointly train

two tasks (Ma et al.,2018a;Kochkina et al.,2018;

Poddar et al.,2018) and learned shared

represen-tations with parameter-sharing. Compared with such solutions based on “parallel” architectures, our method is deployed in a hierarchical fashion that encodes conversation structures to learn more powerful stance features by the bottom component, and models stance evolution by the top component, achieving deep integration between the two tasks’ feature learning.

3 Problem Definition

Consider a Twitter conversation thread C which

consists of a source tweett1(originating a rumor)

and a number of reply tweets_{t2, t3, . . . , t|C|}that

respondt1 directly or indirectly, and each tweet

ti (i ∈ [1,|C|]) expresses its stance towards the

rumor. The thread_Cis a tree structure, in which the

source tweett1 is the root node, and the replying

relationships among tweets form the edges.1

This paper focuses on two tasks. The first task is rumor stance classification, aiming to determine

the stance of each tweet inC, which belongs to

{supporting, denying, querying, commenting_}.

The second task is rumor veracity prediction, with the aim of identifying the veracity of the rumor,

belonging to_{true, f alse, unverif ied_}.

4 Proposed Method

We propose a Hierarchical multi-task learning framework for jointly Predicting rumor Stance and

Veracity (namedHierarchical-PSV). Figure3

illus-trates its overall architecture that is composed of two components. The bottom component is to clas-sify the stances of tweets in a conversation thread, which learns stance features via encoding conver-sation structure using a customized graph

convolu-tional network (namedConversational-GCN). The

top component is to predict the rumor’s veracity, which takes the learned features from the bottom component into account and models the temporal dynamics of stance evolution with a recurrent

neu-ral network (namedStance-Aware RNN).

4.1 Conversational-GCN: Aggregation-based

Structure Modeling for Stance Prediction

Now we detailConversational-GCN, the bottom

component of our framework. We first adopt a

bidirectional GRU (BGRU) (Cho et al.,2014) layer

to learn the content feature for each tweet in the

thread_C. For a tweetti (i∈ [1,|C|]), we run the

1_{We consider each replying relationship between two}