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Due to the rapid spread of rumors on social media, which has a detrimental effect on our lives, it is becoming increasingly important to detect rumors. It has been proved that the study of dynamic graphs is helpful to capture the temporal change of information transmission and understand the evolution trend and pattern change of events. However, the dynamic learning methods currently studied do not fully consider the interaction characteristics of the evolutionary process. Therefore, it is difficult to fully capture the structural and semantic differences between them. In order to fully exploit the potential correlations of such temporal information, we propose a novel model named dynamic evolution characteristics learning (DECL) method for rumor detection. First, we partition the temporal snapshot sequences based on the propagation structure of rumors. Secondly, a multi-task graph contrastive learning method is adopted to enable the graph encoder to capture the essential features of rumors, and to fully explore the temporal structural differences and semantic similarities between true rumor and false rumor events. Experimental results on three real-world social media datasets confirm the effectiveness of our model for rumor detection tasks. [ABSTRACT FROM AUTHOR] more...

To address the problem that the influence of rumor propagation stages is ignored in traditional rumor detection methods, an early rumor detection method based on stage sampling and triple-relationship graph is proposed. Firstly, the sampling probability density function is defined, and the stage sampling method based on normal distribution is proposed to obtain the comment sets with respect to the early stage, middle stage, and late stage of an event. Secondly, both the comment posting time and information propagation directions are considered, and the triple-relationship graph that integrates diffusion relationship, aggregation relationship, and sibling relationship for each stage is constructed. Moreover, the advantage of heterogeneous graph attention network (HAN) in exploring graph structural features is leveraged to obtain the node representations. Finally, to improve the interpretability and to capture the mutual influence between nodes efficiently, a graph-level vector computation method based on compressed self-attention mechanism and soft attention mechanism is proposed. Experimental results on two public datasets show that the proposed method consistently outperforms existing typical methods, with Fw improvements of approximately 2.1% and 3.7% on the CED and Weibo datasets, respectively, validating its effectiveness on early rumor detection. Furthermore, attention weight visualization experiments explicitly highlight the contributions of comments at different stages, significantly enhancing the interpretability of our approach. [ABSTRACT FROM AUTHOR] more...

Because of their proficiency in capturing the category characteristics of graphs, graph neural networks have shown remarkable advantages for graph-level classification tasks, that is, rumor detection and anomaly detection. Due to the manipulation of special means (e.g. bots) on online media, rumors may spread across the whole network at an overwhelming speed. Compared with normal information, popular rumors usually have a special propagation structure, especially in the early stage of information dissemination. More specifically, the special propagation structure determines whether rumors can be spread successfully. Namely, online users and their interaction that constitute the special propagation structure play a key role in the spread of rumors. Therefore, the problem of rumor detection can be transformed into detecting the existence of a special propagation structure. Inspired by backdoor attacks, we propose an interpretable rumor detection algorithm based on backdoor. Firstly, based on causal analysis, the causal sub-graph that determines the category of the graph (rumor vs. normal information) is obtained, that is, the critical online users in the rumor spreading effect are found, and then the specific propagation structure is explored. Finally, the special propagation structure is planted into the rumor detection model as a trigger. Experimental results and performance analysis on three real-world datasets demonstrate the effectiveness of our proposed algorithm in the special propagation structure detection of rumors. Compared with two baselines, Lurker achieves up to 33.1% and 61.8% performance improvement in terms of attack success rate and clean accuracy drop. [ABSTRACT FROM AUTHOR] more...

Recently, lots of pre-trained language models (PLMs) have been investigated for rumor detection and obtained superior results. However, existing PLM-based approaches are challenged and limited when addressing short texts which are often sparse, noisy, and semantically ambiguous. To remedy this, we propose a Plug-and-Play Topic-enhanced Pre-trained Language Model for short-text rumor detection, dubbed PPTopicPLM, aiming to combine topic models with PLMs without modifying the core network structure of PLMs. First, the pre-trained RoBERTa is used to represent the short texts. Then, we utilize the semantics-assisted non-negative factorization (SeaNMF) model to explore topics in short texts since SeaNMF has proven to be effective in incorporating the word-context semantic correlations. Instead of simple but computationally expensive methods like word frequency statistics, we build the semantic correlation matrix based on the semantic similarity between the dynamic word embeddings yielded by pre-trained PLMs to mitigate the semantic information loss especially encountered by short texts. Afterward, we employ the multimodal factorized bilinear pooling technique to thoroughly aggregate topics and text semantics. Finally, substantial experiments conducted on four real-world datasets show that the proposed method achieves significant performance improvements due to its successfully joining topic models and PLMs for short-text rumor detection. We further empirically demonstrate the wide application prospect of our approach on the semantic similarity detection task. The codes are open sourced at . [ABSTRACT FROM AUTHOR] more...

Online Social Networks (OSNs) have become the main platform for individuals to share information, but the rapid dissemination of information has also resulted in an increase in the spread of rumors. Some structure-based deep learning methods focus on the depth of propagation while disregarding the wide disperse patterns of rumors. They are also based on static graphs, which are incompatible for capturing the dynamic nature of real-world rumor propagation. To overcome these challenges, we propose a novel Bi-directional Temporal Graph Attention Networks, termed as BTAN. Firstly, we incorporate propagation patterns, dispersion structure, and temporal information into a unified framework for rumor detection. Secondly, we model the temporal evolution patterns of rumors as a graph that evolves within the context of continuous-time dynamic graphs, and employ two Graph Attention Networks to capture the information regarding both top-down and bottom-up rumor propagation. Finally, extensive experiments conducted on real-world datasets show the superior performance of our model for rumor detection. [ABSTRACT FROM AUTHOR] more...

As vast amounts of rumor content are transmitted on social media, it is very challenging to detect them. This study explores an ensemble approach to rumor detection in social media messages, leveraging the strengths of advanced natural language processing (NLP) models. Specifically, we implemented three distinct models: (i) generative pre-trained transformer (GPT) combined with a bidirectional long short-term memory (BiLSTM) network; (ii) a model integrating part-of-speech (POS) tagging with bidirectional encoder representations from transformers (BERT) and BiLSTM, and (iii) a model that merges POS tagging with GPT and BiLSTM. We included additional features from the dataset in all these models. Each model captures different linguistic, syntactical, and contextual features within the text, contributing uniquely to the classification task. To enhance the robustness and accuracy of our predictions, we employed an ensemble method using hard voting. This technique aggregates the predictions from each model, determining the final classification based on the majority vote. Our experimental results demonstrate that the ensemble approach significantly outperforms individual models, achieving superior accuracy in identifying rumors. To determine the performance of our model, we used PHEME and Weibo datasets available publicly. We found our model gave 97.6% and 98.4% accuracy, respectively, on the datasets and has outperformed the state-of-the-art models. [ABSTRACT FROM AUTHOR] more...

Social networks, while enabling information exchange among individuals, also serve as fertile grounds for the dissemination of rumors. The succinct nature of social media posts poses a challenge for most rumor detection methods reliant on content semantic features due to the insufficiency of semantic information. Additionally, numerous rumor detection techniques focusing on propagation features often disregard the unique attributes of commenters, leading to inadequate allocation of weights to different user comments. Thus, a network rumor detection approach is proposed, integrating text semantic enhancement and weighted comment stance. Initially, entities and concepts in posts are elucidated via an external knowledge graph to furnish additional contextual information, thereby augmenting semantic comprehension. Subsequently, leveraging pointwise mutual information, the enhanced text is translated into a weighted graph representation, and a weighted graph attention network is employed to assimilate enhanced semantic features of posts. Stance information for each comment within the post is then extracted using a pre-trained stance detection model, with weight values of stance information being learnt based on commenters’ characteristics. Furthermore, temporal data of comment stances and corresponding commenter sequences are fed into a cross-modal Transformer to glean the temporal features of comment stances. Ultimately, the enhanced semantic features are adaptively merged with the weighted temporal features of comment stances and fed into a multi-layer perceptron for classification. Experimental results on the PHEME and Weibo datasets demonstrate that this method not only achieves an accuracy improvement of over 1.6 percentage points compared with the state-of-the-art baseline method but also outperforms the best baseline method by at least 12 hours in early rumor detection. more...

Abstract Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively. more...

With the popularity of social media, the rapid spread of rumors has brought great challenges to social stability and personal safety. Therefore, it is of great significance to develop an efficient and accurate rumor detection method. This study first reviews this research status of social network rumor detection, analyzes the advantages and disadvantages of traditional methods and deep learning-based methods, and then completes network language feature extraction based on Transformer, uses KIMI large language model (LLM) for semantic enhancement, and then completes the establishment of semantic feature fusion module. Finally, KIMI and GPT are integrated to construct a hybrid LLM for fast detection of Internet rumors. In order to verify the effectiveness of this method, this study obtains data sets from MicroBlog community management center, Twitter and TikTok for experiments. The experimental results show that the semantic enhancement technology based on the mixed LLM performs well in the rumor detection task, which is significantly better than the single LLM. In addition, we also discuss the effect of semantic enhancement strategy on model performance, and find that this strategy can further improve the accuracy and robustness of the model. The experimental results verify the effectiveness and practicability of this method, and provide a new idea and method for the rapid detection of social network rumors. [ABSTRACT FROM AUTHOR] more...

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

The proliferation of the Internet and mobile devices has made it increasingly easy to propagate rumors on social media. Widespread rumors can incite public panic and have detrimental effects on individuals. In recent years, researchers have found that both the spatial structure of rumor diffusion and the temporal features of propagation can be effective in identifying rumors. However, existing methods tend to focus on either spatial structure or temporal information in isolation, and few models can effectively capture both types of information. Additionally, most existing methods treat continuously changing temporal information as static snapshots, neglecting the precise timing of propagation. Moreover, as users repost and comment, background knowledge associated with the posts also evolves dynamically, which is often ignored. To address these limitations, we propose CGNKP (Continuous-time Dynamic Graph Networks integrated with Knowledge Propagation), a model that jointly captures the spatial structure and continuous-time features of post propagation to fully understand the dynamics of background knowledge. Specifically, we introduce a novel method for encoding continuous-time dynamic graphs, modeling the propagation process through two dynamic graphs: a temporal propagation graph (for posts diffusion) and a temporal knowledge graph (for knowledge diffusion). Extensive experiments on real-world datasets demonstrate that CGNKP significantly outperforms multiple strong baselines, achieving accuracies of 0.861 on the Twitter15 dataset and 0.903 on the Twitter16 dataset. [ABSTRACT FROM AUTHOR] more...

Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively. [ABSTRACT FROM AUTHOR] more...

Social media rumors, which spread uncontrollably, are the world's big problem and therefore there is a great need for effective early detection tools. However, despite the widely known influence of social circles on information disemmination, most existing detection models overlook it. This study addresses this gap by examining three key components such as: BERT for Tweet Analysis for Semantic Understanding, Social Circle and Interaction Tree Embeddings for Network Context. The first step in doing so is to exploit BERT in order to obtain subtle semantic meanings from tweets that could provide insights into rumor structures and their content. Second, social circle data capture the user's circle as well as their involvement with community towards leading rumors diffusion. Finally, interaction embeddings capture network dynamics thus providing useful contextual information to understand how rumor propagates over network paths. These components are integrated to develop a comprehensive algorithm for rumor detection. Our algorithm combines tweet's semantic analysis using BERT and provides network context from social circle and interaction embeddings. Experiments on real datasets demonstrate that our approach outperforms all others in terms of accuracy and efficiency thereby showing its effectiveness over existing methods. The summary of this research implies that BERT, social circles, and interaction patterns are substantial in a comprehensive rumor detection framework for online platforms. Adopting this method will facilitate us to detect rumors from their inception, thereby prevention is taken as one measure against the information mismanagement in social media networks. [ABSTRACT FROM AUTHOR] more...

With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT's robust text representation capabilities, the GCN's feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training. [ABSTRACT FROM AUTHOR] more...

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) more...

Social media, an undeniable facet of the modern era, has become a primary pathway for disseminating information. Unverified and potentially harmful rumors can have detrimental effects on both society and individuals. Owing to the plethora of content generated, it is essential to assess its alignment with factual accuracy and determine its veracity. Previous research has explored various approaches, including feature engineering and deep learning techniques, that leverage propagation theory to identify rumors. In our study, we place significant importance on examining the emotional and sentimental aspects of tweets using deep learning approaches to improve our ability to detect rumors. Leveraging the findings from the previous analysis, we propose a Sentiment and EMotion driven TransformEr Classifier method (SEMTEC). Unlike the existing studies, our method leverages the extraction of emotion and sentiment tags alongside the assimilation of the content-based information from the textual modality, i.e., the main tweet. This meticulous semantic analysis allows us to measure the user's emotional state, leading to an impressive accuracy rate of 92% for rumor detection on the "PHEME" dataset. The validation is carried out on a novel dataset named "Twitter24". Furthermore, SEMTEC exceeds standard methods accuracy by around 2% on "Twitter24" dataset. [ABSTRACT FROM AUTHOR] more...

The rise of social media platforms has led to an increase in the flow and dissemination of information, but it has also made generating and spreading rumors easier. Rumor detection requires understanding the context and semantics of text, dealing with the evolving nature of rumors, and processing vast amounts of data in real-time. Deep learning (DL)-based techniques exhibit a higher accuracy in detecting rumors on social media compared to many traditional machine learning approaches. This study presents a systematic review of DL approaches in rumor detection, analyzing datasets, pre-processing methods, feature taxonomy, and frequently used DL methods. In the context of feature selection, we categorize features into three areas: text-based, user-based, and propagation-based. Besides, we surveyed the trends in DL models for rumor detection and classified them into convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and other methods based on the model structure. It offers insights into effective algorithms and strategies, aiming to guide researchers, developers, social media users, and governments in detecting and preventing the spread of false information. The study contributes to enhancing research in this field and identifies potential areas for future exploration. [ABSTRACT FROM AUTHOR] more...

Establishing a robust rumor detection model is vital in safeguarding the veracity of information on social media platforms. However, existing approaches to stopping rumor from spreading rely on abundant and clean training data, which is rarely available in real-world scenarios. In this work, we aim to develop a trustworthy rumor detection model that can handle inadequate and noisy labeled data. Our work addresses robust rumor detection, including classic and early detection, as well as five types of robustness issues: noisy and incomplete propagation, label scarcity and noise, and user disappearance. We propose a novel method, Robustness-Enhanced Rumor Detection (RERD), which mainly leverages the information propagation graphs of source tweets, along with user profiles and retweeting knowledge, for model learning. The novelty of RERD is four-fold. First, we jointly exploit the propagation structures of non-text and text retweets to learn the representation of a source tweet. Second, we simultaneously utilize the top-down and bottom-up information flows with relational propagations for graph representation learning. Third, to have effective early and robust detection, we implement contrastive learning on graphs with early and complete views of information propagation so that small snapshots can foresee their future shapes. Last, we use curriculum pseudo-labeling to mitigate the impact of label scarcity and noisy labels, and to correct representations learned from corrupted data. Experimental results on three benchmark datasets demonstrate that RERD consistently outperforms competitors in classic, early, and robust rumor detection scenarios. To the best of our knowledge, we are the first to simultaneously cope with early and five robust detections of rumors. [ABSTRACT FROM AUTHOR] more...

At present, most rumor detection work mainly based on the original text content, communication structure and communication text content of Twitter or Weibo. However, these methods ignored the effective integration of original text features with other features, as well as the role of propagating users in the process of rumor propagation. Aiming at the shortcomings of the existing work, a multi-feature fusion model GCNs-BERT based on graph convolutional network was proposed, which combined the features of the original text, the propagating user and the propagating structure. Firstly, a propagation graph was constructed based on the propagation structure and the propagation users, and the combination of multiple user attributes was used as the propagation node feature. Then, multiple graph convolutional networks were used to learn the representation of the propagation graph with different user attribute combinations, and BERT model was used to learn the feature representation of the original text content. Finally, the fusion with the features learned by the graph convolutional network was used to detect rumors. A large number of experiments using publicly available Weibo data sets showed that the GCNs-BERT model was significantly better than the baseline method. In addition, the generalization ability experiment of GCNs-BERT model was conducted on the novel coronavirus epidemic data set. The training sample size of this data set was only 1/ 5 of that of the public Weibo data set, and the accuracy rate was still 92. 5%, which proved that the model had good generalization ability. [ABSTRACT FROM AUTHOR] more...

The rapid dissemination of unverified information through social platforms like Twitter poses considerable dangers to societal stability. Identifying real versus fake claims is challenging, and previous work on rumor detection methods often fails to effectively capture propagation structure features. These methods also often overlook the presence of comments irrelevant to the discussion topic of the source post. To address this, we introduce a novel approach: the Structure-Aware Multilevel Graph Attention Network (SAMGAT) for rumor classification. SAMGAT employs a dynamic attention mechanism that blends GATv2 and dot-product attention to capture the contextual relationships between posts, allowing for varying attention scores based on the stance of the central node. The model incorporates a structure-aware attention mechanism that learns attention weights that can indicate the existence of edges, effectively reflecting the propagation structure of rumors. Moreover, SAMGAT incorporates a top-k attention filtering mechanism to select the most relevant neighboring nodes, enhancing its ability to focus on the key structural features of rumor propagation. Furthermore, SAMGAT includes a claim-guided attention pooling mechanism with a thresholding step to focus on the most informative posts when constructing the event representation. Experimental results on benchmark datasets demonstrate that SAMGAT outperforms state-of-the-art methods in identifying rumors and improves the effectiveness of early rumor detection. [ABSTRACT FROM AUTHOR] more...