Rumour Detection Based on Graph Convolutional Neural Net

Bai, Na; Meng, Fei; Rui, Xiaobin; Wang, Zhixiao

doi:10.1109/access.2021.3050563

Cited by 21 publications

(9 citation statements)

References 13 publications

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“…In this section, the efficiency of the T-EGCN model is analyzed by implementing it in Java. Also, its efficiency is compared with the classical models: EGCN [19], PGNN [22], RNN [23], GCN [25] and BiLSTM-CNN [28]. The comparative analysis is conducted regarding accuracy, precision, recall, and f-measure.…”

Section: Resultsmentioning

confidence: 99%

“…Bai et al [19] designed the SR-graph which utilizes the global structural attributes and content data entirely. By using the SR-graphs, an EGCN with NPAM was developed to identify the rumor.…”

Section: Literature Surveymentioning

confidence: 99%

“…To enhance the text/rumor recognition performance the Text CNN model was incorporated with the GCN called EGCN using the NPAM, which utilizes global structural attributes and content attributes between the source tweets and their responses [19]. The rumor recognition problem was converted into the text classification problem.…”

Section: Overview Of Egcn Modelmentioning

confidence: 99%

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Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification

Rajasekar¹,

Robert²

2022

RIA

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In social networks, rumor identification is a major problem. The structural data in a topic is applied to derive useful attributes for rumor identification. Most standard rumor identification methods concentrate on local structural attributes, ignoring the global structural attributes that exist between the source tweet and its responses. To tackle this issue, a Source-Replies relation Graph (SR-graph) has been built to develop an Ensemble Graph Convolutional neural Net (EGCN) with a Nodes Proportion Allocation Mechanism (NPAM) which identifies the rumor. But, the word vectors were trained by the standard word-embedding model which does not increase the accuracy for large Twitter databases. To solve this problem, an unsupervised word-embedding method is needed for large Twitter corpora. As a result, the Twitter word-embedded EGCN (T-EGCN) model is proposed in this article, which uses unsupervised learning-based word embedding to find rumors in huge Twitter databases. Initially, the latent contextual semantic correlation and co-occurrence statistical attributes among words in tweets are extracted. Then, to create a rumor attribute vector of tweets, these word embeddings are concatenated with the GloVe model's word attribute vectors, Twitter-specific attributes, and n-gram attributes. Further, the EGCN is trained by using this attribute vector to identify rumors in a huge Twitter database. Finally, the testing results exhibit that the T-EGCN achieves 87.56% accuracy, whereas the RNN, GCN, PGNN, EGCN, and BiLSTM-CNN attain 65.38%, 68.41%, 75.04%, 81.87%, and 86.12%, respectively for rumor identification.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification

Rajasekar¹,

Robert²

2022

RIA

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“…However, they did not take into account the time series features in the life cycle of rumors. The graph convolution neural network [31,32] derived from the convolution neural network and the improved model EGCN [33] convert the microblog rumor data to graph data. Then, they use the convolution neural network to train the labeled data.…”

Section: Methods Based On Deep Learningmentioning

confidence: 99%

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

Peng

Wang

2021

Future Internet

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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.

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“…The knowledge concepts were retrieved relevant knowledge from Knowledge Graphs (KG), the visual information was obtained with object detection techniques, and the text information was obtained with GCN. BAI et al [12] firstly built a Source-Replies relationship graph (SRgraph) for each conversation, using nodes to represent a tweet, and edges to represent interactive tweets between them. Based on SR-graphs, an integrated graph convolutional neural network with node proportional allocation mechanism (EGCN) was proposed for Twitter rumor detection.…”

Section: Introductionmentioning

confidence: 99%

Rumors Detection Based on Lifelong Machine Learning

Tuerhong

Wushouer

et al. 2022

IEEE Access

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The amount of training data in the field of Weibo rumor detection is small, and online news changes constantly, but the existing rumor detection models do not have the ability of continuous learning, they also cannot achieve knowledge accumulation and update, they usually need a large number of training examples to improve the effect. In contrast, Lifelong Machine Learning (LML) paradigm has the ability of continuous learning, which retains the knowledge learned in the past and uses it to help future learning tasks, after learning, some knowledge is updated. With the growth and update of knowledge, the performance of each task will be better and better. Hence, we use this paradigm to build a Weibo rumor detection model. We firstly extracted three types of features based on content, user, and propagation from Weibo events, and proposed three new propagation features and Bidirectional Encoder Representations from Transformers (BERT) semantic features of source message for rumor detection. We then used Simulated Annealing (SA) to improve the Genetic Algorithm (GA), which was called GA-SA used to search for the best global minimum feature subset to improve the classification effect of the Efficient Lifelong Learning Algorithm (ELLA) on rumors. In the continuous learning process, the ELLA transfers knowledge to learn new tasks and refines knowledge over time to maximize performance across all tasks. The proposed model is called GA-SA-ELLA. The experimental results show that our model even in the case of less training data for each task could achieve superior detection results.INDEX TERMS Lifelong Machine Learning, continuous learning, Weibo rumor, the best minimum feature.

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Rumour Detection Based on Graph Convolutional Neural Net

Cited by 21 publications

References 13 publications

Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification

Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification

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

Rumors Detection Based on Lifelong Machine Learning

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