One of the problems with steem is how flagging works. I see a lot of censorship going on and things of that nature. If you don't like what someone is posting or have a disagreement then move on or mute them.
Yeah the first down vote I got was for insulting communism!😥 Lost some of my rep and all the income From the post, because this commie bastard held like 15K sp... @spbid is a scammer who uses multiple accounts to flag random post and then offers "protection", but all the do downvote are with minnow accounts, so that doesn't really do much. Especially the latter case is malicious flagging.
I mute stuff I don't want to see and I flag spammers and scammers (pet peeve, people who use #filmphotographytag for digital photography ( some dude uses the tag for reviews of films, not too happy with that,but I never flagged him))
Great work. I wish that I would've seen this to upvote it organically. But with that in mind, here's an additional 100% upvote from me that you didn't have to pay for. Keep up the good work.
That's a tricky topic, and I expect it's pretty hard to classify 900k accounts. there are first classifier tools, but it's far from perfect. What is "a bot"? For some it's obvious, but there are lot's of half human, half machine things out there...
And do we count activity which is just automated through an otherwise manually-managed account? I would love to know how much of the day to day voting is done by people automatically voting for stuff voted by someone else (if that made any sense). That is automation driven by off-site bots, but via the proxy of a manually operated account – and I'm not sure that we can actually determine if that kind of behavior is going on in a precise way.
Maybe we could look for trains of voting activity which occur in extremely small time slices, all focused on the same comment or post, but given the widespread knowledge that you really want to vote on something around the 20 minute mark, it might be hard to determine what is being driven via automation and what is just cleverly timed manual intervention.
At a certain point all we can do is consider our sensors versus what we can derive from them, and that gets very disappointing quickly.
Exactly. A vote 3 seconds after post is created/edited/voted-by-X is probably automated, the same comment on 100 posts as well. But there are auto-votes, trails, vote-selling, pretty clever comment bots...
Autovoters have varying response times as well, so detecting auto-votes would have to happen in ranges. Also not all trailers will always vote in the same order, so this would come down to detecting an unkown list of voters within a guessed range of time...
Stuff like this can certainly be detected somehow, but the complexity and computing time can grow pretty high pretty fast and I'm sure there will be false-positives in between.
My first inclination would be "bot-like" behavior, like rapid voting outside of human response times, posting identical comments to multiple threads, easy things like that.
It would get harder to edge into the cloud of data for more precise definitions, but some of the more obvious behavior may be a starting point.
I realize it isn't an easy classification problem, but there are some edges where it could be attacked from even in a limited sense.
Mostly yes, but there are also some higher reputation users in between. I'm also wondering why? Hiding the own comment in condenser without editing it?! Not wanting to be the first comment on a post after somebody else voted it to the top? I don't know...
Flagging my own comments to the foot of the post is my reason for a self-flag :)
Not surprisingly, 2 of 3 flags are given from higher reputation voters to lower reputation authors. With a "revenge-flag-rate" of around 27%, this is mostly expected.
What are the revenge flag %'s when coming from higher to lower, and then lower to higher? Are higher or lower reps more likely to revenge?
Interesting point to see who was first - I didn't look into this. Doing this on a 3 month time scale might give some wrong results for long going disagreements, but for most of them it should work...
Let me see what I can do :)
when I first saw things like this from experienced users in the data I was quite confused - thinking about it for a while and hearing your arguments it starts to make sense in some situations.
Thank you @lexiconial! I actually thought I was quoting the titles of posts from others by heart with these two statements, but I can't find the second one anymore. I guess some reading between the lines made it in there...
I love data! This is awesome! Thanks for posting this. I'm so amazed how the data charts almost everything so perfectly so anyone can understand. There's so much information there. Wow. There's a lot of valuable implications of this.
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One of the problems with steem is how flagging works. I see a lot of censorship going on and things of that nature. If you don't like what someone is posting or have a disagreement then move on or mute them.
Yeah the first down vote I got was for insulting communism!😥 Lost some of my rep and all the income From the post, because this commie bastard held like 15K sp...
@spbid is a scammer who uses multiple accounts to flag random post and then offers "protection", but all the do downvote are with minnow accounts, so that doesn't really do much. Especially the latter case is malicious flagging.
I mute stuff I don't want to see and I flag spammers and scammers (pet peeve, people who use #filmphotographytag for digital photography ( some dude uses the tag for reviews of films, not too happy with that,but I never flagged him))
Flagging can deter people from posting CRAP💩
this is a very interesting analysis @crokkon. both the revenge flag value and the funny fact are very interesting.
A lot of though and work went into this analysis, nice work. Have resteemed
Thank you @paulag, also for the RS!
Great work. I wish that I would've seen this to upvote it organically. But with that in mind, here's an additional 100% upvote from me that you didn't have to pay for. Keep up the good work.
wow, thanks a lot!
Thank you for the contribution. It has been approved.
Flagging is such a sensitive thing i guess. There's always fear of retaliation from the account your downvoting.
Thanks for the downvote-101. Now i know that only higher rep accounts are the ones who can affect my reputation.
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Thank you @eastmael!
I'd like an analysis done to estimate the percentage of users that are actually bots.
Its probably much higher than anyone thinks, and their sole purpose is to game the Reward Pool.
Call it the "Steemit Decline Index".
Because it is declining.
That's a tricky topic, and I expect it's pretty hard to classify 900k accounts. there are first classifier tools, but it's far from perfect. What is "a bot"? For some it's obvious, but there are lot's of half human, half machine things out there...
And do we count activity which is just automated through an otherwise manually-managed account? I would love to know how much of the day to day voting is done by people automatically voting for stuff voted by someone else (if that made any sense). That is automation driven by off-site bots, but via the proxy of a manually operated account – and I'm not sure that we can actually determine if that kind of behavior is going on in a precise way.
Maybe we could look for trains of voting activity which occur in extremely small time slices, all focused on the same comment or post, but given the widespread knowledge that you really want to vote on something around the 20 minute mark, it might be hard to determine what is being driven via automation and what is just cleverly timed manual intervention.
At a certain point all we can do is consider our sensors versus what we can derive from them, and that gets very disappointing quickly.
Exactly. A vote 3 seconds after post is created/edited/voted-by-X is probably automated, the same comment on 100 posts as well. But there are auto-votes, trails, vote-selling, pretty clever comment bots...
Autovoters have varying response times as well, so detecting auto-votes would have to happen in ranges. Also not all trailers will always vote in the same order, so this would come down to detecting an unkown list of voters within a guessed range of time...
Stuff like this can certainly be detected somehow, but the complexity and computing time can grow pretty high pretty fast and I'm sure there will be false-positives in between.
My first inclination would be "bot-like" behavior, like rapid voting outside of human response times, posting identical comments to multiple threads, easy things like that.
It would get harder to edge into the cloud of data for more precise definitions, but some of the more obvious behavior may be a starting point.
I realize it isn't an easy classification problem, but there are some edges where it could be attacked from even in a limited sense.
only downvotes from voters with a reputation higher than the author reputation can reduce the author reputation.
Really?
Didn't know that.
Yes, see for example here or here.
Those are surely from new users not knowing what they are doing.
Mostly yes, but there are also some higher reputation users in between. I'm also wondering why? Hiding the own comment in condenser without editing it?! Not wanting to be the first comment on a post after somebody else voted it to the top? I don't know...
Flagging my own comments to the foot of the post is my reason for a self-flag :)
What are the revenge flag %'s when coming from higher to lower, and then lower to higher? Are higher or lower reps more likely to revenge?
I should really start my post soon :/
Interesting point to see who was first - I didn't look into this. Doing this on a 3 month time scale might give some wrong results for long going disagreements, but for most of them it should work...
Let me see what I can do :)
a quarterly post sounds good to me :D
I flagged myself 3 times yesterday :D
Really? Why? Not voting on your own comment I can understand, but flagging it?
Long lists that i wanted to move to the bottom of the the comments section :D
when I first saw things like this from experienced users in the data I was quite confused - thinking about it for a while and hearing your arguments it starts to make sense in some situations.
A small burn in VP for the reading ease of others :)
"The most recent "Let's Clean Trending" and "Let's Randomly Flag Bid-Bot Users""
I like that you called this as it actually is in reality.
Great work and I'm sure it took a ton of effort.
Thank you @lexiconial! I actually thought I was quoting the titles of posts from others by heart with these two statements, but I can't find the second one anymore. I guess some reading between the lines made it in there...
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I love data! This is awesome! Thanks for posting this. I'm so amazed how the data charts almost everything so perfectly so anyone can understand. There's so much information there. Wow. There's a lot of valuable implications of this.
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