Deep spatial–temporal structure learning for rumor detection on Twitter

Huang, Qi; Zhou, Chuan; Wu, Jia; Liu, Luchen; Wang, Bin

doi:10.1007/s00521-020-05236-4

Cited by 41 publications

(27 citation statements)

References 17 publications

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“…Here, single content type is used to predict the fake or real classification of the information over the social platform like image, text, context, and user profile. Huang et al [ 25 ] proposed the spatial–temporal structural neural network framework to model the message spread from temporal and spatial perspectives for rumour detection. It worked fine for rumours, but the propagation of fake images was not considered.…”

Section: Related Workmentioning

confidence: 99%

Predicting image credibility in fake news over social media using multi-modal approach

Singh

Sharma

2021

Neural Comput & Applic

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Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.

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Section: Related Workmentioning

confidence: 99%

Predicting image credibility in fake news over social media using multi-modal approach

Singh

Sharma

2021

Neural Comput & Applic

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“…Although issues about rumors have been investigated intensively in multiple disciplines, the number of studies on the detection of rumors in social media is still limited [ 2 ]. In general, rumor detection could be viewed as a binary classification problem using machine learning algorithms, in which some features obtained from social media data are considered [ 2 , 7 , 26 ]. Some previous studies stated that different learning algorithms may achieve similar results [ 15 , 16 ].…”

Section: Related Workmentioning

confidence: 99%

“…These features seem to be the most direct proof to exactly judge whether a certain post is a rumor or not, and this has been fully explored by previous studies [ 3 ]. In addition, it is important to note that the diffusion-based set of features, e.g., the number of retweets and comments, are commonly employed to detect rumors [ 7 , 14 , 34 ]. However, the propagation process of rumors in social media is extremely that cannot be effectively and comprehensively represented by existing features, to a large extent.…”

Section: (2) Content-based Featuresmentioning

confidence: 99%

“…Alzanin and Azmi [ 1 ] put forward a semi-supervised system for detecting rumors in Arabic language social networks, which achieved an F1 score of 80% in the experiment. Existing rumor detection models are frequently established based on a wide range of features corresponding to account information, content of posts, and some other aspects [ 7 , 16 , 30 , 39 ]. However, there is still no effective methods or frameworks to connect these features.…”

Section: Introductionmentioning

confidence: 99%

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A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

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Based on the supernetwork theory, a two-step rumor detection model was proposed. The first step was the classification of users on the basis of user-based features. In the second step, non-user-based features, including psychology-based features, content-based features, and parts of supernetwork-based features, were used to detect rumors posted by different types of users. Four machine learning methods, namely, Naive Bayes, Neural Network, Support Vector Machine, and Logistic Regression, were applied to train the classifier. Four real cases and several assessment metrics were employed to verify the effectiveness of the proposed model. Performance of the model regarding early rumor detection was also evaluated by separating the datasets according to the posting time of posts. Results showed that this model exhibited better performance in rumor detection compared to five benchmark models, mainly owing to the application of the supernetwork theory and the two-step mechanism.

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“…Liu & Wu, 2020;Nakamura et al, 2020;. The propagation-based methods (Huang et al, 2020;Jiang et al, 2019;Y. Liu & Wu, 2018;Qian et al, 2018) utilize the information related to the dissemination of fake news, e.g., how users spread it.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

2021

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This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the stateof-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi-and cross-lingual machine learning in few-and zero-shot settings. 7

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Deep spatial–temporal structure learning for rumor detection on Twitter

Cited by 41 publications

References 17 publications

Predicting image credibility in fake news over social media using multi-modal approach

Predicting image credibility in fake news over social media using multi-modal approach

A two-step rumor detection model based on the supernetwork theory about Weibo

Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

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