Hidden Costs of Epistemic Conformity: Lessons from Information Cascade Simulations

2021 ◽  
Author(s):  
Patricia Rich
Keyword(s):  
Information Cascade

scholarly journals Information cascade on networks

2016 ◽  
Vol 450 ◽  
pp. 570-584 ◽  
Author(s):  
Masato Hisakado ◽  
Shintaro Mori
Keyword(s):  
Information Cascade

scholarly journals What Causes Herding: Information Cascade or Search Cost?

2010 ◽  
Author(s):  
William T. LIn ◽  
Shih-Chuan Tsai ◽  
David S. Sun
Keyword(s):  
Search Cost ◽  
Information Cascade

information cascade experiments

2010 ◽  
pp. 166-167
Author(s):  
Lisa Anderson ◽  
Charles A. Holt
Keyword(s):  
Information Cascade

scholarly journals A Measurement Model of Mutual Influence for Information Dissemination

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 725
Author(s):  
Liang Zhang ◽  
Yong Quan ◽  
Bin Zhou ◽  
Yan Jia ◽  
Liqun Gao
Keyword(s):  
Social Network ◽  
Information Dissemination ◽  
Mutual Influence ◽  
Synthetic Data ◽  
Measurement Model ◽  
Mobile Internet ◽  
Information Propagation ◽  
Information Cascade ◽  
Data Set ◽  
Access Information

The recent development of the mobile Internet and the rise of social media have significantly enriched the way people access information. Accurate modeling of the probability of information propagation between users is essential for studying information dissemination issues in social networks. As the dissemination of information is inseparable from the interactions between users, the probability of propagation can be characterized by such interactions. In general, there are differences in the dissemination modes of information that carry different topics in a real social network. Using these factors, we propose a method (TMIVM) to measure the mutual influence between users at the topic level. The method associates two vectorization parameters for each user—an influence vector and a susceptibility vector—where the dimensions of the vector represent different topic categories. The magnitude of the mutual influence between users on different topics can be obtained by the product of the corresponding elements of the vectors. Specifically, in this article, we fit a social network historical information cascade data through Survival Analysis to learn the parameters of the influence and susceptibility vectors. The experimental results on a synthetic data set and a real Microblog data set show that this method better measures the propagation probability and information cascade predictions compared to other methods.

scholarly journals Predicting Information Cascade on Twitter Using Random Walk

2020 ◽  
Vol 173 ◽  
pp. 201-209
Author(s):  
Nidhi Singh ◽  
Anurag Singh ◽  
Rajesh Sharma
Keyword(s):  
Random Walk ◽  
Information Cascade

scholarly journals Information cascade prediction based on T-DeepHawkes model

2020 ◽  
Vol 715 ◽  
pp. 012042
Author(s):  
Shijie Wang ◽  
Lihua Zhou ◽  
Bing Kong
Keyword(s):  
Information Cascade
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