02/19/2020 – Palakh Mignonne Jude – The Work of Sustaining Order in Wikipedia: The Banning of a Vandal

SUMMARY

In this paper, the authors focus on the efforts (both human and non-human) taken in order to moderate content on the English-language Wikipedia. The authors use trace ethnography in order to indicate how these ‘non-human’ technologies have transformed the way editing and moderation is performed on Wikipedia. These tools not only increase the speed and efficiency of the moderators, but also aide them in identifying changes that may have gone unnoticed by moderators – for example, the use of the ‘diff’ feature to identify edits made by a user enables the ‘vandal fighters’ to easily view malicious changes that may have been made to Wikipedia pages. The authors mention editing tools such as Huggle, Twinkle as well as a bot called the ClueBot that can examine edits and revert them based on a set of criteria such as obscenity, patent nonsense as well as mass removal of content by a user.  This synergy between the tools and humans has helped monitor changes to Wikipedia in near real-time and has lowered the level of expertise required by reviewers as an average volunteer with little to no knowledge of a domain is capable of performing these moderation tasks with the help of the aforementioned tools.

REFLECTION

I think it is interesting that the authors focus on the social effect on the activities done in Wikipedia due to various bots and assisted editing tools. I especially liked the analogy drawn from the work of Ed Hutchins of a navigator that is able to know the various trajectories through the work of a dozen crew members which the authors mention to be similar to blocking a vandal on Wikipedia through the combined effort of a complex network of interactions between software systems as well as human reviewers.

I thought it was interesting that the use of bots in edits increased from 2-4% in 2006 to about 16.33% in just about 4 years and this made me wonder what the current percentage of edits made by bots would be. The paper also mentions that the detection algorithms often discriminate against anonymous and newly registered users which is why I found it interesting to learn that users were allowed to reconfigure their queues such that they did not view anonymous edits as more suspicious. The paper mentions ClueBot that is capable to automatically reverting edits that contain obscene content, which made me wonder if efforts were made to develop bots that would be able to automatically revert edits that may contain hate speech and highly bigoted views.

QUESTIONS

1. As indicated in the paper ‘Updates in Human-AI teams’, humans tend to form mental models when it comes to trusting machine recommendations. Considering that the editing tools in this paper are responsible for queuing the edits made as well as accurately keeping track of the number of warnings given to a user, do changes in the rules used by these tools affect human-machine team performance?
2. Would restricting edits on Wikipedia to only users that are required to have non-anonymous login credentials (if not to the general public, non-anonymous to the moderators such as the implementation on Piazza wherein the professor can always view the true identity of the person posting the question) help lower the number of cases of vandalism?
3. The study performed by this paper is now about 10 years old. What are the latest tools that are used by Wikipedia reviewers? How do they differ from the ones mentioned in this paper? Are more sophisticated detection methods employed by these newer tools? And which is the most popularly used assisted editing tool?