25,103 research outputs found
Fostering Public Good Contributions with Symbolic Awards: A Large-Scale Natural Field Experiment at Wikipedia
This natural field experiment tests the effects of purely symbolic awards on volunteer retention in a public goods context. The experiment is conducted at Wikipedia, which faces declining editor retention rates, particularly among newcomers. Randomization assures that award receipt is orthogonal to previous performance. The analysis reveals that awards have a sizeable effect on newcomer retention, which persists over the four quarters following the initial intervention. This is noteworthy for indicating that awards for volunteers can be effective even if they have no impact on the volunteers’ future career opportunities. The awards are purely symbolic, and the status increment they produce is limited to the recipients’ pseudonymous online identities in a community they have just recently joined. The results can be explained by enhanced self-identification with the community, but they are also in line with recent findings on the role of status and reputation, recognition, and evaluation potential in online communities. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2016.2540 . This paper was accepted by John List, behavioral economics
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Can Who-Edits-What Predict Edit Survival?
As the number of contributors to online peer-production systems grows, it
becomes increasingly important to predict whether the edits that users make
will eventually be beneficial to the project. Existing solutions either rely on
a user reputation system or consist of a highly specialized predictor that is
tailored to a specific peer-production system. In this work, we explore a
different point in the solution space that goes beyond user reputation but does
not involve any content-based feature of the edits. We view each edit as a game
between the editor and the component of the project. We posit that the
probability that an edit is accepted is a function of the editor's skill, of
the difficulty of editing the component and of a user-component interaction
term. Our model is broadly applicable, as it only requires observing data about
who makes an edit, what the edit affects and whether the edit survives or not.
We apply our model on Wikipedia and the Linux kernel, two examples of
large-scale peer-production systems, and we seek to understand whether it can
effectively predict edit survival: in both cases, we provide a positive answer.
Our approach significantly outperforms those based solely on user reputation
and bridges the gap with specialized predictors that use content-based
features. It is simple to implement, computationally inexpensive, and in
addition it enables us to discover interesting structure in the data.Comment: Accepted at KDD 201
Characterizing and Modeling the Dynamics of Activity and Popularity
Social media, regarded as two-layer networks consisting of users and items,
turn out to be the most important channels for access to massive information in
the era of Web 2.0. The dynamics of human activity and item popularity is a
crucial issue in social media networks. In this paper, by analyzing the growth
of user activity and item popularity in four empirical social media networks,
i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links
between users and items are more likely to be created by active users and to be
acquired by popular items, where user activity and item popularity are measured
by the number of cross links associated with users and items. This indicates
that users generally trace popular items, overall. However, it is found that
the inactive users more severely trace popular items than the active users.
Inspired by empirical analysis, we propose an evolving model for such networks,
in which the evolution is driven only by two-step random walk. Numerical
experiments verified that the model can qualitatively reproduce the
distributions of user activity and item popularity observed in empirical
networks. These results might shed light on the understandings of micro
dynamics of activity and popularity in social media networks.Comment: 13 pages, 6 figures, 2 table
Cross-language Wikipedia Editing of Okinawa, Japan
This article analyzes users who edit Wikipedia articles about Okinawa, Japan,
in English and Japanese. It finds these users are among the most active and
dedicated users in their primary languages, where they make many large,
high-quality edits. However, when these users edit in their non-primary
languages, they tend to make edits of a different type that are overall smaller
in size and more often restricted to the narrow set of articles that exist in
both languages. Design changes to motivate wider contributions from users in
their non-primary languages and to encourage multilingual users to transfer
more information across language divides are presented.Comment: In Proceedings of the SIGCHI Conference on Human Factors in Computing
Systems, CHI 2015. AC
Vandalism on Collaborative Web Communities: An Exploration of Editorial Behaviour in Wikipedia
Modern online discussion communities allow people to contribute, sometimes anonymously. Such flexibility sometimes threatens the reputation and reliability of community-owned resources. Such flexibility is understandable, however, they engender threats to the reputation and reliability in collective goods. Since not a lot of previous work addressed these issues it is important to study the aforementioned issues to build an innate understanding of recent ongoing vandalism of Wikipedia pages and ways to preventing those.
In this study, we consider the type of activity that the anonymous users carry out on Wikipedia and also contemplate how others react to their activities. In particular, we want to study vandalism of Wikipedia pages and ways of preventing this kind of activity. Our preliminary analysis reveals (~ 90%) of the vandalism or foul edits are done by unregistered users in Wikipedia due to nature of openness. The community reaction seemed to be immediate: most vandalisms were reverted within five minutes on an average. Further analysis shed light on the tolerance of Wikipedia community, reliability of anonymous users revisions and feasibility of early prediction of vandalism
The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics
Activity of modern scholarship creates online footprints galore. Along with
traditional metrics of research quality, such as citation counts, online images
of researchers and institutions increasingly matter in evaluating academic
impact, decisions about grant allocation, and promotion. We examined 400
biographical Wikipedia articles on academics from four scientific fields to
test if being featured in the world's largest online encyclopedia is correlated
with higher academic notability (assessed through citation counts). We found no
statistically significant correlation between Wikipedia articles metrics
(length, number of edits, number of incoming links from other articles, etc.)
and academic notability of the mentioned researchers. We also did not find any
evidence that the scientists with better WP representation are necessarily more
prominent in their fields. In addition, we inspected the Wikipedia coverage of
notable scientists sampled from Thomson Reuters list of "highly cited
researchers". In each of the examined fields, Wikipedia failed in covering
notable scholars properly. Both findings imply that Wikipedia might be
producing an inaccurate image of academics on the front end of science. By
shedding light on how public perception of academic progress is formed, this
study alerts that a subjective element might have been introduced into the
hitherto structured system of academic evaluation.Comment: To appear in EPJ Data Science. To have the Additional Files and
Datasets e-mail the corresponding autho
- …