作者：
Zhang, Min;Zhang, Dongxin;Zhang, Yin;Yeager, Kristin;Fields, Taylor N.
期刊：
Journal of Informetrics,2023年17(4) ISSN：1751-1577
通讯作者：
Zhang, Y
作者机构：
[Zhang, Min] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Zhang, Dongxin] Southwest Univ, Sch Comp & Informat Sci, Chongqing, Peoples R China.;[Zhang, Yin; Fields, Taylor N.; Zhang, Y] Kent State Univ, Sch Informat, Kent, OH 44242 USA.;[Yeager, Kristin] Kent State Univ, Univ Lib, Kent, OH USA.
通讯机构：
[Zhang, Y ] K;Kent State Univ, Sch Informat, Kent, OH 44242 USA.
摘要：
Influence plays a critical role in information communication, and the ubiquitous use of social media has made measuring influence on social media platforms a salient challenge. While previous studies have attempted to measure and investigate influence on Twitter, there is no consensus on its definition or relation to fundamental Twitter metrics. This study examined relationships between a composite influence measure of Twitter and fundamental social media metrics using a sample of tweets from a multi-year public campaign. Correlation analyses indicated that a user's number of followers had the strongest correlation with the composite measure. Principal components analysis was conducted for dimension reduction, and multiple regression analysis was performed using the resulting components. The findings revealed that a user's network was the most important predictor of the composite influence measure and that there was a negative relationship between campaign related activity and the composite measure. Implications of these findings are discussed. Overall, this study contributes to the understanding of and future efforts in the measurement of influence on social media.
作者：
Ye, Guanghui;Wang, Cancan;Wu, Chuan;Peng, Ze;Wei, Jinyu;...
期刊：
Journal of Informetrics,2023年17(3):101421 ISSN：1751-1577
通讯作者：
Wu, C
作者机构：
[Wu, Chuan; Peng, Ze; Wu, C; Tan, Qitao; Wu, Lanqi; Ye, Guanghui; Wei, Jinyu; Song, Xiaoying; Wang, Cancan] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构：
[Wu, C ] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
关键词：
Research front detection;Research grant information;Evolution analysis;Health informatics
摘要：
Identifying research fronts is an essential aspect of promoting scientific development. Many re-searchers choose their research directions and topics by analyzing their field's current research fronts. Many previous researchers have used academic papers or patents to identify research fronts; however, this is potentially outdated and reduces the prospective value of the research front detection. Considering this, this work proposes adapted indicators to conduct research front topic detection based on research grant data, which aims to identify research front topics and fore-cast trends using path analysis. First, research topics were identified using topic modeling, and then the mapping relations from topics to both fund projects and cross-domain categories were built. Then, research front topics were detected by multi-dimensional measurements, and the evo-lution of research topics was analyzed using topic evolution visualization to predict development trends. Finally, the Brillouin index was used to measure the cross-domain degree. Our method was evaluated using a dataset from the field of health informatics and was shown to be effective in research front identification. We found that the proposed adapted indicators were informative in identifying the evolutional trends in the health informatics field. In addition, research grants with higher cross-domain degrees are more likely to receive a high amount of funding.
摘要：
BACKGROUND: The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. OBJECTIVE: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. METHODS: Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. RESULTS: A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. CONCLUSIONS: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.
作者机构：
[Liang, Han; Chen, Jincai] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan, Peoples R China.;[Chen, Jincai; Lu, Ping] Huazhong Univ Sci & Technol, Inst Nat & Math Sci, Wuhan, Peoples R China.;[Wang, Ruili; Liang, Han] Massey Univ, Inst Nat & Math Sci, Auckland, New Zealand.;[Zeng, Jiangfeng] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Zeng, Jiangfeng] Ctr Data Governance & Intelligent Decis Making Hub, Wuhan, Peoples R China.
通讯机构：
[Zeng, JF ] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;Ctr Data Governance & Intelligent Decis Making Hub, Wuhan, Peoples R China.
关键词：
Audio-visual event localization;Dynamic fusion;Attention mechanism;Difference loss
