期刊文献+
共找到3篇文章
< 1 >
每页显示 20 50 100
Microblogging Reposting Mechanism: An Information Adoption Perspective 被引量:4
1
作者 Wei Yan Jinghua Huang 《Tsinghua Science and Technology》 SCIE EI CAS 2014年第5期531-542,共12页
This study uses the Elaboration Likelihood Model (ELM) and social presence theory to examine the microblogging reposting mechanism. Subjective and objective data were collected from 216 respondents in a field experi... This study uses the Elaboration Likelihood Model (ELM) and social presence theory to examine the microblogging reposting mechanism. Subjective and objective data were collected from 216 respondents in a field experiment. The results indicate that information quality and source credibility of microblogging messages affect users' reposting intention by affecting their perceptions of the usefulness and enjoyment of the information. Perceived enjoyment has a greater impact on reposting intention than perceived usefulness. Furthermore, users are able to perceive social presence when interacting with microblogging messages. Social presence plays a full mediating role between information quality and perceived enjoyment, and a partial mediating role between information quality and perceived usefulness. 展开更多
关键词 MICROBLOGGING reposting information adoption elaboration likelihood model social presence field experiment
原文传递
The Direct and Reposting Effects of Advertorials on Sales
2
作者 Xia Wang Chunling Yu Lily C. Dong 《Frontiers of Business Research in China》 2016年第3期451-469,共19页
Advertorials have been shown to influence advertising recall and brand evaluation. However, no research has examined their impact on sales. This study uses longitudinal data on durable consumer goods in China to asses... Advertorials have been shown to influence advertising recall and brand evaluation. However, no research has examined their impact on sales. This study uses longitudinal data on durable consumer goods in China to assess the extent to which advertorials influence sales. The study finds that advertorials have dual, positive effects on firms' sales: a direct effect through original copy and a reposting effect through online exposure. 展开更多
关键词 advertorial ADVERTISING reposting effect
原文传递
Uncovering Causal Relationships for Debiased Repost Prediction Using Deep Generative Models
3
作者 Wu-Jiu Sun Xiao Fan Liu 《Computers, Materials & Continua》 SCIE EI 2024年第12期4551-4573,共23页
Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information online.Accurately predicting information spread,such as users’reposting activities,is essential for ap... Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information online.Accurately predicting information spread,such as users’reposting activities,is essential for applications including content recommendation and analyzing public sentiment.Current advanced models rely on deep representation learning to extract features from various inputs,such as users’social connections and repost history,to forecast reposting behavior.Nonetheless,these models frequently ignore intrinsic confounding factors,which may cause the models to capture spurious relationships,ultimately impacting prediction performance.To address this limitation,we propose a novel Debiased Reposting Prediction model(DRP).Our model mitigates the influence of confounding variables by incorporating intervention operations from causal inference,enabling it to learn the causal associations between features and user reposting behavior.Specifically,we introduce a memory network within DRP to enhance the model’s perception of confounder distributions.This network aggregates and learns confounding information dispersed across different training data batches by optimizing the reconstruction loss.Furthermore,recognizing the challenge of acquiring prior knowledge of causal graphs,which is crucial for causal inference,we develop a causal discovery module within DRP(CD-DRP).This module allows the model to autonomously uncover the causal graph of feature variables by analyzing microblogging data.Experimental results on multiple real-world datasets demonstrate that our proposed method effectively uncovers causal relationships between variables,exhibits strong time efficiency,and outperforms state-of-the-art models in prediction performance(improved by 2.54%)and overfitting reduction(by 7.44%). 展开更多
关键词 Repost prediction causal inference causal discovery memory network
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部