作者机构：
[Jing Chen; Lu Zhang] School of Information Management, Central China Normal University, Wuhan 430079, China;Institute of Digital Commerce, Wuhan Technology and Business University, Wuhan 430065, China;Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China;[Hui Liu] Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China;[Shuaipu Chen] School of Information Management, Wuhan University, Wuhan 430072, China
通讯机构：
[Quan Lu] I;Institute of Digital Commerce, Wuhan Technology and Business University, Wuhan 430065, China<&wdkj&>Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
关键词：
Predicting information usefulness;Gaze;Gesture;Health information identification;Misinformation
摘要：
Finding useful health information should be the highest priority when identifying health information. Predicting information usefulness will significantly improve the effectiveness and efficiency of health information identification, which plays a vital role in fighting against misinformation. Modal behaviors, such as gesture and gaze, are promising indicators of usefulness since they deliver a reliable, thorough, natural, and direct process of user cognitive processing. Therefore, this study aimed to use gesture and gaze behaviors to predict whether information is useful for health information identification. Twenty-four college students were recruited to freely search for information using a smartphone to identify the truthfulness of four propositions (two were true and two were false) about public health epidemics. The participants' gesture behavior, gaze behavior, and information usefulness as perceived by themselves were collected. Based on user cognition, the process of information usefulness judgment was placed into two phases: skimming and reading. Thirty-one features derived from modal behaviors in each phase were extracted. Feature optimization based on the Mann-Whitney U test and random forest was performed. Five common algorithms were used to construct information usefulness prediction models, and these models were compared by the F1_score. Finally, dwell time and gaze entropy in the reading phase were the most important gesture and gaze features respectively. BP neural network was selected to build a unimodal model based on gesture, and gradient boosting decision tree was selected to build a unimodal model based on gaze and a multimodal model combining both. These models all achieved F1_score above 77% and were applicable to different scenarios in health information identification. The model based on gesture could satisfy strong technology or legal constrains, the model based on gaze was ideal for AR, MR or metaverse applications, and the model combining both offered an alternative for multimodal human-computer interaction.
期刊：
Information Processing & Management,2023年60(4):103348 ISSN：0306-4573
通讯作者：
Duantengchuan Li<&wdkj&>Yan Zhang
作者机构：
[Li, Zhifei; Zhang, Yan] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Hubei, Peoples R China.;[Zhang, Qi] Cent China Normal Univ, Sch Informat Management, Wuhan 430072, Hubei, Peoples R China.;[Zhu, Fangfang] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Hubei, Peoples R China.;[Zheng, Chao; Li, Duantengchuan] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.
通讯机构：
[Duantengchuan Li; Yan Zhang] S;School of Computer Science, Wuhan University, Wuhan, Hubei 430072, China<&wdkj&>School of Computer Science and Information Engineering, Hubei University, Wuhan, Hubei 430062, China
期刊：
Technological and Economic Development of Economy,2023年29(6):1728-1752 ISSN：2029-4913
通讯作者：
Gao, MY
作者机构：
[Xiao, Qinzi; Chen, Lin; Jiang, Juncheng] Wuhan Inst Technol, Sch Management, Wuhan, Peoples R China.;[Gao, Mingyun] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构：
[Gao, MY ] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
关键词：
digital economy evaluation;grey relational degree;fuzzy integral;grey information coverage;normal cloud matrix
摘要：
This study aims to reflect the grey information coverage and complex interactions effect in digital economy development. Therefore, a multi-attribute decision making method based on the grey interaction relational degree of the normal cloud matrix (GIRD-NCM) model is pro-posed. First, the original information coverage grey numbers are transformed into normal cloud matrixes, and then a novel Minkowski distance between normal clouds is proposed by using different information principles. Second, the GIRD-NCM model is established according to the Choquet fuzzy integral and grey relational degree. Finally, the dynamic comprehensive evaluation of digital economy development in China from 2013 to 2020 is conducted. The implementation, availability, and feasibility of the GIRD-NCM model are verified by comparative analysis with three existing evaluation models. The empirical findings reveal a stable growth trend in China's digital economy, with an annual growth rate of 7.87%, however, there are notable regional devel-opment disparities. The change in interaction degree has no effect on the rankings of provinces that are in the lead or have a moderately high level of digital economy development, but has a positive and negative impact on the rankings of these provinces with high and low levels of digital economy development, respectively.
