科研成果

Forthcoming
李文琦 and 张鹏翼, “数据行为的概念界定与模型构建,” 图书情报工作, Forthcoming.PKU 
2022
P. Zhang, “Collaborative sensemaking: The starting point of intelligent information use by teams and groups,” Information Matters, vol. 2, no. 10, 2022. 访问链接
李珂 and 张鹏翼, “先验态度对选择性信息行为与认知机制的影响探究,” 图书情报工作, vol. 66, no. 20, 2022.PKU 
梁昌豪 and 张鹏翼, “学习材料知识表示形式对数字阅读效果的影响研究,” 图书情报工作, vol. 66, no. 8, 2022.PKU 
张鹏翼, 王丹雪, and 唐震怡, “在线知识协作行为研究:团队成员亲密度及协作倾向的影响,” 图书情报工作, vol. 66, no. 8, 2022.PKU 
林蕙, 李世娟, and 张鹏翼, “社交平台中癌症患者及家属健康信息自我表露研究,” 情报科学, vol. 40, no. 3, 2022.PKU 
K. Li, Y. Li, and P. Zhang, “Selective exposure to COVID-19 vaccination information: the influence of prior attitude, perceived threat level, and information limit,” Library Hi Tech, vol. 40, 2022. 访问链接
2021
Y. Zhang, J. Tang, and P. Zhang, “An Exploratory Study on Chinese Preteens' Internet Use and Parental Mediation during the COVID-19 Pandemic,” Poster presented at ASIST ’21. 2021.
钱志超, 姜雪, 苏洋, 张鹏翼, and 韩圣龙, “信息分层理论视角下的技术帮助影响因素探析,” 文献与数据学报, vol. 3, no. 03, pp. 36-52, 2021.Abstract
[目的 /意义]探究技术帮助的属性及其影响因素,从而加深对数字鸿沟的变化过程的理解。技术帮助指人们在使用不熟悉的信息与通信技术遇到困难时获取或提供帮助的行为。[方法 /过程]在技术帮助研究中引入信息分层理论,对18名大学生和大学后勤工作人员进行深度访谈,考察信息富裕者和信息中层两个群体在技术帮助方面的特征。在数据分析阶段,采用扎根理论研究方法,通过开放性编码、主轴编码和选择编码,探讨影响技术帮助的发生和完成的因素,以及不同的影响因素间的关系。[结果 /结论]技术帮助受到认知因素、ICT使用水平、可接触性、人口学因素和社会因素这五类因素的共同影响。其中,认知因素、ICT使用水平、可接触性是技术帮助的直接影响因素,人口学因素和社会因素是间接影响因素。
闫慧, 刘畅, and 张鹏翼等, “信息疫情: 信息科学家的观点与对策,” 图书情报知识, vol. 38, no. 1, pp. 136-143, 2021.PKU 
O. Marzouk, J. Salminen, P. Zhang, and B. J. Jansen, “Which Message? Which Channel? Which Customer?-Exploring Response Rates in Multi-Channel Marketing Using Short-Form Advertising,” Data and Information Management, 2021.
S. Li, Q. Jiang, and P. Zhang, “Factors Influencing the Health Behavior During Public Health Emergency: A Case Study on Norovirus Outbreak in a University,” Data and Information Management, vol. 5, pp. 27 - 39, 2021. 访问链接
2020
D. Soergel and P. Zhang, “Design of a sensemaking assistant to support learning,” in The Future of Education, 2020.Abstract
Thinking tools that assist by externalizing thought processes and conceptual structures so they can be manipulated potentially improve user learning. We propose the design of a sensemaking assistant that integrates many such tools. Our design emerged from an intensive study of sensemaking by users working on real tasks, providing a link from users to developers. Sensemaking is the process of forming meaningful representations and working with them to gain understanding, possibly communicated in a report, to support planning, decision‑making, problem‑solving, and informed action. At the heart of our design is a set of tightly integrated tools for representing and manipulating a conceptual space: tools for producing and maintaining concept maps, causal maps/influence diagrams, argument maps, with support through self-organizing semantic maps, importing concepts and relationships from external Knowledge Organization Systems, and inferring connections between texts; further a tool for organizing information items (documents, text passages notes, images) linked to the concept map. The sensemaking assistant we envision guides users through the sensemaking process; for each function it suggests appropriate cognitive processes and provides tools that automate tasks. The comprehensive sensemaking model introduced in specifies functions in the iterative process of sensemaking: Task analysis and planning; Gap identification (tools for both: brainstorming, finding documents on the task); information acquisition, data seeking and structure seeking (search tool: finding databases, query expansion, passage retrieval; summarization tool); information organization, building structure, instantiating structure, information synthesis / new ideas / emerging sense (conceptual space tools mentioned above); information presentation, creating reports (from concept map to outline, guide through the writing process, analyze draft writing for coherence and clarity). The system tracks sources. Users using a sensemaking assistant may well internalize good ways for intellectual processes and good conceptual organization in addition to learning a useful application. The paper will provide some evidence from the literature and propose further testing.
H. Lin and P. Zhang, “Comparing Topics of Scholars’ Blog Posts in an Academic Social Networking Site and Publication Keywords,” in Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, Virtual Event, China, 2020, pp. 499–500. 访问链接
J. Liu, J. An, and P. Zhang, “Analyzing opinion conflicts in an online group discussion: From the perspective of majority and minority influence,” iConference 2020. iSchools, 2020. 访问链接Abstract
Online community and groups often experience heated discussion. This paper examines a WeChat group discussion from the perspective of majority and minority influence to explore the evolvement of the discussion and the be-haviors of group members. Content analysis of 515 messages suggests that opin- ion conflicts between majority and minority evoke discussion engagement and knowledge exchange. There are different patterns of knowledge construction expressions between majority and minority groups. The majority prefer egocentric expression, while the minority prefer allocentric expression. Majority opinion holders have different conflict handling styles compared to minority opinion holders, who are more likely to avoid. Minority group is under great pressure in social interaction, they are easier to receive unfair comments and personal attacks.
X. Chen, Y. Yang, and P. Zhang, “Examining scholars' activity on a Chinese blogging and academic social network site,” iConference 2020. iSchools, 2020.
J. Liu and P. Zhang, “How to Initiate a Discussion Thread?: Exploring Factors Influencing Engagement Level of Online Deliberation”. Springer International Publishing, pp. 220-226, 2020.Abstract
Online platforms provide a public sphere for discussion, debate, and deliberation among citizens. The engagement of online deliberation enables participants to exchange viewpoints and form communities. This paper aims to explore the influencing factors on engagement level of online deliberation by examining the relationship between an initial post’s content features and length and the engagement of the discussion thread it initiates. We sampled 254 discussion threads with 254 initial posts and 2934 following posts and conducted quantitative and qualitative analysis of the posts. Findings show that initial posts which are longer and allocentric (as opposed to egocentric) would evoke longer following posts in a discussion. Different content type (social interaction, claim, argument) of initial posts would lead to significant different engagement, arguments would trigger higher level engagement (average posts per participant and average length of posts in discussions). Whether an initial post holds a clear position has no significant impact on discussion engagement. These findings contribute to a deeper understanding of online deliberation and its engagement and can be useful in promoting engagements in online deliberation.
W. Huang, J. Liu, H. Bai, and P. Zhang, “Value assessment of companies by using an enterprise value assessment system based on their public transfer specification,” Information Processing and Management, vol. 57, no. 5, 2020.
张璐, 张鹏翼, and 刘畅, “协同搜索过程中用户交流内容与模式研究,” 图书情报知识, no. 03, pp. 51-62, 2020.AbstractPKU 
[目的/意义]旨在分析协同搜索用户在信息搜索任务过程中的交流内容与模式,从而理解协同搜索用户的关注重点与搜索过程。[研究设计/方法]基于书籍交互检索平台(CLEF-Social Book Search)设计实验,共招募18名被试完成两种搜索任务,通过录音记录对话并对其进行编码和分析,总结交流内容特征和模式。结合任务类型、认知类型组合、服务器记录的搜索交互行为日志以及问卷收集的搜索体验进行了探索分析。[结论/发现]从交流内容上看,协同搜索用户主要理解与评判书目信息、商讨搜索任务计划;比起认知类型不同的用户,相同认知类型的用户在操作交互方面交流更多,在评判决策方面交流较少。交流模式依据讨论内容比重可分为理解评判型、评判主导型、均衡交流型三种,评判主导型用户的任务完成满意度最高。[创新/价值]协同搜索用户的交流反映出搜索过程中需要与同伴商讨协同的焦点,也是需要系统提供协助的重点,给协同搜索系统设计提供一定参考。本研究针对协同搜索的交流内容设计的编码系统对相关的协同交流研究也有借鉴意义。
K. Marzullo, 张鹏翼, 德德玛, 刘洁丽, and 安佳鑫, “当计算机科学遇到信息科学——马里兰大学信息学院院长Keith Marzullo教授学术访谈,” 图书情报知识, no. 03, pp. 4-10, 2020.AbstractPKU 
<正>1社会技术视角:CS与IS的联合张鹏翼:Marzullo教授,您好!感谢您抽时间接受我的访谈。据我了解,您的学术背景是计算机科学,现在作为马里兰大学iSchool的院长,您如何看待计算机科学(CS)与信息科学(IS)二者之间的关系?计算机科学对信息科学的影响更大吗?信息科学对计算机科学是否产生了什么影响呢?Prof.Marzullo:诚然,计算机科学院系的数量确实远多于iSchools院系的数量。不过,如果我们关注非常有影响力的团队,例如佐治亚理工学院、康奈尔

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