3,626 research outputs found
Bots in Wikipedia: Unfolding their duties
The success of crowdsourcing systems such as Wikipedia relies on people participating in these systems. However, in this research we reveal to what extent human and machine intelligence is combined to carry out semi-automatic workflows of complex tasks. In Wikipedia, bots are used to realize such combination of human-machine intelligence. We provide an extensive overview on various edit types bots carry out in this regard through the analysis of 1,639 approved task requests. We classify existing tasks by an action-object-pair structure and reveal existing differences in their probability of occurrence depending on the investigated work context. In the context of community services, bots mainly create reports, whereas in the area of guidelines or policies bots are mostly responsible for adding templates to pages. Moreover, the analysis of existing bot tasks revealed insights that suggest general reasons, why Wikipedia’s editor community uses bots as well as approaches, how they organize machine tasks to provide a sustainable service. We conclude by discussing how these insights can prepare the foundation for further research
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
Scholars and practitioners across domains are increasingly concerned with
algorithmic transparency and opacity, interrogating the values and assumptions
embedded in automated, black-boxed systems, particularly in user-generated
content platforms. I report from an ethnography of infrastructure in Wikipedia
to discuss an often understudied aspect of this topic: the local, contextual,
learned expertise involved in participating in a highly automated
social-technical environment. Today, the organizational culture of Wikipedia is
deeply intertwined with various data-driven algorithmic systems, which
Wikipedians rely on to help manage and govern the "anyone can edit"
encyclopedia at a massive scale. These bots, scripts, tools, plugins, and
dashboards make Wikipedia more efficient for those who know how to work with
them, but like all organizational culture, newcomers must learn them if they
want to fully participate. I illustrate how cultural and organizational
expertise is enacted around algorithmic agents by discussing two
autoethnographic vignettes, which relate my personal experience as a veteran in
Wikipedia. I present thick descriptions of how governance and gatekeeping
practices are articulated through and in alignment with these automated
infrastructures. Over the past 15 years, Wikipedian veterans and administrators
have made specific decisions to support administrative and editorial workflows
with automation in particular ways and not others. I use these cases of
Wikipedia's bot-supported bureaucracy to discuss several issues in the fields
of critical algorithms studies, critical data studies, and fairness,
accountability, and transparency in machine learning -- most principally
arguing that scholarship and practice must go beyond trying to "open up the
black box" of such systems and also examine sociocultural processes like
newcomer socialization.Comment: 14 pages, typo fixed in v
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor
User-generated content sites routinely block contributions from users of
privacy-enhancing proxies like Tor because of a perception that proxies are a
source of vandalism, spam, and abuse. Although these blocks might be effective,
collateral damage in the form of unrealized valuable contributions from
anonymity seekers is invisible. One of the largest and most important
user-generated content sites, Wikipedia, has attempted to block contributions
from Tor users since as early as 2005. We demonstrate that these blocks have
been imperfect and that thousands of attempts to edit on Wikipedia through Tor
have been successful. We draw upon several data sources and analytical
techniques to measure and describe the history of Tor editing on Wikipedia over
time and to compare contributions from Tor users to those from other groups of
Wikipedia users. Our analysis suggests that although Tor users who slip through
Wikipedia's ban contribute content that is more likely to be reverted and to
revert others, their contributions are otherwise similar in quality to those
from other unregistered participants and to the initial contributions of
registered users.Comment: To appear in the IEEE Symposium on Security & Privacy, May 202
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
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
Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year direct
imaging survey of 600 stars to discover and characterize young Jovian
exoplanets and their environments. We have developed an automated data
architecture to process and index all data related to the survey uniformly. An
automated and flexible data processing framework, which we term the Data
Cruncher, combines multiple data reduction pipelines together to process all
spectroscopic, polarimetric, and calibration data taken with GPIES. With no
human intervention, fully reduced and calibrated data products are available
less than an hour after the data are taken to expedite follow-up on potential
objects of interest. The Data Cruncher can run on a supercomputer to reprocess
all GPIES data in a single day as improvements are made to our data reduction
pipelines. A backend MySQL database indexes all files, which are synced to the
cloud, and a front-end web server allows for easy browsing of all files
associated with GPIES. To help observers, quicklook displays show reduced data
as they are processed in real-time, and chatbots on Slack post observing
information as well as reduced data products. Together, the GPIES automated
data processing architecture reduces our workload, provides real-time data
reduction, optimizes our observing strategy, and maintains a homogeneously
reduced dataset to study planet occurrence and instrument performance.Comment: 21 pages, 3 figures, accepted in JATI
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