Haiping Zhu

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Haiping Zhu is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Effectiveness of shared leadership in Wikipedia Aversive leadership
Directive leadership
Feedback
Online community
Person-based leadership
Shared leadership
Transactional leadership
Wikipedia
Human Factors English 2013 Objective: The objective of the paper is to understand leadership in an online community, specifically, Wikipedia. Background: Wikipedia successfully aggregates millions of volunteers' efforts to create the largest encyclopedia in human history. Without formal employment contracts and monetary incentives, one significant question for Wikipedia is how it organizes individual members with differing goals, experience, and commitment to achieve a collective outcome. Rather than focusing on the role of the small set of people occupying a core leadership position, we propose a shared leadership model to explain the leadership in Wikipedia. Members mutually influence one another by exercising leadership behaviors, including rewarding, regulating, directing, and socializing one another. Method: We conducted a two-phase study to investigate how distinct types of leadership behaviors (transactional, aversive, directive, and person-focused), the legitimacy of the people who deliver the leadership, and the experience of the people who receive the leadership influence the effectiveness of shared leadership in Wikipedia. Results: Our results highlight the importance of shared leadership in Wikipedia and identify trade-offs in the effectiveness of different types of leadership behaviors. Aversive and directive leadership increased contribution to the focal task, whereas transactional and person-focused leadership increased general motivation. We also found important differences in how newcomers and experienced members responded to leadership behaviors from peers. Application: These findings extend shared leadership theories, contribute new insight into the important underlying mechanisms in Wikipedia, and have implications for practitioners who wish to design more effective and successful online communities. Copyright 0 0
Effects of peer feedback on contribution: A field experiment in Wikipedia Field experiment
Online community
Peer feedback
Wikipedia
Conference on Human Factors in Computing Systems - Proceedings English 2013 One of the most significant challenges for many online communities is increasing members' contributions over time. Prior studies on peer feedback in online communities have suggested its impact on contribution, but have been limited by their correlational nature. In this paper, we conducted a field experiment on Wikipedia to test the effects of different feedback types (positive feedback, negative feedback, directive feedback, and social feedback) on members' contribution. Our results characterize the effects of different feedback types, and suggest trade-offs in the effects of feedback between the focal task and general motivation, as well as differences in how newcomers and experienced editors respond to peer feedback. This research provides insights into the mechanisms underlying peer feedback in online communities and practical guidance to design more effective peer feedback systems. Copyright 0 0
Coordination and beyond: Social functions of groups in open content production Groupwork
Open content
Peer production
Wikipedia
Wikiprojects
English 2012 We report on a study of the English edition of Wikipedia in which we used a mixed methods approach to understand how nested organizational structures called WikiProjects support collaboration. We first conducted two rounds of interviews with a total of 20 Wikipedians to understand how WikiProjects function and what it's like to participate in them from the perspective of Wikipedia editors. We then used a quantitative approach to further explore interpretations that arose from the qualitative data. Our analysis of these data together demonstrates how WikiProjects not only help Wikipedians coordinate tasks and produce articles, but also support community members and small groups of editors in important ways such as: providing a place to find collaborators, socialize and network; protecting editors' work; and structuring opportunities to contribute. 0 0
Effectiveness of shared leadership in online communities Motivation
Online community
Shared leadership
Wikipedia
English 2012 Traditional research on leadership in online communities has consistently focused on the small set of people occupying leadership roles. In this paper, we use a model of shared leadership, which posits that leadership behaviors come from members at all levels, not simply from people in high-level leadership positions. Although every member can exhibit some leadership behavior, different types of leadership behavior performed by different types of leaders may not be equally effective. This paper investigates how distinct types of leadership behaviors (transactional, aversive, directive and person-focused) and the legitimacy of the people who deliver them (people in formal leadership positions or not) influence the contributions that other participants make in the context of Wikipedia. After using propensity score matching to control for potential pre-existing differences among those who were and were not targets of leadership behaviors, we found that 1) leadership behaviors performed by members at all levels significantly influenced other members' motivation; 2) transactional leadership and person-focused leadership were effective in motivating others to contribute more, whereas aversive leadership decreased other contributors' motivations; and 3) legitimate leaders were in general more influential than regular peer leaders. We discuss the theoretical and practical implication of our work. 0 1
Organizing without formal organization: Group identification, goal setting and social modeling in directing online production Directing behaviors
Governance mechanisms
Group goals
Group identification
Online production communities
English 2012 A challenge for many online production communities is to direct their members to accomplish tasks that are important to the group, even when these tasks may not match individual members' interests. Here we investigate how combining group identification and direction setting can motivate volunteers in online communities to accomplish tasks important to the success of the group as a whole. We hypothesize that group identity, the perception of belonging to a group, triggers in-group favoritism; and direction setting (including explicit direction from group goals and implicit direction from role models) focuses people's group-oriented motivation towards the group's important tasks. We tested our hypotheses in the context of Wikipedia's Collaborations of the Week (COTW), a group goal setting mechanism and a social event within Wikiprojects. Results demonstrate that 1) publicizing important group goals via COTW can have a strong motivating influence on editors who have voluntarily identified themselves as group members compared to those who have not self-identified; 2) the effects of goals spill over to non-goal related tasks; and 3) editors exposed to group role models in COTW are more likely to perform similarly to the models on group-relevant citizenship behaviors. Finally, we discuss design and managerial implications based on our findings. 0 0
Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring The Semantic Web English 2008 Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source. 0 0
EachWiki: Suggest to be an easy-to-edit wiki interface for everyone CEUR Workshop Proceedings English 2007 In this paper, we present EachWiki, an extension of Semantic MediaWiki characterized by an intelligent suggestion mechanism. It aims to facilitate the wiki authoring by recommending the following elements: links, categories, and properties. We exploit the semantics of Wikipedia data and leverage the collective wisdom of web users to provide high quality annotation suggestions. The proposed mechanism not only improves the usability of Semantic MediaWiki but also speeds up its converging use of terminology. The suggestions are applied to relieve the burden of wiki authoring and attract more inexperienced contributors, thus making Semantic MediaWiki even better Semantic Web proto types and data source. 0 0
Exploit Semantic Information for Category Annotation Recommendation in Wikipedia Natural Language Processing and Information Systems English 2007 Compared with plain-text resources, the ones in “semi-semantic” web sites, such as Wikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a “collaborative annotating” approach to automatically recommend categories for a Wikipedia article by reusing category annotations from its most similar articles and ranking these annotations by their confidence. In this approach, four typical semantic features in Wikipedia, namely incoming link, outgoing link, section heading and template item, are investigated and exploited as the representation of articles to feed the similarity calculation. The experiment results have not only proven that these semantic features improve the performance of category annotating, with comparison to the plain text feature; but also demonstrated the strength of our approach in discovering missing annotations and proper level ones for Wikipedia articles. 0 0
Exploit semantic information for category annotation recommendation in Wikipedia Collaborative annotating
Semantic features
Vector space model
Wikipedia category
Lecture Notes in Computer Science English 2007 Compared with plain-text resources, the ones in "semi-semantic" web sites, such as Wikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a "collaborative annotating" approach to automatically recommend categories for a Wikipedia article by reusing category annotations from its most similar articles and ranking these annotations by their confidence. In this approach, four typical semantic features in Wikipedia, namely incoming link, outgoing link, section heading and template item, are investigated and exploited as the representation of articles to feed the similarity calculation. The experiment results have not only proven that these semantic features improve the performance of category annotating, with comparison to the plain text feature; but also demonstrated the strength of our approach in discovering missing annotations and proper level ones for Wikipedia articles. 0 0
PORE: Positive-Only Relation Extraction from Wikipedia Text The Semantic Web English 2007 Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identifi cation, and transductive inference to work with fewer positive training exam ples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly out per forms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wiki pedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains. 0 0
PORE: Positive-only relation extraction from wikipedia text Ontology population
Positive-only learning
Relation extraction
Lecture Notes in Computer Science English 2007 Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identifi cation, and transductive inference to work with fewer positive training exam ples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly out per forms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wiki pedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains. 0 0