Contributing to Wikipedia: Through Content or Social Interaction?

Proceedings of the 16th International Symposium on Open Collaboration

In any collaborative system, people do not contribute equally. This is particularly observed to be true for systems seeking to gather contributions from a large, diverse group of people. In such settings, it is seen that a sizable amount of contribution comes from a small group of highly-active users. While it is well-understood that such users are instrumental in the system's progress, the contribution made by a large group of less-active users is not sufficiently understood. Popularly called masses, these users comprise of the majority of the system's user base. It is, therefore, important to examine their worth in the system. The literature in this direction points towards two contradicting points of view with one acknowledging masses' contribution (Ortega Hypothesis) while the other deeming them unnecessary in the system (Newton Hypothesis). Given the large-scale collaboration facilitated by Wikipedia where a large crowd with a diverse skill-set and hence unequal contribution participates, a detailed investigation of the worth of masses becomes necessary for informed policy-making. In this work, we examine whether masses help or hamper the knowledge-building in Wikipedia. We specifically consider their contribution across different contribution types pertaining to the insertion of new content as well as the administrative activities. We observe that although the individual contribution by masses is small, yet they contribute important pieces of knowledge to Wikipedia articles. The results indicate that the overall contribution of masses across several parameters even exceeds the contribution by elites. We also find that as compared to masses, highly-active users dominate the edits where no new content is inserted and only activities involving the up-keeping of the existing content such as restructuring or formatting take place. The results of the study may help in devising appropriate incentivization policies for Wikipedia and the collaborative systems in general. CCS CONCEPTS • Human-centered computing → Empirical studies in collaborative and social computing; Collaborative and social computing design and evaluation methods.

2017

In this vision paper, we suggest combining two lines of research to study the collective behavior of Wikipedia contributors. The first line of research analyzes Wikipedia's edit history to quantify the quality of individual contributions and the resulting reputation of the contributor. The second line of research surveys Wikipedia contributors to gain insights, e.g., on their personal and professional background, socioeconomic status, or motives to contribute toWikipedia. While both lines of research are valuable on their own, we argue that the combination of both approaches could yield insights that exceed the sum of the individual parts. Linking survey data to contributor reputation and content-based quality metrics could provide a large-scale, public domain data set to perform user modeling, i.e. deducing interest profiles of user groups. User profiles can, among other applications, help to improve recommender systems. The resulting dataset can also enable a better understand...

Proceedings of the ACM 2009 …, 2009

Open content web sites depend on users to produce information of value. Wikipedia is the largest and most well-known such site. Previous work has shown that a small fraction of editors -Wikipedians -do most of the work and produce most of the value. Other work has offered conjectures about how Wikipedians differ from other editors and how Wikipedians change over time. We quantify and test these conjectures. Our key findings include: Wikipedians' edits last longer; Wikipedians invoke community norms more often to justify their edits; on many dimensions of activity, Wikipedians start intensely, tail off a little, then maintain a relatively high level of activity over the course of their career. Finally, we show that the amount of work done by Wikipedians and non-Wikipedians differs significantly from their very first day. Our results suggest a design opportunity: customizing the initial user experience to improve retention and channel new users' intense energy.

Scientific Reports

Wikipedia, a paradigmatic example of online knowledge space is organized in a collaborative, bottom-up way with voluntary contributions, yet it maintains a level of reliability comparable to that of traditional encyclopedias. The lack of selected professional writers and editors makes the judgement about quality and trustworthiness of the articles a real challenge. Here we show that a self-consistent metrics for the network defined by the edit records captures well the character of editors’ activity and the articles’ level of complexity. Using our metrics, one can better identify the human-labeled high-quality articles, e.g., “featured” ones, and differentiate them from the popular and controversial articles. Furthermore, the dynamics of the editor-article system is also well captured by the metrics, revealing the evolutionary pathways of articles and diverse roles of editors. We demonstrate that the collective effort of the editors indeed drives to the direction of article improvem...

2011

Abstract In this study we measure the impact of pre-existing social capital on the efficiency of collaboration among Wikipedia editors. To construct a social network among Wikipedians we look to mutual interaction on the user talk pages of Wikipedia editors. As our data set, we analyze the communication networks associated with 3085 featured articles-the articles of highest quality in the English Wikipedia, comparing it to the networks of 80154 articles of lower quality.

Ninth International AAAI Conference on Web and Social Media, 2015

Is it an encyclopedia or a social network? Without considering both aspects it would not be possible to understand how a worldwide army of editors created the largest online knowledge repository. Wikipedia has a consistent set of rules and it responds to many of the User Engagement Framework attributes, and this is why it works. In this paper , we identify these confirmed attributes as well as those presenting problems. We explain that although having a strong editor base Wikipedia is finding it challenging to maintain this base or increase its size. In order to understand this, scholars have analyzed Wikipedia using current metrics like user session and activity. We conclude there exist opportunities to analyze engagement in new aspects in order to understand its success, as well as to redesign mechanisms to improve the system and help the transition between reader and editor.

2009

Wikipedia, one of the top ten most visited websites, is commonly viewed as the largest online reference for encyclopedic knowledge. Because of its open editing model -allowing anyone to enter and edit content-Wikipedia's overall quality has often been questioned as a source of reliable information. Lack of study of the open editing model of Wikipedia and its effectiveness has resulted in a new generation of wikis that restrict contributions to registered users only, using their real names. In this paper, we present an empirical study of user contributions to Wikipedia. We statistically analyze contributions by both anonymous and registered users. The results show that submissions of anonymous and registered users in Wikipedia suggest a power law behavior. About 80% of the revisions are submitted by less than 7% of the users, most of whom are registered users. To further refine the analyzes, we use the Wiki Trust Model (WTM), a user reputation model developed in our previous work to assign a reputation value to each user. As expected, the results show that registered users contribute higher quality content and therefore are assigned higher reputation values. However, a significant number of anonymous users also contribute high-quality content. We provide further evidence that regardless of a user s' attribution, registered or anonymous, high reputation users are the dominant contributors that actively edit Wikipedia articles in order to remove vandalism or poor quality content.

The study of collaboration patterns in wikis can help shed light on the process of content creation by online communities. To turn a wiki's revision history into a collaboration network, we propose an algorithm that identifies as authors of a page the users who provided the most of its relevant content, measured in terms of quantity and of acceptance by the community. The scalability of this approach allows us to study the English Wikipedia community as a co-authorship network. We find evidence of the presence of a nucleus of very active contributors, who seem to spread over the whole wiki, and to interact preferentially with inexperienced users. The fundamental role played by this elite is witnessed by the growing centrality of sociometric stars in the network. Isolating the community active around a category, it is possible to study its specific dynamics and most influential authors.

Lecture Notes in Computer Science, 2016

Collaborations such as Wikipedia are a key part of the value of the modern Internet. At the same time there is concern that these collaborations are threatened by high levels of member turnover. In this paper we borrow ideas from topic analysis to editor activity on Wikipedia over time into a latent space that offers an insight into the evolving patterns of editor behavior. This latent space representation reveals a number of different categories of editor (e.g. content experts, social networkers) and we show that it does provide a signal that predicts an editor's departure from the community. We also show that long term editors gradually diversify their participation by shifting edit preference from one or two namespaces to multiple namespaces and experience relatively soft evolution in their editor profiles, while short term editors generally distribute their contribution randomly among the namespaces and experience considerably fluctuated evolution in their editor profiles.