A State-independent and Time-evolving Network for Early Rumor Detection in Social Media (2020)

Abstract: In this paper, we study automatic rumor detection for in social media at the event level where an event consists of a sequence of posts organized according to the posting time. It is common that the state of an event is dynamically evolving. However, most of the existing methods to this task ignored this problem, and established a global representation based on all the posts in the event’s life cycle. Such coarse-grained methods failed to capture the event’s unique features in different states. To address this limitation, we propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation. Given an event composed of a sequence of posts, STN first predicts the corresponding sequence of states and segments the event into several state-independent sub-events. For each sub-event, STN independently trains an encoder to learn the feature representation for that sub-event and incrementally fuses the representation of the current sub-event with previous ones for rumor prediction. This framework can more accurately learn the representation of an event in the initial stage and enable early rumor detection. Experiments on two benchmark datasets show that STN can significantly improve the rumor detection accuracy in comparison with some strong baseline systems. We also design a new evaluation metric to measure the performance of early rumor detection, under which STN shows a higher advantage in comparison.

## introduction rumor is defined as an unverified statement, which may be unintentionally created or deliberately fabricated @xcite . false rumors are damaging as they may cause public panic and figure 1 : an illustration of the spread of rumors, which displays the average number of posts on twitter and weibo datasets @xcite over the propagation timeline. in recent years, the rumor detection task has attracted continuous attention from many researchers in the nlp community. we denote a statement in social media as an event consisting of a source post and its following posts such as comments or reposts (collectively called posts). given an event, the rumor detection task is typically defined as a text classification problem @xcite . the former aims to detect whether an event is a rumor or not. in the literature, the typical method was to first obtain a global representation of the event based on all posts in the event's life cycle, and then employ a machine learning algorithm, such as random forest (rf, @xcite , support vector machine (svm, @xcite , convolution neural network @xcite and recurrent neural network @xcite to learn the connection between the representation and the class labels. on the one hand, events in social media evolve dynamically. according to communication stud-ies, the dissemination of an event can be roughly divided into an evolution period, a high-tide period and an extinction period @xcite . as shown in figure 1 , similar curves can be observed in two real-world social media rumor datasets (i.e., twitter and weibo). each state of the event has different posting density and data distribution. however, most of the aforementioned coarsegrained methods ignored the dynamics in the text data stream, and failed to capture the unique features in different states. although part of these methods have considered temporal features or modeled the sequential dynamics with rnn, they still failed to establish fine-grained representations for different states. on the other hand, the early detection of rumors is of great importance. according to our observation on the two rumor datasets, most events reach the high-tide period in less than five minutes. although some of the previous work segmented the timeline by equal time span or equal number of posts for early rumor detection @xcite @xcite , they potentially ignored the vital features of early states and failed to train targeted models for early detection. to address the limitations mentioned above, we propose a new state-independent and timeevolving network (stn) for rumor detection based on propagation state detection and segmentation, and apply it to early rumor detection in this paper. specifically, since an event in social media is actually a sequence of posts sorted according to the posting time, it can be viewed as a time-series text data stream. to learn the propagation states in the text data stream, we first employ the kleinberg algorithm @xcite to segment an event into several sub-events based on the state transition, each of which represents a continuous and identical state. subsequently, we train an encoder to fit each sub-event separately. we furthermore propose a time-evolving fusion @xcite mechanism to merge the current sub-event representation with previous ones, and combine them together for incremental prediction. stn no longer outputs one predictive label for one event, but outputs a sequence of labels for each state-independent sub-event, which enables early detection of rumors. moreover, we further present a new evaluation metric, called time-series smoothing accuracy (ts-acc), for measuring the performance of early rumor detection. experimental results on two real-world rumor detection datasets released by @xcite demonstrate the effectiveness of our stn model. it not only achieves significant improvements for rumor detection in comparison with several strong baseline systems, but also greatly improves the early rumor detection performance. ## related work in recent years, rumor classification system has developed rapidly. based on the definition in @xcite , a complete rumor classification system consists of four components: i. rumor detection; ii. rumor tracking; iii. stance classification; iv. rumor verification. among the four sub-tasks, rumor verification resembles rumor detection closely. for rumor detection, the goal is to detect whether a statement is a rumor or not (i.e, the class labels are rumor and non-rumor); for rumor verification, the goal is to determine whether a rumor is true, false or unconfirmed. some following work have also combined the class labels together and consider it as a four-class classification problem (non rumor, true rumor, false rumor, unverified rumor) @xcite foot_0 . during the prophase study of rumor detection, researchers focused on extracting various obvious features of microblog events on social media platforms, and combined the features with traditional machine learning classifiers to detect rumors or identify information credibility @xcite @xcite @xcite @xcite @xcite . these manually-designed features can be roughly categorized into three groups, including text content, user portraits and propagation states. however, it is hard for these traditional approaches to capture the dynamic characteristics during the spread of an event and the relationship between the posts. to address this issue, @xcite constructed a massage propagation model to find the diversity of the amount of related posts between rumor and nor-rumor. @xcite first proposed to divide the event timeline into equal-span periods and utilized the dynamic changes of fea-tures in adjacent periods. based on this, @xcite further introduced rnn models to encode the time periods, which verified the effectiveness of rnn models on encoding sequential posts. @xcite utilized a sequential approach based on linear-chain conditional random fields (crf) to learn the dynamic relations between posts, which relies on the content of a source microblog and its related posts. @xcite employed different sets of features to keep the properties of the propagation structure and temporal relations among posts. moreover, @xcite incorporated the attention mechanism into stacked rnns to model the temporal propagation of an event. 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 @xcite @xcite @xcite . among them, the representative studies are @xcite and @xcite , 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 @xcite 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. although much work has been done for rumor detection, only a few previous studies focused on the early detection of rumors (edr). @xcite argued that rumors are more likely to arouse users' suspicion, and proposed to aggregate related posts with specific phrases, followed by performing edr with cluster-based classifiers. however, this work inevitably involved much human effort. to alleviate the reliance of feature engineering, @xcite utilized deep neural networks to automatically capture features at the post level. although it achieves better early detection performance, it is difficult to be applied to large events. @xcite believed that early posts are easy to be manipulated by the source microblog, while user characteristics are relatively stable. they integrated rnn and cnn models to capture user characteristics in the propagation process of an event. however, only using user features makes their model unable to achieve continuous performance improvement as time goes by. more re-cently, @xcite introduced the concept of credible detection points, and proposed to gather every ten posts along the timeline as one time-step of rnn and made prediction at each step. but tens of thousands of posts make the number of time steps large, which may reduce the reliability of long-distance dependence. we train a rumor detection model based on d, and use it to predict the class labels y on an unseen event e. ## conclusion and future work in this paper, we first introduce the kleinberg algorithm to identify the propagation states for an event composed of a sequence of posts and segment the sequence into several state-independent sub-events. on this basis, we propose a state-independent and time-evolving network (stn) for rumor detection as well as early rumor detection. we also present a new metric called time-series smoothing accuracy (ts-acc) for measuring the efficiency of early rumor detection. the experimental results on two real-world microblog rumor datasets demonstrates the advantages of our stn approach in terms of both rumor detection accuracy and our proposed ts-acc metric, in comparison with some strong rumor detection systems. one disadvantage of this work is that the kleinberg algorithm is performed on the combination of all events in the dataset to maintain global states. this way may fail to capture the individual state transition in single events. secondly, it is a retrospective algorithm which depends on the condition all posts along the timeline should be provided in advance. therefore, one direction for future work is to explore an online state detection algorithm and perform it for each event, but at the same time ensure that the state of each event is globally defined. it would be even better if the state detection and segmentation step can be integrated with sub-sequent state-independent feature extraction and rumor detection in an end-to-end framework.