Search (4 results, page 1 of 1)

  • × author_ss:"Zhang, Y."
  • × language_ss:"e"
  • × year_i:[2020 TO 2030}
  1. 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) 0.02 
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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.
  2. Zhang, Y.; Ren, P.; Rijke, M. de: ¬A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses (2021) 0.01 
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    Abstract
    Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state-of-the-art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.
  3. Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.01 
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    Date
    22. 6.2023 18:18:34
  4. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.01 
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    Date
    22. 6.2023 18:07:12

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