[Utopian Stats] Analyzing Utopian Moderation Time - Blockchain Business Intelligence
This analysis aims to give an overview of the average review time of each of the Utopian categories.
This also aims to inform Utopian moderators of the average review time of each of the categories to act as basis for them should they choose to set Key Performance Indicators (KPIs) for their teams or to the project owner to set a KPI for the whole Utopian moderation team.
Due to the amount of data that need to be analyzed and the time required to come up with these reports, this author decided to split this into multiple reports. With this report providing an overview.
This report analyzed all categories as a whole - the number of contributions, the % rejection, and the average review time.
Outline
- Contributions Overview
- Monthly Contributions
- Accepted vs Rejected
- Rejection Rate
- Per Category
- Per Moderator
- Monthly Average Review Time
- Conclusion
Scope of Analysis
The data extracted was from January 19, 2018 to March 12, 2018.
1. Contributions Overview
Based on the left chart, from Jan. 19 to Mar 12, 2018, there were more than 24000 contributions reviewed by 121 moderators.
Around 40% (9679) of these were rejected while the remaining 60% (14602) were accepted.
2. Rejection Rate
2.1. Per Category
The table on the left shows the reject rate per category sorted by number of contributions.
- translations, the top-most contributed category, before it was temporarily paused, was at 23% rejection
- ideas (or suggestions), the second most contributed, was at 57%
- While graphics, the third most contributed, was at 53%
Referring to the data on the previous section, the rejection rate of 23% for translations was below the overall average rejection rate of 40%.
This can either mean that translation contributions were high-quality that 80% were accepted or a possible leniency in moderation
2.2. Per Moderator
Based on the left table, @onos, @tykee, and @andrejcibik led all moderators with 1708, 1435, and 1031 contributions reviewed, respectively. Their rejection rate were 50%, 66%, and 54%, respectively.
The contributions they reviewed mainly came from the ideas and graphics categories.
3. Monthly Average Review Time
Based on the chart above, the average review time across all categories was 33 hours. For January it was 33 hours, for February 34 hours, and for March 27 hours.
The fastest review time was 26 seconds for this contribution while the longest review time was 545 hours for this contribution .
545 hours (23 days) was a bogus review time which prompted me to look into these contributions that took this long to review. Here were examples:
Contribution | Category | Review Time (Hours) | Last Moderator | Status |
---|---|---|---|---|
Behavior Analysis: #cebu for Cebu community(January 2018) | Analysis | 545 (23 days) | @knowledges | Rejected |
How To Build AJAX Based Navigation list in ASP.NET | Tutorial | 507 (21 days) | @espoem | Rejected |
NIGERIANS EARN FROM MUSIC THROUGH DSOUND | Visibility | 488 (20 days) | @knowledges | Rejected |
There were 83 contributions like these which I think were related with cleaning up the Utopian feed.
This prompted me to add the filter:
Remove all contributions whose review times exceeded 6 days or 144 hours
Here were the stats side-by-side for comparison:
It didn't make much difference. Here were the categories that had these cleanup activities which affected the review times.
The average review time across all categories is still 32 hours or 1.3 days.
4. Conclusion
Based on this analysis, we saw that there were more than 24000 contributions reviewed by 121 moderators with an average of 32 hours review time from January 19 to March 12, 2018.
These contributions had a 60% acceptance rate (and consequently a 40% rejection rate) with the translation category's higher at 77%.
We also looked at the rejection rate of the top 3 moderators with each moderator rejecting more than 50% of the contributions they reviewed with the rejected ones mostly coming from the ideas and graphics categories. This indicated poor quality submissions in these categories.
Afterwards, we also looked into the detail of the moderation review time and saw that the quickest review time was 26 seconds and the longest reaching 545 hours (23 days). We learned that the latter with review time exceeding 144 hours (6 days) were related with Utopian feed cleanup by supervisors.
Tools
- Power BI for charts
- Utopian.io local DB (MongoDB) for the data
- Studio 3T MongoDB for exporting data from local DB
db.posts.find({created: { $gte: '2018-01-19' }}, { 'json_metadata': 1, created: 1, author: 1 } )
db.posts.aggregate([
{ $match: { created: { $gte: '2018-01-19' } } },
{"$group": { _id: "$json_metadata.type",
count:{$sum: 1} } },
{ $sort : { 'count' : -1}}
])
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Posted on Utopian.io - Rewarding Open Source Contributors
Thank you for the contribution. It has been approved.
Hi @eastmael, great job collecting the stats for all categories! For the moderation time filters you could maybe also look for the first review on contributions - is this available in the data? I think the high moderation time outliers are mostly supervisors correcting moderator decisions.
You can contact us on Discord.
[utopian-moderator]
As for the moderation time filters, the first review on the contributio s were no longer available as the source of the data (utopian mongo db) do not store the previous moderation time. The values were overwritten by new supervisor's moderation time.
I see. Maybe it could be available in the blockchain. IIRC, a moderator decision triggers another
comment
operation on the BC with the accepted/rejected state in the metadata... So the firstcomment
operation including that metadata after the post was created should be the first moderator decision. But that probably makes things even more complicated...Hahaha. Yes, indeed. Thank you for sharing your idea on how to get the first moderator comment.
Hello @crokkon, yes indeed the high moderation time outliers were related with supervisors correcting moderator decisions. Even though the posts were already voted on by the bot, they were changed to hidden (flagged) which I think is about cleaning or correcting the Utopian feed (all active/accepted contributions).
Hey @crokkon, I just gave you a tip for your hard work on moderation. Upvote this comment to support the utopian moderators and increase your future rewards!
Hey @eastmael I am @utopian-io. I have just upvoted you!
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Hey, thanks for the work you do for us! These analysis' are invaluable, at least for me when working on my team. Keep them up!
Thank you Victor. This analyses only exist because people like us need them. Unlike some other platforms (Stackoverflow come to mind) that has no deadline (7 days til payout), having this numbers can act as a benchmark for the moderation teams. It can also act as a KPI when presenting the performance of the moderation team to outside investors. Although we already have our own audience in Steemit, which we should not overlook as well. (just thinking out loud... lol)
I saw my name was mentioned, checked it out and found this. Nice analysis mate.
Thanks for visiting and the feedback. Tough moderation efforts you've done.
Great to see you contributing to utopian,nice post such a useful content for newbee's,btw nice to meet you.
Welcome to Steemit @sarah.taylor. Yes, Utopian is pretty much where i focus since it's where my background mostly fits. Hope you enjoy your stay here.
love to see these stats as it give more insight the approval rate is more than 60 % which is great
Hello starboye. Yes, 60% is an amazing rate. The challenge for the Utopian or perhaps a Utopian university platform is how to increase this acceptance rate to above 60%. This of course need putting something in place - one thing that comes to mind is educating existing contributors.
Nice analysis this is you got all the data in a single and perfect format for all :)
Thank you blazing, yes it did take time (more than 8 hours I think, excluding the breaks I took :D) to finish this and put them in a presentable layout.
the moderators too did a great work its amazing a collective effort
Indeed! I'm also amazed how much a collective effort can bring.
Great analysis.i will remember this blog forever in my life.at this point of time its my favorite blog.
Hahaha. Wow thank you for that comment. But i'm sure there will be more memories for you here in Steemit. Welcome to Steemit.
iam a newbee here glad that i found your blog,such an amazing content,i can learn lot of things,see you soon.
Thank you. Welcome to Steemit.