A Deep Content-Based Model for Persian Rumor Verification

During the development of social media, there has been a transformation in social communication. Despite their positive applications in social interactions and news spread, it also provides an ideal platform for spreading rumors. Rumors can endanger the security of society in normal or critical situations. Therefore, it is important to detect and verify the rumors in the early stage of their spreading. Many research works have focused on social attributes in the social network to solve the problem of rumor detection and verification, while less attention has been paid to content features. The social and structural features of rumors develop over time and are not available in the early stage of rumor. Therefore, this study presented a content-based model to verify the Persian rumors on Twitter and Telegram early. The proposed model demonstrates the important role of content in spreading rumors and generates a better-integrated representation for each source rumor document by fusing its semantic, pragmatic, and syntactic information. First, contextual word embeddings of the source rumor are generated by a hybrid model based on ParsBERT and parallel CapsNets. Then, pragmatic and syntactic features of the rumor are extracted and concatenated with embeddings to capture the rich information for rumor verification. Experimental results on real-world datasets demonstrated that the proposed model significantly outperforms the state-of-the-art models in the early rumor verification task. Also, it can enhance the performance of the classifier from 2% to 11% on Twitter and from 5% to 23% on Telegram. These results validate the model's effectiveness when limited content information is available.

Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection

Social media has become more popular these days due to widely used instant messaging. Nevertheless, rumor propagation on social media has become an increasingly important issue. The purpose of this study is to investigate the impact of various features in social media on rumor detection, propose a dual co-attention-based multi-feature fusion method for rumor detection, and explore the detection capability of the proposed method in early rumor detection tasks. The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding . It simultaneously integrates features from three sources: publishing user profiles, source tweets, and comments. In the BDCoNN method, user discrete features and identity descriptors in user profiles are extracted using a one-dimensional convolutional neural network (CNN) and TextCNN, respectively. The bidirectional gate recurrent unit network (BiGRU) with a hierarchical attention mechanism is used to learn the hidden layer representation of tweet sequence and comment sequence. A dual collaborative attention mechanism is used to explore the correlation among publishing user profiles, tweet content, and comments. Then the feature vector is fed into classifier to identify the implicit differences between rumor spreaders and non-rumor spreaders. In this study, we conducted several experiments on the Weibo and CED datasets collected from microblog. The results show that the proposed method achieves the state-of-the-art performance compared with baseline methods, which is 5.2% and 5% higher than the dEFEND. The F1 value is increased by 4.4% and 4%, respectively. In addition, this paper conducts research on early rumor detection tasks, which verifies the proposed method detects rumors more quickly and accurately than competitors.

Editorial

It gives me great pleasure to announce the sixth regular issue of 2021. All this is only possible thanks to the great support of the J.UCS community. Therefore, I would like to thank all the authors for their sound research and the editorial board for the highly valuable reviews and suggestions for improvement. These contributions together with the generous support of the consortium members sustain the quality of our journal. We are always interested in receiving high quality proposals for special issues on new topics and emerging trends. Please consider yourself and encourage your colleagues to submit high quality articles to our journal. I am also still looking to expand our editorial board: If you are a tenured associate professor or higher and have a good publication record, please feel free to apply to join our editorial board. In this regular issue, I am very pleased to introduce six accepted papers contributed by 17 authors from six different countries. Rochdi Boudjehem and Yacine Lafifi from Algeria outline their research on how to identify and assist struggling learners by monitoring and analyzing their behavior within the e-learning environment. Abdelouafi Ikidid, Abdelaziz El Fazziki and Mohammed Sadgal from Morocco introduce a fuzzy logic-based multi-agent system for traffic light control at a signalized intersection by acting on the length and sequence of traffic light phases to favor priority flows and make traffic flow more smoothly at an isolated intersection and for the entire network with multiple intersections. Andrea Lezcano Airaldi, Jorge Andrés Diaz-Pace and Emanuel Irrazábal from Argentina conducted a case study to evaluate the benefits of incorporating data-driven storytelling into the development of a software system to support decision-making in crisis settings. José Monteiro, Maria Bernardo, Mafalda Ferreira, and Tânia Rocha from Portugal discuss their study of quality aspects of e-government information with the goal of better understanding which of these attributes are most valued from the users’ perspective when evaluating content provided by government websites. Ricardo Pérez- Castillo and Mario Piattini from Spain outline their study on how the evolution of the development effort influences the code quality, which was analyzed on 13 open source projects. Hamda Slimi, Ibrahim Bounhas and Yahya Slimani from Tunisia discuss their approach, which aims to detect emerging and unseen rumors on Twitter by adapting a pre-trained language model, namely RoBERTa, to the task of rumor detection.

Adapting Pre-trained Language Models to Rumor Detection on Twitter

Fake news has invaded social media platforms where false information is being propagated with malicious intent at a fast pace. These circumstances required the development of solutions to monitor and detect rumor in a timely manner. In this paper, we propose an approach that seeks to detect emerging and unseen rumors on Twitter by adapting a pre-trained language model to the task of rumor detection, namely RoBERTa. A comparison against content-based characteristics has shown the capability of the model to surpass handcrafted features. Experimental results show that our approach outperforms state of the art ones in all metrics and that the fine tuning of RoBERTa led to richer word embeddings that consistently and significantly enhance the precision of rumor recognition.

Rumor Detection Based on Attention CNN and Time Series of Context Information

This study aims to explore the time series context and sentiment polarity features of rumors’ life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors’ classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.