Rumor Detection Research Articles

Algerian Arabizi rumour detection based on morphosyntactic analysis

Algerian Arabizi rumour detection based on morphosyntactic analysis

Algerian Arabizi rumour detection based on morphosyntactic analysis

Algerian Arabizi rumour detection based on morphosyntactic analysis

Multi-modal rumour detection using bilinear pooling and domain adversarial neural networks

Multi-modal rumour detection using bilinear pooling and domain adversarial neural networks

A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance

A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance

Multi-modal Rumor Detection Using Bilinear Pooling and Domain Adversarial Neural Networks

Multi-modal Rumor Detection Using Bilinear Pooling and Domain Adversarial Neural Networks

Retracted: A Rumor Detection Method from Social Network Based on Deep Learning in Big Data Environment.

[This retracts the article DOI: 10.1155/2022/1354233.].

Robust Rumor Detection based on Multi-Defense Model Ensemble

ABSTRACT The development of adversarial technology, represented by adversarial text, has brought new challenges to rumor detection based on deep learning. In order to improve the robustness of rumor detection models under adversarial conditions, we propose a robust detection method based on the ensemble of multi-defense model on the basis of several mainstream defense methods such as data enhancement, random smoothing, and adversarial training. First, multiple robust detection models are trained based on different defense principles; then, two different ensemble strategies are used to integrate the above models, and the detection effect under different ensemble strategies is studied. The test results on the open-source dataset Twitter15 show that the proposed method is able to compensate for the shortcomings of a single model by ensembling different decision boundaries to effectively defend against mainstream adversarial text attacks and improve the robustness of rumor detection models compared to existing defense methods.

Combining Temporal and Interactive Features for Rumor Detection: A Graph Neural Network Based Model

Combining Temporal and Interactive Features for Rumor Detection: A Graph Neural Network Based Model

Rumor detection with self-supervised learning on texts and social graph

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g., social network, or post content) or ignoring the relations among multiple sources (e.g., fusing social and content features via simple concatenation). Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination. Specifically, given two heterogeneous views of a post (i.e., representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as self-supervised rumor detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.

Rumour detection technology based on the BiGRU_capsule network

Rumour detection technology based on the BiGRU_capsule network

A graph-based pivotal semantic mining framework for rumor detection

As the gathering place for modern content sharing, social platforms not only provide places for people to obtain information and express opinions, but also provide hidden channels for the generation and dissemination of rumors. Rumor detection is an important and challenging task for mining fake information in social networks. Previous methods utilized sequential models to embed semantic features, which are not comprehensive for mining pivotal semantic information and easy to ignore discontinuous dependencies. However, excessive mining of information content will lead to the acquisition of some redundant information, whose existence will affect the extraction of useful features and the detection ability of the model. To address these issues, this paper offers a graph-based pivotal semantic mining framework. Specifically, we model the content information as a graph structure, learn the semantic dependencies across segments through a gated graph neural network, and co-learn by combining the propagation features of rumors. Furthermore, in order to highlight the precious essential semantic information, a shared unit cell is offered to reduce the influence of redundant information. Experimental results on realworld datasets show that the proposed method exceeds existing methods in terms of benchmark testing.

Identifying Cantonese rumors with discriminative feature integration in online social networks

To reduce the negative impacts of rumors on the real world, rumor detection on social networks has practical significance. Currently, the research on Chinese rumor detection is relatively comprehensive, but Cantonese rumors are much less investigated. As a main dialect of Chinese, Cantonese has more than 60 million speakers globally, but there are some great challenges in the Cantonese rumor detection. Firstly, there is no available benchmark dataset of Cantonese rumors. Secondly, it is a significant challenge to learn the unique linguistic characteristics of Cantonese. Thirdly, traditional rumor detection approaches cannot be directly applied to Cantonese rumors. Therefore, we propose a novel framework for Cantonese rumor detection using deep neural networks with feature fusion. To the best of our knowledge, it is the first study conducted on Cantonese rumor detection on social networks. Specifically, we build a Cantonese rumor dataset and a multi-domain Cantonese corpus. Next, a total of 27 statistical features are extracted and seven of them are newly proposed. Then, a novel deep learning model called BLA is designed to identify Cantonese rumors, which generates text and Jyutping embeddings using a further pre-trained BERT model and a CNN model. Moreover, the BLA model integrates the statistical and semantic features to implement the classification of Cantonese rumors. Experiments demonstrate that the BLA model achieves a remarkable Cantonese rumor detection performance with an F1 Score of 0.9225.

A novel rumor detection with multi-objective loss functions in online social networks

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.

Leverage Boosting and Transformer on Text-Image Matching for Cheap Fakes Detection

The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-AI techniques such as unaltered images with false context news to create false news, which makes it easy and “cheap” to create and leads to an abundant amount in the social media community. Moreover, the development of deep learning also opens and invents many domains relevant to news such as fake news detection, rumour detection, fact-checking, and verification of claimed images. Nevertheless, despite the impact on and harmfulness of cheapfakes for the social community and the real world, there is little research on detecting cheapfakes in the computer science domain. It is challenging to detect misused/false/out-of-context pairs of images and captions, even with human effort, because of the complex correlation between the attached image and the veracity of the caption content. Existing research focuses mostly on training and evaluating on given dataset, which makes the proposal limited in terms of categories, semantics and situations based on the characteristics of the dataset. In this paper, to address these issues, we aimed to leverage textual semantics understanding from the large corpus and integrated with different combinations of text-image matching and image captioning methods via ANN/Transformer boosting schema to classify a triple of (image, caption1, caption2) into OOC (out-of-context) and NOOC (no out-of-context) labels. We customized these combinations according to various exceptional cases that we observed during data analysis. We evaluate our approach using the dataset and evaluation metrics provided by the COSMOS baseline. Compared to other methods, including the baseline, our method achieves the highest Accuracy, Recall, and F1 scores.

AN EFFECTIVE IDENTIFICATION OF TRUTHFUL INFORMATION USING POLYMERIZATION SENTIMENTAL MODLEL

Despite the increasing use of social media platforms for information and news gathering, its immoderate nature will create peoples like keyboard warriors, rumormongers etc. This often leads to the emergence and spreading of rumors, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumors, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey, we introduce the Big Data technology and discuss two types of rumors that circulate on social media; long-standing rumors that circulate for long periods of time, and newlyemerging rumors spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of datasets into social media rumors with the ultimate goal of developing a rumor classification system that consists of four components: rumor detection, rumor tracking, rumor stance classification and rumor veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarize the efforts and achievements so far towards the development of rumor classification systems and conclude with suggestions for avenues for future research in social media mining for detection and resolution of rumors

Improving Rumor Detection by Image Captioning and Multi-Cell Bi-RNN With Self-Attention in Social Networks

User-generated contents in social media are not verified before being posted. They could bring many problems if they were misused. Among various types of rumors, the authors focus on the type in which there's mismatch between images and their surrounding texts. They can be detected by multimodal feature fusion in RNNs with attention mechanism, but the relations between images and texts are not well-addressed. In this paper, the authors propose to improve rumor detection by image captioning and RNNs with self-attention. Firstly, they utilize the idea of image captioning to translate images into the corresponding text descriptions. Secondly, these caption words are represented by word embedding models and aggregated with surrounding texts using early fusion. Finally, multi-cell bi-directional RNNs with self-attention are used to learn important features to identify rumors. From the experimental results, the best F-measure of 0.882 can be obtained, which shows the potential of our proposed approach to rumor detection. Further investigation is needed for data in larger scale.

How rumors diffuse in the infodemic: Evidence from the healthy online social change in China

Infodemic is defined as ‘an overabundance of information-some accurate and some not-that makes it hard for people to find trustworthy sources and reliable guidance when they need it’ by the World Health Organization. As unverified information, rumors can widely spread in online society, further diffusing infodemic. Existed studies mainly focused on rumor detection and prediction from the statement itself and give the probability that it will evolve into a rumor in the future. However, the detection and prediction from rumors production perspective is lack. This research explores the production mechanism from the uncertainty perspective using the data from Weibo and public rumor data set. Specifically, we identify the public uncertainty through user-generated content on social media based on systemic functional linguistics theory. Then we empirically verify the promoting effect of uncertainty on rumor production and constructed a model for rumor prediction. The fitting effect of the empirical model with the public uncertainty is significantly better than that with only control variables, indicating that our framework identifies public uncertainty well and uncertainty has a significantly predictive effect on rumors. Our study contributes to the research of rumor prediction and uncertainty identification, providing implications for healthy online social change in the post-epidemic era.

Rumor detection driven by graph attention capsule network on dynamic propagation structures

Rumor detection aims to judge the authenticity of posts on social media (such as Weibo and Twitter), which can effectively prevent the spread of rumors. While many recent rumor detection methods based on graph neural networks can be conducive to extracting the global features of rumors, each node of the rumor propagation structure learned from graph neural networks is considered to have multiple individual scalar features, which are insufficient for reflecting the deep-level rumor properties. To address the above challenge, we propose a novel model named graph attention capsule network on dynamic propagation structures (GACN) for rumor detection. Specifically, GACN consists of two components: a graph attention network enforced by capsule network that can encode static graphs into substructure classification capsules for mining the deep-level properties of rumor, and a dynamic network framework that can divide the rumor structure into multiple static graphs in chronological order for capturing the dynamic interactive features in the evolving process of the rumor propagation structure. Moreover, we use the capsule attention mechanism to combine the capsules generated from each substructure to focus more on informative substructures in rumor propagation. Extensive validation on two real-world datasets demonstrates the superiority of GACN over baselines.

A Rumor Detection Method Based on Multimodal Feature Fusion by a Joining Aggregation Structure

Online rumors spread rapidly through social media, which is a great threat to public safety. Existing solutions are mainly based on content features or propagation structures for rumor detection. However, due to the variety of strategies in creating rumors, only considering certain features cannot achieve good enough detection results. In addition, existing works only consider the rumor propagation structure and ignore the aggregation structures of rumors, which cannot provide enough discriminative features (especially in the early days of rumors, when the structure of the propagation is incomplete). To solve these problems, this paper proposes a rumor detection method with multimodal feature fusion and enhances the feature representation of the rumor propagation network by adding aggregation features. More specifically, we built a graph model of the propagation structure as well as the aggregation structure. Next, by utilizing the BERT pre-training model and the bidirectional graph convolutional network, we captured the features of text content, propagation structure, and aggregation structure, respectively. Finally, the multimodal features were aggregated based on the attention mechanism, and the final result was obtained through the MLP classifier. Experiments on real-world datasets show that our model outperforms state-of-the-art approaches.

Adaptive cost-sensitive stance classification model for rumor detection in social networks

As online social networks are experiencing extreme popularity growth, determining the veracity of online statements denoted by rumors automatically as earliest as possible is essential to prevent the harmful effects of propagating misinformation. Early detection of rumors is facilitated by considering the wisdom of the crowd through analyzing different attitudes expressed toward a rumor (i.e., users’ stances). Stance detection is an imbalanced problem as the querying and denying stances against a given rumor are significantly less than supportive and commenting stances. However, the success of stance-based rumor detection significantly depends on the efficient detection of “query” and “deny” classes. The imbalance problem has led the previous stance classifier models to bias toward the majority classes and ignore the minority ones. Consequently, the stance and subsequently rumor classifiers have been faced with the problem of low performance. This paper proposes a novel adaptive cost-sensitive loss function for learning imbalanced stance data using deep neural networks, which improves the performance of stance classifiers in rare classes. The proposed loss function is a cost-sensitive form of cross-entropy loss. In contrast to most of the existing cost-sensitive deep neural network models, the utilized cost matrix is not manually set but adaptively tuned during the learning process. Hence, the contributions of the proposed method are both in the formulation of the loss function and the algorithm for calculating adaptive costs. The experimental results of applying the proposed algorithm to stance classification of real Twitter and Reddit data demonstrate its capability in detecting rare classes while improving the overall performance. The proposed method improves the mean F-score of rare classes by about 13% in RumorEval 2017 dataset and about 20% in RumorEval 2019 dataset.