Twitter admits bias in algorithm for rightwing politicians and news outlets(theguardian.com)
theguardian.com
Twitter admits bias in algorithm for rightwing politicians and news outlets
https://www.theguardian.com/technology/2021/oct/22/twitter-admits-bias-in-algorithm-for-rightwing-politicians-and-news-outlets
9 comments
It would not work as both "sides" claim the other to be over-represented. In truth they want to silence each other if we are honest. I don't like Twitter making content decision but I guess that ship has sailed long ago for any large social media platform.
Oh well … the ship has sailed … nothing can be done about it.
Make you wonder what kind of engagement it is. Likes? Shares? Agreement? Discussion? Outraged shouting?
I think this is the type of question we're left with, usually, after analysing the behaviour of any ranking/amplification algorithm.
The reductive conclusion is either "right wing tweets are more engaging" or "the algorithm amplifies right wing tweets." A reduction implies that either the algorithm or users are neutral. Without algorithmic bias, users will amplify both equally. Or conversely, results reflect user bias/preference/behaviour because the software is unbiased.
Like a lot of reductions, these are true but incomplete. We know damn well that software design will affect the popularity of various post types. Most forums have active mitigation strategies to avoid certain types of content from becoming overabundant. HN has the "middlebrow dismissal" rule, for example. We also have no reason to believe that users, on average, are "unbiased" or that any specific outcome represents and unbiased reflection collective user views.
There's no doubt that the character of HN's homepage, or reddit, FB & Twitter reflection of both moderation/software decisions and "organic" user preferences. There's no hard line between them.
OTOH, this doesn't mean "nothing to see here."
This is also not unique to social media. You can clearly see that specific traditional media stations/publications favour specific views. More importantly, they amplify certain stories, cover certain candidates, and have many other preferences and biases that have big political impacts.
Take this example on a recent US bill's media coverage: https://thecolumn.substack.com/p/on-reconciliation-bill-cnn-...
Here, instead of "left vs right," they distinguish between "horse race coverage" and "substance." 91.3% of CNN's coverage was found to be "horse race coverage?" Is that a bias? Pattern? Editorial Decision." Reflection of user interest? It sure does affect politics though. Frames the whole debate.
The reductive conclusion is either "right wing tweets are more engaging" or "the algorithm amplifies right wing tweets." A reduction implies that either the algorithm or users are neutral. Without algorithmic bias, users will amplify both equally. Or conversely, results reflect user bias/preference/behaviour because the software is unbiased.
Like a lot of reductions, these are true but incomplete. We know damn well that software design will affect the popularity of various post types. Most forums have active mitigation strategies to avoid certain types of content from becoming overabundant. HN has the "middlebrow dismissal" rule, for example. We also have no reason to believe that users, on average, are "unbiased" or that any specific outcome represents and unbiased reflection collective user views.
There's no doubt that the character of HN's homepage, or reddit, FB & Twitter reflection of both moderation/software decisions and "organic" user preferences. There's no hard line between them.
OTOH, this doesn't mean "nothing to see here."
This is also not unique to social media. You can clearly see that specific traditional media stations/publications favour specific views. More importantly, they amplify certain stories, cover certain candidates, and have many other preferences and biases that have big political impacts.
Take this example on a recent US bill's media coverage: https://thecolumn.substack.com/p/on-reconciliation-bill-cnn-...
Here, instead of "left vs right," they distinguish between "horse race coverage" and "substance." 91.3% of CNN's coverage was found to be "horse race coverage?" Is that a bias? Pattern? Editorial Decision." Reflection of user interest? It sure does affect politics though. Frames the whole debate.
I don’t see the claims from Twitter matching the headline The Guardian is running. Twitter’s internal research is saying that certain tweets are organically more popular and engaging and more likely to be shown, not that the algorithm has an ideological bias. If you search for the word “bias” in Twitter’s post you’ll not find support for The Guardian’s editorialization. Here’s an important excerpt, which notes that more analysis is needed to understand whether the amplification is unnatural relative to user interactions:
> Algorithmic amplification is problematic if there is preferential treatment as a function of how the algorithm is constructed versus the interactions people have with it. Further root cause analysis is required in order to determine what, if any, changes are required to reduce adverse impacts by our Home timeline algorithm.
This study also leaves out a very important consideration, which is the impact of Twitter’s moderation along political lines. I suspect that’s where the true bias lies and given Twitter’s content policies reflect progressive ideology, it is very likely that bias leans left.
Twitter’s original blog post: https://blog.twitter.com/en_us/topics/company/2021/rml-polit...
> Algorithmic amplification is problematic if there is preferential treatment as a function of how the algorithm is constructed versus the interactions people have with it. Further root cause analysis is required in order to determine what, if any, changes are required to reduce adverse impacts by our Home timeline algorithm.
This study also leaves out a very important consideration, which is the impact of Twitter’s moderation along political lines. I suspect that’s where the true bias lies and given Twitter’s content policies reflect progressive ideology, it is very likely that bias leans left.
Twitter’s original blog post: https://blog.twitter.com/en_us/topics/company/2021/rml-polit...
> which notes that more analysis is needed to understand whether the amplification is unnatural relative to user interactions
Whether it’s natural or not is irrelevant IMHO.
If a post fanning the flames of a religious war or ethnic cleansing is “organically popular” should the algorithm amplified it?
It should also be noted that just because something is popular doesn’t mean it’s a factually correct piece of information or a sensible course of action.
Sometimes lies and bad ideas are more (emotionally) appealing and eye catching but that doesn’t mean one should propagate them.
Reddit and HN are guilty of this too given how their vote based system works.
Whether it’s natural or not is irrelevant IMHO.
If a post fanning the flames of a religious war or ethnic cleansing is “organically popular” should the algorithm amplified it?
It should also be noted that just because something is popular doesn’t mean it’s a factually correct piece of information or a sensible course of action.
Sometimes lies and bad ideas are more (emotionally) appealing and eye catching but that doesn’t mean one should propagate them.
Reddit and HN are guilty of this too given how their vote based system works.
“Sometimes lies and bad ideas are more (emotionally) appealing and eye catching but that doesn’t mean one should propagate them.”
How does an algorithm determine what is a lie or bad idea? Please share.
How does an algorithm determine what is a lie or bad idea? Please share.
> How does an algorithm determine what is a lie or bad idea? Please share.
I don't know. Perhaps there will never be an algorithm that can do it even "approximately" never mind accurately - OK maybe "never" is too strong a word.
It doesn't change the fact that the current way we are doing things are causing problems.
In the past we rely on fallible editors to filter the news. It wasn't perfect. However I think they did a better job than the algorithms of Twitter and Facebook - most editors had some common sense with regards to the effects of what they allow to go to print; the more responsible ones will do due diligence and fact check stuff before printing it (as imperfect as their ability to do so is, it's better than the no fact checking at all done by Twitter/Facebook before they amplify it to millions of people).
I don't know. Perhaps there will never be an algorithm that can do it even "approximately" never mind accurately - OK maybe "never" is too strong a word.
It doesn't change the fact that the current way we are doing things are causing problems.
In the past we rely on fallible editors to filter the news. It wasn't perfect. However I think they did a better job than the algorithms of Twitter and Facebook - most editors had some common sense with regards to the effects of what they allow to go to print; the more responsible ones will do due diligence and fact check stuff before printing it (as imperfect as their ability to do so is, it's better than the no fact checking at all done by Twitter/Facebook before they amplify it to millions of people).
>Twitter’s internal research is saying that certain tweets are organically more popular and engaging and more likely to be shown, not that the algorithm has an ideological bias
No one is claiming that there's a piece of code that detects a Twitter user being a right-wing politician and then showing it to more users.
Whether the bias is deliberate or not, it's still a bias. Right wing gets their message amplified more than the left.
No one is claiming that there's a piece of code that detects a Twitter user being a right-wing politician and then showing it to more users.
Whether the bias is deliberate or not, it's still a bias. Right wing gets their message amplified more than the left.
Perhaps I didn’t explain my opinion clearly, but I think we might have different definitions for what constitutes bias. You’re suggesting that a difference in outcomes means something is wrong or unfair with the algorithm. But what if it is that right wing users simply engage more, or that the right wing news outlets put out better content, or other such factor? I wouldn’t view the increased algorithmic amplification as problematic if there were such root causes behind it. Put another way, I am taking an equality lens (equality of opportunity) rather than an equity lens (equality of outcomes). If we take an equity lens, then you can start questioning why any number of things don’t get the same amplification - it doesn’t have to stop at a binary of “left versus right”.
That's not the definition of bias, in either the colloquial or statistics sense.
A platform promoting one side of the political spectrum over another is not bias? There's no deliberation needed to meet the definition of bias. Every person have unconscious biases, and any structural system, including algorithms that promote content on social media can have unconscious biases as well. Simply stating that it's not bias does not cut it for me.
OK, I admit it's not unequivocally not bias. It could be bias. I would just say there could be other explanations, such as a different ratio between non-politician activists to politicians on either side of the isle and how each slice of the pie tends to monopolize limited attention bandwidth.
If three political tweets by Lebron James are algorithmically boosted, and three political tweets by Ted Cruz are boosted, I wouldn't call that bias necessarily even though the metric being studied did.
If three political tweets by Lebron James are algorithmically boosted, and three political tweets by Ted Cruz are boosted, I wouldn't call that bias necessarily even though the metric being studied did.
How would you enforce, e.g. hate speech, moderation if there are both left and right wing definitions for it?
Or Twitter's content policies could follow the law and reflect the political leaning of the governement, but then how do you enforce moderation on a free/liberal social network which by definition should be free from governement censorship?
Or Twitter's content policies could follow the law and reflect the political leaning of the governement, but then how do you enforce moderation on a free/liberal social network which by definition should be free from governement censorship?
Actually the guardian headline is perfectly accurate. It completely is accurate to say the algorithm has a bias, even if that bias isn't "if (right-wing) then amplify()"
> If you search for the word “bias” in Twitter’s post you’ll not find support for The Guardian’s editorialization
But if you search for "favour" you'll find "[our findings revealed that] algorithmic amplification favours right-leaning news source". In the abstract. Which means the exact same thing.
> Here’s an important excerpt, which notes that more analysis is needed to understand whether the amplification is unnatural relative to user interactions
The guardian article includes your quote so I don't think you can accuse it of omitting important context.
> Twitter’s internal research is saying that certain tweets are organically more popular and engaging and more likely to be shown
I'm not sure where you're reading this from. It is pretty explicit that further research is required in to the cause of the discrepancy.
> If you search for the word “bias” in Twitter’s post you’ll not find support for The Guardian’s editorialization
But if you search for "favour" you'll find "[our findings revealed that] algorithmic amplification favours right-leaning news source". In the abstract. Which means the exact same thing.
> Here’s an important excerpt, which notes that more analysis is needed to understand whether the amplification is unnatural relative to user interactions
The guardian article includes your quote so I don't think you can accuse it of omitting important context.
> Twitter’s internal research is saying that certain tweets are organically more popular and engaging and more likely to be shown
I'm not sure where you're reading this from. It is pretty explicit that further research is required in to the cause of the discrepancy.
lol yeah right, more like the opposite.
Didn't they totally ban Trump?
Didn't they totally ban Trump?
For breaking their ToS literally hundreds of times? If anything it showed incredible bias they kept him on the platform just because of his job ( and the fear of retaliation). If he were a random person, he would have been banned much earlier.
A human decision to ban an individual has no relation to the biases of an algorithm.
Sure it does. If someone burns down a whole forest, it doesn't really matter if someone else cuts down a single tree.
? The specific claim made by the headline is about algorithmic bias. I do not recall an algorithmic banning of Donald Trump.
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Emergence[1] happens. This is a cautionary point about automating societal processes. In the extreme case, we could arrive at SkyNet.
[1] https://en.m.wikipedia.org/wiki/Emergence
[1] https://en.m.wikipedia.org/wiki/Emergence
I skimmed through the study [1], and it seems that the methodology is as follows:
They define a metric called "amplification" ratio for a set of tweets. Roughly, for a specific set of the tweets, it is the ratio of their "reach" in the sample of users with the chronological timeline (control, 1% of global users), to the sample of users with the ML timeline (treatment, 4% of global users). For a sample of users, reach of a set of tweets is defined as the share of the sample which encounter at least one of the tweets in the set. (the amplification metric is actually shifted so that 0% means a ratio of 1, i.e. equal reach)
Then, they took a sample of right-wing and left-wing politions and media and calculated these amplification ratios (for individual accounts, and for the left-team and right-team grouped together, etc.). Generally, this "amplification" metric was larger for right-wing accounts (or groups of accounts).
I think the use of that metric for measuring bias is misleading though, in the sense that they do not account for the fact that Twitter users are mostly left-wing [2], and this does significantly effect the metric they have chosen.
Assume that I'm left-winger who does not follow any right-wing politions. Then probably any sensible algorithm which includes tweets in my timeline from accounts that I do not follow will increase the right-wing "amplification" metric, as it is enough for it to show me just one single tweet from a right-winger. If my understanding is correct, their measure for amplification is way too sensitive. (and it is worse when applied to understand the reach of a larger group of the accounts, as an encounter with a single tweet from any of the members of the group is counted as reach)
[1]: https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter...
[2]: There are many studies on this, e.g. this one by Pew Research Center: https://archive.md/iEJaq
They define a metric called "amplification" ratio for a set of tweets. Roughly, for a specific set of the tweets, it is the ratio of their "reach" in the sample of users with the chronological timeline (control, 1% of global users), to the sample of users with the ML timeline (treatment, 4% of global users). For a sample of users, reach of a set of tweets is defined as the share of the sample which encounter at least one of the tweets in the set. (the amplification metric is actually shifted so that 0% means a ratio of 1, i.e. equal reach)
Then, they took a sample of right-wing and left-wing politions and media and calculated these amplification ratios (for individual accounts, and for the left-team and right-team grouped together, etc.). Generally, this "amplification" metric was larger for right-wing accounts (or groups of accounts).
I think the use of that metric for measuring bias is misleading though, in the sense that they do not account for the fact that Twitter users are mostly left-wing [2], and this does significantly effect the metric they have chosen.
Assume that I'm left-winger who does not follow any right-wing politions. Then probably any sensible algorithm which includes tweets in my timeline from accounts that I do not follow will increase the right-wing "amplification" metric, as it is enough for it to show me just one single tweet from a right-winger. If my understanding is correct, their measure for amplification is way too sensitive. (and it is worse when applied to understand the reach of a larger group of the accounts, as an encounter with a single tweet from any of the members of the group is counted as reach)
[1]: https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter...
[2]: There are many studies on this, e.g. this one by Pew Research Center: https://archive.md/iEJaq
The proposed change would then introduce conscious political bias to ensure an even representation of the spectrum... but I wonder how we define even? Perhaps we can base it on user's constituencies?