Summary

In this paper, the authors study the social roles of editing tools in Wikipedia and the way vandalism fighting is addressed. The authors focus on the effected automated tools, like robots, and assisted editing tools on the distributed editing used by the encyclopedia. Wikipedia allows anyone in the universe to edit the content of its articles, which make keeping the quality of the content a difficult task. The platform depends on distributed social network of volunteers to approve or deny changes. Wikipedia uses a source control system to help the users see the changes. The source control shows both versions of the edited content side by side which allow the editor to see the change history. The authors mention that Wikipedia uses bots and automated scripts to help editing some content and fight vandalism. They also mentioned different tools used by the platform to assist the editing process. A combination of humans, automated tasks, and assisted edit tools make Wikipedia able to handle such massive number of edits and fight vandalism attempts. Most research papers that studied the editing process are outdated since they didn’t pay a close attention to these tools, while the authors highlights the importance of these tools on improving the overall quality of the content and allow more edits to be performed. These technological tools like bots and assisted editing tools changed the way humans interact with system and have a significant social effect on the types of activities that are made possible in Wikipedia.

Reflection

I found the idea of the distributed editing and vandalism fighting in Wikipedia interesting. Giving the massive amount of contents in Wikipedia, it is very challenging to keep high quality contents giving that anyone in the universe who has access to the internet can make edit. The internal source control and the assisted tools used to help the editing job at a scale are amazing.

I also found the usage of the bots to automate the edit for some content interesting. These automated scripts can help expediting the content refresh in Wikipedia, but also cause errors. Some tools mentioned in the paper don’t even show the bots changes, so I am not sure if there some method that can measure the accuracy f these bots.

The concept of distributed editing is similar to the concept of pull request in GitHub where any one can submit a change to an open source project and only group of system owners or administrator can accept or reject the changes.

Questions

• Since millions or billions of people have smart phones nowadays, the amount of anonymous edit might significantly increase.  Are these tools still efficient in handling such increased volume of edits?
• Can we use deep learning or machine learning in fighting vandalism or spams? The number of edits performed on articles can be treated as a rich training dataset.
• Why don’t Wikipedia combine all the assisted editing tools in to one too that has the best of each tool? Do you think this a good idea or more tools means more innovation and more choices?

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Given an extensive website such as Wikipedia, there is bound to be an abundance of actors, both good and bad. With the scalability and wide ruleset of the popular web forum site, it would be nigh impossible for human moderators to handle the workload and cross examine each page in depth. To alleviate this, programs that use machine learning were created to help cross track user’s usage of the site into a single repository. Once all the information is gathered here, if a user is acting in a malicious way, it can easily be caught by the system and auto-reverted based on the machine learnings predictions. Such was the case for the user from the case study, whom attempted to slander a famous musician, but was caught quickly and with ease.

I absolutely agree with all the moderation going on around Wikipedia. Given the site domain, there are a vast number of pages that must be secured and protected (all to the same level). It is unrealistic to expect a non-profit website to be able to hire more manual workers to accomplish this same task (in contrast to Youtube, or Facebook). Also, the context in which must be followed in order to fully track a malicious user down manually would be completely exhaustive. For the security side of malware tracking, there is a vast amount of decompilers, raw binary program tracers, and even a custom Virtual Machine and Operation System (Security Onion) that contains various amounts of programs “out of the box” that are ready to track the full environment for the malware.

I disagree with one of the major issues that arises, regarding the bots creating and executing their own moral agenda. This is completely learned and based on the various factors (such as the rules, the training data, and correction values). Though they have the power to automatically revert and edit someone else’s page, these are done at the discretion of the person who created the rules. It would likely have some issues, but it is the overall learning process. These false positives would also be able to be appealed if the author so chooses to follow through, so it’s not a fully final decision.

• I would believe with such a tool suite, there would be a tool that would act as a combination, a “Visual Studio Code” like interface for all these tools. Having all these tools at the ready is useful, however since time is of the essence some tool wrapping all the common functions would be very convenient.
• I would like to get several how many reviews from moderators are completely biased. Having a moderator work force should ideally be unbiased however realistically it is unlikely to fully happen.
• I would also like to see the percentage of false positives, even in this robust of a system. Likely with new moderators they are likely to flag or unflag something if they are unfamiliar with the rules.

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Word count: 565

Summary of the Reading

This paper analyzes the use of autonomous technologies that are used on Wikipedia. These technologies help to keep the peace on the large platform, helping to flag malicious users and revert inaccurate and spammy changes so that Wikipedia stays accurate and up to date. Many people may think that humans play the major role in policing the platform, but machines and algorithms also play a very large part, aiding the humans to deal with the large amount of edits.

Some tools are completely automated and can prevent vandalism with no human input. Other tools give human contributors tips to help them spot and fight vandalism. Humans work together with the automated systems and each other to edit the site and keep the pages vandal free. The way in which all of the editors edit together, even though they are not physically together or connected as a team, is an impressive feat of human and AI interaction.

Reflections and Connections

To start, I think that Wikipedia is such and interesting thing to examine for a paper like this. While many organizations have a similar structure, I think that WIkipedia is unique and interesting to study because it is so large, so distributed, and so widely used. It can be hard enough to get a small team of people to work together on documentation. At Wikipedia’s size the complexities of making it all work must be unimaginable. It is so interesting to find out how machines and humans work together at that scale to keep the site running smoothly. The ideas and analysis seen here can easily be applied to smaller systems that are trying to accomplish the same thing.

I also think that this article serves as a great reminder of the power of AI. The fact that AI is able to do some much to help editors keep the site running smoothly even with all of the complexities of the site is amazing and it shows just how much power AI can have when applied to the right situation. A lot of the work done on Wikipedia is not hard work. The article mentions some of the things that bots do, like importing data and fixing grammatical mistakes. These things are incredibly tedious for humans to do and yet they are perfect work for machines. They can do this work almost instantly while it may take a human an hour. This not only serves as a great reminder of the power of AI’s and humans complimenting each other’s abilities, but it also shows what the power of what the internet can do. Something like this never would have been possible before in the history of human civilization. The mere fact that we can do something like this now speaks to the amazing power of the current age. 

Questions

1. Does this research have applications elsewhere? What would be the best place to apply this analysis?
2. Could this process ever be done with no human input whatsoever? Could Wikipedia one day be completely self sufficient?
3. This article talks a lot about how the bots of Wikipedia are becoming more and more important, compared to the policies and social interactions between editors. Is this happening elsewhere? Are there bots other places that we might not see and might not notice, even though they are doing a larger and larger share of the work?

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This paper is a very interesting inside look at how the inner cogs of Wikipeda functions, particularly relating to how vandalism is managed with the help of automated software tools. The tools developed unofficially by Wikipedia contributors were created out of necessity in order to a) make it easier to identify bad actors, b) automate and speed up reversions of vandalism, and c) give power to the non-experts to police obvious vandalism such as changing or deleting sections without needing a subject matter expert to do a full review of the article. The paper uses trace ethnography in order to study the usage of these tools and puts forth an interesting case study of a vandal defacing various articles and how through distributed actions by various volunteers, assisted by these tools, the vandal was identified, warned for their repeated offenses, and finally banned as their egregious actions continued, all within the span of 15 minutes and no explicit coordination among the volunteers.

I find this to be a fascinating look into distributed cognition in action, where in multiple independent actors are able to take independent action that produce a cohesive result (in the case study, multiple volunteers and automated tools identifying a vandal and issuing warnings, ultimately resulting in their ban). I find I’m thinking the work of these tools as kind of an equivalent to human body’s unconscious activities. For example, the act of walking is incredibly complex involving precise coordination of hundreds of muscles all moving at the right moments. However, we do not have to think any harder than “I want to get from here to there” and our body handles the rest. That’s kind of what it feels like these tools are, something that handles the complex busywork and leave the big decisions to us. I am wondering though how things have changed from 2009. The paper mentions that the bots tend to ignore changes made by other bots because presumably those other bots are being managed by other volunteers but the bot configuration can be changed so that it explicitly monitors other bots. I wonder how much of that functionality is used now because I am sure Wikipedia now has to deal with a lot more politically motivated vandalism, and much of it is being done by bots. Reddit is a big victim of this, so it is not hard to imagine Wikipedia faces the same problem. Of course, the adversarial bots would be a lot more clever than just pretending to be a friendly bot because that might not cut it anymore. It’s still an important thing to think about.

1. How would the functionality of Huggle and its ilk fare in the space of Reddit’s automoderator, and vice versa? Are they dealing with fundamentally different things or is there overlap?
2. How has dealing with vandalism changed on Wikipedia in the decade since this paper was published?
3. Is there a place for a heirarchy of bots, where lower level bots scan for vandalism and higher level bots make the decisions for banning, all with minimal human intervention? Or will there always need active human participation?

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Summary

The paper examines the roles of software tools in English language Wikipedia. The authors shed light on the process of counter-vandalism in Wikipedia. They explain in detail how participants and their assisted editing tools review Wikipedia contributions and enforce standards. They show that the editing process in Wikipedia is not a disconnected activity where editors force their views on others. Specifically, vandal fighting is shown as a distributed cognition process where users come to know their projects and users who edit it in a way that is impossible for a single individual. The authors claim the blocking of a vandal a cognitive process made possible by a complex network of interactions between humans, encyclopedia articles, software systems, and databases. Humans and non-humans work to produce and maintain a social order in the collaborative production of an encyclopedia with hundreds of thousands of diverse and often unorganized contributors. The authors introduce trace ethnography as a method of studying the seemingly ad-hoc assemblage of editors, administrators, bots, assisted editing tools, and others who constitute Wikipedia’s vandal fighting network.

Reflection

The paper comes off as a survey paper. I found that the authors explained some methods that already existed and used one of the authors experience to elaborate on others’ work. I couldn’t see their contribution but maybe that was needed 10 years ago? The tools that they mentioned (Huggle, AIV, Twinkle, ..etc.) were standard tools to be used when editing Wikipedia’s articles and monitoring edits made by others. They reflected on how those tools were helpful in a manner that made fighting vandalism an easier task. They mention that these tools facilitate viewing each edited article by linking it with a detailed edit summary with an explanation why it was done, by whom, and related IP addresses. They explain how they use such software to detect vandalism and how to revert back to the correct version of the article. They presented a case study of a Wikipedia vandal and showed logs of the changes that he was able to make in an hour. The authors also referenced Ed hutchins who explains how cognitive work must be performed in order to keep US Navy ships on course at any given time. And how that is a similar reference to what it takes to manage Wikipedia. Technological actors in Wikipedia, such as Huggle, make what would be a difficult task into a mundane affair. Reverting edits becomes a matter of pressing a button. The paper was informative for someone who hasn’t worked on editing Wiki articles but I thought that this paper could have been presented as a tutorial, it would’ve been more beneficial. 

Discussion

• Have you worked on Wikipedia article editing before?
• Did you encounter using the tools mentioned in the paper?
• Is there any application that comes to mind where this can be used other than Wikipedia?
• Do you think such tools could be beneficial when it comes to open source software version control?
• How would this method generalize to open source software version control?

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Summary

            Geiger and Ribes cover the case of using automated tools or “bots” in order to prevent vandalism on the popular online and user-generated encyclopedia “Wikipedia.” The authors detail how editors use popular distributed cognition coordination services such as “Huggle,” and argue that these coordination applications affect the creation and maintenance of wikipedia as much as the traditional social roles of editors. The team of human and AI work together to fight vandalism in the form of rogue edits. They cover how bots assisted essentially 0% of edits in 2006 to 12% in 2009, while editors use even more bot assistance. They then deep dive into how the editors came to ban a single vandal that committed 20 false edits to Wikipedia in an hour, which they term a “trace ethnography.”

Personal Reflection

            This work was eye-opening in seeing exactly how Wikipedia editors leverage bots and other distributed cognition to maintain order in Wikipedia. Furthermore, after reading this, I am much more confident in the accuracy of articles contained on the website (possibly to the chagrin of teachers everywhere). I was surprised how easily attack edits were repelled by the Wikipedia editors, considering that hostile bot networks could be deployed against Wikipedia as well.

            I also generally enjoyed the analogy of how managing Wikipedia is like navigating a naval vessel in that both leverage significant amounts of distributed cognition in order to succeed. Showing how many roles are needed in order to understand various jobs and collaborate between people was quite effective.

            Lastly, their focus (trace ethnography) on a single vandal was an effective way of portraying what is essentially daily life for these maintainers. I was somewhat surprised that only four people were involved before banning a user; I had figured that each vandal took much longer to identify and remedy. How the process proceeded, where the vandal got repeated warnings before a (temporary) ban occurred, and how the bots and humans worked together in order to come to this conclusion, was a fascinating process that I hadn’t seen written in a paper before.

Questions

1. One bot that this article didn’t look into is a twitter bot that tracked all changes on Wikipedia made by IP addresses used by congressional members (@CongressEdits). Its audience is not specifically intended to be the editors of Wikipedia, but how might this help them? How does this bot help the general public? (It has since been banned in 2018) How might a tool like this be abused?
2. How might a trace ethnography be used in other applications for HCI? Does this approach make sense for domains other than global editors?
3. How can Huggle (or the other tools) be changed in order to tackle a different application, such as version control? Would it be better than current tools?
4. Is there a way to exploit this system for vandals? That is, are there any weaknesses to human/bot collaboration in this case?

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Summary

This paper, through a case study, highlights the invisible distributed cognition process that goes on underneath a collaborative environment like Wikipedia, and how different actors, both humans and non-humans, come together for achieving a common goal – banning a vandal on Wikipedia. The authors show the usefulness of trace ethnography as a method for reconstructing user actions and understanding better the role each actor plays in the larger scheme of things. The paper advocates for not dismissing the role of bots as mere force multipliers, but to see them in a different lens considering the wide impact they have.

Reflection

Similar to the “Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator” paper, this paper is a great example that intelligent agents (AI-infused systems, bots, scripts, etc.) should not be studied in isolation only, but through a socio-technical lens. In my opinion, that provides a more comprehensive picture of the goals these agents can and cannot achieve, the collaboration processes they may inevitably transform, the human roles they may affect and other unintended consequences than performance/accuracy metrics alone.

Trace ethnography is a powerful method for reconstructing user actions in a distributed environment, and using that to understand how multiple actors (human and non-humans) achieve a complex objective, by sub-consciously collaborating with each other. The paper advocates that bots/automation/intelligent agents should not be seen as just force multipliers or irrelevant users. This is important as a lot of current evaluation metrics focus only on quantitative measures such as performance or accuracy. This paints an incomplete, and sometimes, an irresponsible picture of intelligent agents, as they have now evolved to assume an irreplaceable role in the larger scheme of things (or goals).

The final decision-making privilege resides with the human administrator and the whole socio-technical pipeline assists each step of decision-making with all possible information available so that checks and bounds (or order, as the paper mentions) is maintained at every stage. Automated decisions, whenever taken, are grounded in some confidence of certainty. In my opinion, while building AI models, researchers should think about the AI-infused system or the real-world setting of which these algorithms would be a part of. This might motivate researchers to make these algorithms more transparent or interpretable. The lens of the user who is going to wield these models/algorithms might help further.

It’s interesting to see some of the principles of mixed-initiative systems being used here i.e. history of the vandal’s actions, templated messages, showing statuses, etc.

Questions

1. Do you plan to use trace ethnography in your proposed project? If so, how? Why do you think it’s going to make a difference?
2. What are some of the risks and benefits of employing a fully automated pipeline in this particular case study i.e. banning a Wikipedia vandal?
3. A democratic online platform like Wikipedia supports the notion of anyone coming in and making changes, and thus necessitates deploying moderation workflows to curb bad actors. However, if a platform were restrictive to some degrees, a post-hoc setup may not be necessary and the platform might be less toxic. This does not necessarily be the case for Wikipedia and can also extend to SNS like Twitter/Facebook, etc. What would you prefer, a democratic one or a restrictive one?

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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?

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Summary: 

The paper discusses software development tools that help in moderating content posted or altered in the online encyclopedia, popularly known as Wikipedia. Wikipedia was built on the concept that anyone with an internet connection and a device could edit pages on the platform. However, such platforms with “anyone can edit” mantra are prone to malicious users, aka Vandals. Vandals are people who post inappropriate content in the form of text alteration, the introduction of offensive content, etc. Humans can be moderators who can scan for offensive content and remove them. However, this is a tedious task for humans to do. It is impossible for them to monitor huge amounts of content and look for small changes hidden in a large body of the text. To aid humans, there are fully automated softwares that are responsible for monitoring, editing, and overall maintenance of the platform. Examples of such software are Huggle and Twinkle. These tools work with humans and help in keeping the platform free from vandals by flagging users and taking appropriate actions as deemed necessary.

Reflection:

This paper was an interesting read on how offensive content is dealt with in platforms like Wikipedia. It was interesting to learn about different tools and how they interact with humans and help them in making the platform clean of bad content. These tools are extremely useful, and it makes use of machine affordance of dealing with large amounts of data. However, I feel we should also discuss the fact that machines need human interference to evaluate its performance. The paper mentions “leveraging the skills of volunteers who may not be qualified to review an article formally”, this statement is bold and leads to a lot of open questions. Yes, this makes it easy to hire people with lesser expertise, but at the same time, it makes us aware of the fact that machines are taking up some jobs and undermining humans’ expertise.  

Most of the tools mentioned are flagging content based on words, phrases, or deletion of enormous content. These can be defined to be rule-based rather than machine learning. Can we implement machine learning and deep learning algorithms where the tool learns from user behavior as Wikipedia is data-rich and could provide a lot of data to the model to train on? The paper mentioned that “significant removal of content” is placed higher on the filter queue. My only concern is sometimes a user might press enter by mistake. For example, take the case of git. Users write codes, and the difference is generally recorded and shown in the diff from the previous commit. If a coder writes new lines of code may be a line or two and press enter erroneously before or after the entire piece, the whole block shows as “newly added” in the diff. This is easy for a human to understand, but a machine flags such content, nonetheless. This may lead to extra work which normally would have been not in the queue or even lower.

The paper talks about the “talk page” where the warnings are posted by tools. This is a very good step as public shaming is needed to stop such baneful behavior. However, we can incorporate a harsher way to “shame” such users. This may be in the form of poster usernames on the main homepage for every category. This won’t work for anonymous, but maybe blocking their IP address would be a temporary fix, I feel like human and computer interaction is well defined in the paper and the concept of content controlling bots make our life easier.

Questions:

1. Are machines undermining human capabilities? Do we not need expertise any more?
2. How can such tools utilize the vast amount of data better? E.g., for training deep learning models.
3. How could such works be extended to other platforms like Twitter?

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The paper takes about the counter-vandalism process in Wikipedia focussing on both the human efforts as well as the silent non-human efforts put in. Fully-automated anti-vandalism bots are a key part of this process and play a critical role in managing the content on Wikipedia. The actors involved range from being fully autonomous software to semi-automated programs to user interfaces used by humans. A case study is presented which is an account of detecting and banning a vandal. This aims to highlight the importance and impact of bots and assisted editing programs. Vandalism-reverting software use queuing algorithms teamed with a ranking mechanism based on vandalism-identification algorithms. The queuing algorithm takes into account multiple factors like the kind of user who made the edit, revert history of the user as well as the type of edit made. The software proves to be extremely effective in presenting prospective vandals to the reviewers. User talk pages are forums utilized to take action after an offense has been reverted. This largely invisible infrastructure has been extremely critical in insulating Wikipedia from vandals, spammers, and other malevolent editors. 

I feel that the case study presented helps understand the internal working of vandalism-reverting software and it is a great example of handling a problem by leveraging the complementary strengths of AI and humans via technology. It is interesting to note that the cognitive work of identifying a vandal is distributed across a heterogeneous network and is unified using technology! This lends speed and efficiency and makes the entire system robust. I found it particularly interesting that ClueBot, after identifying a vandal, immediately reverted the edit within seconds. This edit did not have a wait in a queue for a human or a non-human bot to review but was resolved immediately using a bot.

A pivotal feature of this ecosystem that I found very fascinating was the fact that domain expertise or skill is not required to handle such vandal cases. The only expertise required of vandal fighters is in the use of the assisted editing tools themselves, and the kinds of commonsensical judgment those tools enable. This widens the eligibility criteria for prospective workers since specialized domain experts are not required.

• The queuing algorithm takes into account multiple factors like the kind of user who made the edit, revert history of the user as well as the type of edit made. Apart from the factors mentioned in the paper, what other factors can be incorporated into the queuing algorithm to improve its efficiency?
• What are some innovative ideas that can be used to further minimize the turnaround reaction time to a vandal in this ecosystem?
• What other tools can be used to leverage the complementary strengths of humans and AI using technology to detect and handle vandals in an efficient manner?

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