Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction

2021 ◽  
Author(s):  
Fan Zhou ◽  
Liu Yu ◽  
Xovee Xu ◽  
Goce Trajcevski
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

Information cascades in online reading: an empirical investigation of panel data

2014 ◽  
Vol 32 (4) ◽  
pp. 687-705 ◽  
Author(s):  
Qihua Liu ◽  
Liyi Zhang
Keyword(s):  
Market Share ◽  
Web Sites ◽  
Network Externalities ◽  
Book Reading ◽  
Information Cascades ◽  
Online Reading ◽  
Information Cascade ◽  
Reading Behavior ◽  
Informational Cascades ◽  
Content Type

Purpose – The purpose of this paper is to examine information cascades in the context of users’ e-book reading behavior and differentiate it from alternative factors that lead to herd behavior, such as network externalities and word-of-mouth effects. Design/methodology/approach – This paper constructed panel data using information concerning 226 e-books in 30 consecutive days from Sina.com’s reading channel (Book.Sina.com.cn) from October 2, 2013, to October 31, 2013 of the same year in China. A multinomial logit market-share model was employed. Findings – E-books’ ranking has a significant impact on their market share, as predicted by informational cascades theory. Higher ranking e-books’ clicks will see a greater increase as a result of an increase in clicks ranking. Due to the information cascades effect, review volume had no impact on the market share of popular e-books. Total votes had a powerful impact on the market share of e-books, showing that once information cascade occurred, it could be enhanced by the increase in total votes. The total clicks of e-books had a significant impact on their market share, suggesting that online reading behavior would be influenced by network externalities. Practical implications – As important information, the ranking or popularity of e-books should be carefully considered by online reading web sites, publishers, and authors. It is not enough for the authors and publishers of e-books to simply pay attention to the content. They should design their marketing strategies to allow network externalities and informational cascades to work for them, not against them. Online reading web sites should also focus on eliminating certain behavior, such as “brush clicks” and “brush votes,” in order to prevent an undesirable information cascade due to false information. Originality/value – To the best of the knowledge, this is the first study to examine information cascades in the context of users’ e-book reading behavior. Moreover, this study can help other researchers by utilizing a large sample of daily data from one of the earliest online reading platforms in China.

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