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  • × author_ss:"Wu, Y."
  1. Du, J.; Tang, X.; Wu, Y.: ¬The effects of research level and article type on the differences between citation metrics and F1000 recommendations (2016) 0.02 
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    Abstract
    F1000 recommendations were assessed as a potential data source for research evaluation, but the reasons for differences between F1000 Article Factor (FFa scores) and citations remain unexplored. By linking recommendations for 28,254 publications in F1000 with citations in Scopus, we investigated the effect of research level (basic, clinical, mixed) and article type on the internal consistency of assessments based on citations and FFa scores. The research level has little impact on the differences between the 2 evaluation tools, while article type has a big effect. These 2 measures differ significantly for 2 groups: (a) nonprimary research or evidence-based research are more highly cited but not highly recommended, while (b) translational research or transformative research are more highly recommended but have fewer citations. This can be expected, since citation activity is usually practiced by academic authors while the potential for scientific revolutions and the suitability for clinical practice of an article should be investigated from a practitioners' perspective. We conclude with a recommendation that the application of bibliometric approaches in research evaluation should consider the proportion of 3 types of publications: evidence-based research, transformative research, and translational research. The latter 2 types are more suitable for assessment through peer review.
  2. Liu, J.; Wu, Y.; Zhou, L.: ¬A hybrid method for abstracting newspaper articles (1999) 0.01 
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  3. Li, J.; Zhang, P.; Song, D.; Wu, Y.: Understanding an enriched multidimensional user relevance model by analyzing query logs (2017) 0.01 
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  4. Wang, F.; Yang, J.; Wu, Y.: Non-synchronism in theoretical research of information science (2021) 0.01 
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  5. Xiao, C.; Zhou, F.; Wu, Y.: Predicting audience gender in online content-sharing social networks (2013) 0.00 
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    Abstract
    Understanding the behavior and characteristics of web users is valuable when improving information dissemination, designing recommendation systems, and so on. In this work, we explore various methods of predicting the ratio of male viewers to female viewers on YouTube. First, we propose and examine two hypotheses relating to audience consistency and topic consistency. The former means that videos made by the same authors tend to have similar male-to-female audience ratios, whereas the latter means that videos with similar topics tend to have similar audience gender ratios. To predict the audience gender ratio before video publication, two features based on these two hypotheses and other features are used in multiple linear regression (MLR) and support vector regression (SVR). We find that these two features are the key indicators of audience gender, whereas other features, such as gender of the user and duration of the video, have limited relationships. Second, another method is explored to predict the audience gender ratio. Specifically, we use the early comments collected after video publication to predict the ratio via simple linear regression (SLR). The experiments indicate that this model can achieve better performance by using a few early comments. We also observe that the correlation between the number of early comments (cost) and the predictive accuracy (gain) follows the law of diminishing marginal utility. We build the functions of these elements via curve fitting to find the appropriate number of early comments (approximately 250) that can achieve maximum gain at minimum cost.

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