-
Ding, Y.: Topic-based PageRank on author cocitation networks (2011)
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- Abstract
- Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.449-466
-
Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011)
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- Abstract
- Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.467-477
-
Zhang, G.; Ding, Y.; Milojevic, S.: Citation content analysis (CCA) : a framework for syntactic and semantic analysis of citation content (2013)
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- Abstract
- This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1490-1503
-
Yan, E.; Ding, Y.: Discovering author impact : a PageRank perspective (2011)
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- Source
- Information processing and management. 47(2011) no.1, S.125-134
-
Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015)
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- Abstract
- Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2657-2673
-
Li, D.; Wang, Y.; Madden, A.; Ding, Y.; Sun, G.G.; Zhang, N.; Zhou, E.: Analyzing stock market trends using social media user moods and social influence (2019)
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- Abstract
- Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1000-1013
-
Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022)
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- Abstract
- Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1489-1505
-
Ding, Y.; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1313-1326
-
Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.2, S.352-371
-
Hu, B.; Dong, X.; Zhang, C.; Bowman, T.D.; Ding, Y.; Milojevic, S.; Ni, C.; Yan, E.; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2643-2656
-
Zhang, C.; Bu, Y.; Ding, Y.; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.72-86
-
Zhai, Y; Ding, Y.; Wang, F.: Measuring the diffusion of an innovation : a citation analysis (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.3, S.368-379
-
Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.5, S.462-475
-
Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019)
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- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1098-1107
-
Zhai, Y.; Ding, Y.; Zhang, H.: Innovation adoption : broadcasting versus virality (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.4, S.403-416
-
Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866
-
Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012)
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- Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162
-
Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014)
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- Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1007-1017
-
Tan, L.K.-W.; Na, J.-C.; Ding, Y.: Influence diffusion detection using the influence style (INFUSE) model (2015)
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- Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1717-1733
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Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.87-97
