Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks

Xing, Yunfei; Wang, Xiwei; Wang, Feng-Kwei; Shi, Yang; He, Wu; Chang, Haowu

doi:10.4018/jgim.20210701.oa5

Cited by 10 publications

(3 citation statements)

References 66 publications

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“…Traditional rumor detection methods usually use feature engineering to extract features from user's profiles [7,8], text content [9,10], and propagation patterns [11][12][13] and train classifiers based on these features to detect rumors. [18] proposed an algorithm based on the information entropy theory, which can quantitatively analyze the influence of Weibo users. Other scholars use NLP and machine learning methods to extract abstracts [19] and emotions [20] from text content.…”

Section: Related Workmentioning

confidence: 99%

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining

Chen,

Li,

Yang

2023

Advances in Multimedia

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This paper addresses the problem of detecting internet rumors in social media. Rumors do great harm to information society, making rumor detection necessary. However, existing methods for detecting rumors generally only learn pattern features or text content features from the whole propagation process, which fall short in capturing multilevel features with topic relevance of text content from social media data. In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. We build a heterogeneous tweet graph for each single-level feature GCN to encode the topic relation among tweets based on the text contents. Experiments on real-world Twitter data demonstrate that our proposed approach achieves much better performance than the state-of-the-art methods with higher values of precision and recall as well as their corresponding F1 score. In addition, the diversity of our experimental results shows the generalization ability of our model.

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Section: Related Workmentioning

confidence: 99%

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining

Chen,

Li,

Yang

2023

Advances in Multimedia

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“…First, the limited attention is based on the argument that investors can only respond to a portion of information (Bernard & Thomas, 1989;Kahneman, 1973;Kohlhas & Walther, 2021;Peng & Xiong, 2006). Second, the platform can identify the most suitable investors based on the algorithm (Shin, 2021;Xing et al, 2021), which may generate a specific preference of investors (Badham & Mykkänen, 2022;Bruns, 2012;Kim, 2016).…”

Section: Audiences and Platformsmentioning

confidence: 99%

Media Platforms and Stock Performance

Jin

Wang

Tan

et al. 2022

Journal of Global Information Management

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Media-aware stock performance has been well recognized in recent studies. Previous research, however, focused on the content influence of the media, ignoring the manner in which the media is delivered. Based on the trust theory, this study argues that the media platforms, as media distribution vehicles and trust endorsement for news, are themselves influential on the stock market. This paper collected news data from seven Chinese mainstream media platforms and classified them into official, professional, and mass media platforms to investigate the impact of different platforms. The authors find that high official and professional media coverage predict increased abnormal returns, while high mass media coverage predicts the opposite. In addition, this paper systematically explores the mechanism of media platforms on stock performance from the perspectives of platform content, audience, and publication timeliness. The findings include that investors' attention to media platforms has a moderating effect on the stock performance, and such an effect is more salient in bear markets.

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“…In the field of natural language processing, text generation is an important research direction. How to automatically generate smooth and fluent text that meets the user's preferences has always been the research focus in this field, and at the same time, it has high application value [9]. At present, the manually labeled data can hardly meet the needs of deep learning.…”

Section: Introductionmentioning

confidence: 99%

New Media Public Relations Regulation Strategy Model Based on Generative Confrontation Network

2022

Mobile Information Systems

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The rapid development of new media weakens the control of traditional news media on information. At the same time, under the impact of the rapid development of new media, it has brought new challenges to the regulation of public relations. In the new media environment, this paper constructs a text emotion generation model based on GAN to support the application of new media public relations regulation strategy. Aiming at the problem of insufficient constraint information of keywords in text generation, this paper uses the confrontation generation model based on reinforcement learning to supplement sentence components around keywords, so as to generate the text with the highest quality. At the same time, in order to extend GAN from continuous space to discrete space, the differentiable function based on Softmax transformation is adopted to approach the original nondifferentiable function. In this paper, LTP word segmentation system is used to select 356742 pieces of data with a length less than 20 for the experiment. Compared with Seqtoseq+attenion and Transform models, this model has higher similarity of real text distribution and higher text diversity. The retention degree of the main content of the input text is as high as 96.17%, which is higher than that of Seqtoseq+attenion model of 8.49% and Transform model of 6.11%. It provides effective support for the regulation of new media public relations.

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Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks

Cited by 10 publications

References 66 publications

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining

Media Platforms and Stock Performance

New Media Public Relations Regulation Strategy Model Based on Generative Confrontation Network

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