Exploiting Microblog Conversation Structures to Detect Rumors (2020)

Abstract: As one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter due to the lack of an automatic information verification system. Twitter users communicate by replying to other users’ messages, forming a conversation structure. Using this structure, users can decide whether the information in the source tweet is a rumor by reading the tweet’s replies, which voice other users’ stances on the tweet. The majority of rumor detection researchers process such tweets based on time, ignoring the conversation structure. To reap the benefits of the Twitter conversation structure, we developed a model to detect rumors by modeling conversation structure as a graph. Thus, our model’s improved representation of the conversation structure enhances its rumor detection accuracy. The experimental results on two rumor datasets show that our model outperforms several baseline models, including a state-of-the-art model

## introduction social media platforms have become a primary source of information due to the ease of sharing information they provide. the latest survey from the pew research center states that 68% of american adults occasionally read news on social media platforms @xcite . however, the credibility of the massive amount of news propagated through social media is questionable due to the absence of editors who can validate it @xcite . as a result, social media platforms have become perfect avenues for spreading unverified information and rumors. users on social media platforms communicate by replying to other users' posts, repeatedly responding to one another, forming a conversation structure. the conversation structure established by social media consists of a tree representation of the information distributed by users posting at a specific time in response to a source post. @xcite @xcite . furthermore, @xcite distinguish two types of conversation: dialogic (horizontal conversation among users) and dialectic (vertical conversation with a source post) conversations. conversations on social media can influence users' perception of the information within them @xcite . for example, figure 1 shows a rumor conversation on twitter in which users give opinions, make conjectures, or supply evidence in reply to the source post or other users' replies. users can obtain clues about the truth of the source post by reading the replies in the conversation structure. @xcite refer to this self-correcting mechanism in their research. however, the majority of users disregard such comments, preferring to immediately share tweets; thus, this self-correcting mechanism cannot prevent the spread of rumors in social media. therefore, an automatic rumor detection mechanism is necessary. several researchers have attempted to automatically detect rumors by analyzing the text features @xcite @xcite @xcite , user and message features @xcite @xcite , or both @xcite . however, most of the existing approaches rely on analyzing a single message and ignore the topological information of the social media conversation structure. ignoring the conversation structure can lead to information misinterpretation and affect rumor detection accuracy. for example, in figure 1 , the numbers indicate the sequence of tweet publishing times, and the words depicted in red font represent the stance of the posts, namely, support, deny, or comment, symbolized by (+), (-), and (o), respectively. because each specific reply is directly related to the source tweet or other responses, a conversation structure is formed. deep learning methods such as rnns (including lstm and gru) and cnns ignore this relationship and oversimplify it into a time-based chain structure for encoding tweets (see figure 2 ). for instance, based on the tweet publication time, the 5th tweet will be fed into the model after the 4th tweet and before the 6th tweet. according to this timebased chain structure and the tweets' stances, the 5th tweet supports the 4th tweet, and the 6th tweet denies the 5th tweet. however, the 5th tweet is actually a reply to the source tweet, not the 4th tweet. in other words, there is no actual relationship between the 4th, 5th, and 6th tweets, but the time-based chain structure assigns them a false relationship. a social media conversation can be illustrated by a graph where each message is a node, and the relationships between posts are edges. figure 3 shows a graph representation of the conversation structure described in figure 1 that maintains the original conversation structure. to utilize the conversation structure between a source tweet and its replies, we propose a graph-based deep learning model for rumor detection. the graph structure overcomes the shortcomings of rnns and cnns by processing tweets based on conversation structure rather than based on time. our model consists of the following three hierarchically structured modules: the tweet representation module (trm), the conversation propagation module (cpm) and the classifier module (cm). the trm captures the high-level information of a tweet and creates its representation, the cpm propagates the tweet's representation through a graph structure, and the cm is a deep neural network for rumor classification. the main contributions of our study are as follows: on the other hand, deep learning does not depend on hand-crafted features and automatically extracts features from data. rnns are the most widely used deep learning models for text classification tasks, including rumor detection. rnns can extract features and learn contextual information from text over time @xcite . to shorten the training time and enhance the prediction accuracy, @xcite proposed a hybrid approach that integrates cnns and rnns. regardless of their traditional machine learning or deep learning approaches, all of the aforementioned researchers process data based on time, ignoring structure information. to take advantage of the event structure in social media, @xcite proposed a hierarchical structure combined with social attention to process the data based on an event. these authors divided the process into three levels: word, post, and subevent levels. however, this event structure does not reflect the actual conversation structure. using another approach, @xcite constructed a recursive neural network to handle conversational structure. this model generates a tree structure by bottom-up or top-down propagation. however, the nodes' influences are unbalanced, as the last nodes have a greater impact on the representation results. moreover, because this model uses an rnn as a processing unit, it also encounters the long-term dependencies problem. in addition, an acyclic graph is required as input; thus, graph generalization is unreliable. graph neural networks (gnns) have recently become a popular model in deep learning research. gnns process information by modeling the dependencies between nodes through message passing. moreover, gnns achieve a state that contains information from their neighborhood with varied depth @xcite . gnn variants have demonstrated solid performance on a variety of nlp tasks, e.g., text classification, sentiment analysis, neural machine translation, and multihop reading comprehension. the gnn variant graphsage uses a general inductive model to learn embedded 1 2 3 4 5 6 7 classifier @xcite . graphsage can learn from dynamic graphs such as those found in social media conversations with variable numbers of graph nodes. ## model architecture we formulate our task as a supervised classification problem, designating the detection unit as a conversation involving a single source post and its replies. let c denote a conversation 𝐶 𝑒 = {𝑃 𝑠 , 𝑃 1 , 𝑃 2 , … , 𝑃 |𝑐| } where 𝑃 𝑠 is the source post, and 𝑃 |𝑐| is the last relevant post. note that the numbers assigned to each post do not indicate that the conversation has a sequential structure; rather, the links between each post are based on reply or repost relationships. the objective of our model is to classify 𝐶 𝑒 as 'rumor' or 'nonrumor'. the classifier performs learning through labeled information, i.e., 𝑓 𝑒 : 𝐶 𝑒 → 𝑦 𝑒 . the core concept of our approach is to strengthen the representation of the information by propagating it through conversation structure. to achieve this goal, we designed three modules: the tweet representation module (trm), conversation propagation module (cpm), and classifier module (cm) (figure 4 ). ## conclusions we introduce a novel model that shows how the conversation structure of social media can help detect rumors. by viewing conversation structure as a graph, we propagate a message through its structure and benefit from users' related stances. in experiments using the pheme 2017 and pheme 2018 datasets, which contain only small amounts of data, our model outperforms the baseline and state-of-the-art models. we expect that our model's performance will increase when it is trained on larger datasets. moreover, since the cpm module can aggregate information from local neighbors and create a representation of unseen nodes, our model can be used for unsupervised tasks because it enables nearby nodes to have a similar representation while enforcing the highly distinct representation of the disparate node. in the future, we will investigate unsupervised models on massive amounts of unlabeled rumor data from social media.