Viewing last 25 versions of post by doloresbridge in topic Don't blame me, I voted for the other guy. (Politics General)

doloresbridge
Solar Supporter - Fought against the New Lunar Republic rebellion on the side of the Solar Deity (April Fools 2023).
Non-Fungible Trixie -
Preenhub - We all know what you were up to this evening~
My Little Pony - 1992 Edition

Peace to all
[@Kiryu-Chan](/forums/generals/topics/tartarus?post_id=5236117#post_5236117)
Heard about this but didn't get the chance to read it at the time. This is a interesting read, though I am not sure this means exactly what a lot of people would say it means.
> **This effect is strongest in Canada (Liberals 43% vs Conservatives 167%) and the United Kingdom (Labour 112% vs Conservatives 176%). In both countries the Prime Ministers and members of the Government are also Members of the Parliament and are thus included in our analysis.** We therefore recomputed the amplification statistics after excluding top government officials. Our findings, shown in SI Fig. S2.,remained qualitatively similar. When studying amplification at the level of individual politicians (Fig. 1C), we find that amplification varies substantially within each political party: **while Tweets from some individual politicians are amplified up to 400%, for others amplification is below 0%, meaning they reach fewer users on ranked timelines than they do on chronological ones.**
>
> We repeated the comparison between major left-wing and right-wing parties, comparing the distribution of individual amplification values between parties. When studied at the individual level, a permutation test detected no statistically significant association between an individual’s party affiliation and their amplification. We see that comparing political parties on the basis of aggregate amplification of the entire party (Fig. 1A-B) or on the basis of individual amplification of their members (Fig. 1C) leads to seemingly different conclusions: while individual amplification is not associated with party membership, the aggregate group amplification may be different for each party. **These findings are not contradictory, considering that different politicians may reach overlapping audiences. Even if the amplification of individual politicians is uncorrelated with their political affiliation, when we consider increases to their combined reach, group-level correlations might emerge.** For a more detailed discussion please refer to SI Section 1.E.3.
>
> Our fine-grained data also allows us to evaluate whether recommender systems amplify extreme ideologies, far-left or far-right politicians, over more moderate ones [37]. **We found that in countries where far-left or far-right parties have substantial representation among elected officials (e.g. VOX in Spain, Die Linke and AfD in Germany, LFI and RN in France) the amplification of these parties is generally lower than that of moderate/centrist parties in the same country (see Fig. S1)**. Finally, we considered whether personalization consistently amplifies messages from governing coalition or the opposition, and found no consistent pattern across countries. For example, in the United Kingdom amplification favors the governing Conservatives, while in Canada the opposition Conservative Party of Canada is more highly amplified
*Note, I am quoting from [the PDF](https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf), there was significant formatting issues copy pasting parts of this and I tried to fix it as best I can but some could have still slipped past me.*

Lots of interesting stuff to highlight here that would probably get lost in the argument that I would imagine play it. So, before I give my two bits on the US portions of this I think it is worth to consider that this shows a lot of _wide_ reaching effects of there machine learning algorithm versus there old control group in the old system.

[It was argued by some at the time that the algorithm was boosting](https://archive.ph/gDiBd) tweets based on outrage. Kind of twitters MO. conservative politicians would be more likely than to be mocked/argued with and is still based on real audience interaction over some sort of artificial favoritism out of manipulation for conservative causes. Look at the graphs on page 4 for example. Notice how the Democrats in the House are much closer to Republicans in amplification then the ones in the Senate? The Democrats have far more super stars in the house and that is where the energy is while in the Senate, you may have Bernie Sanders and Elizabeth Warren, but the majority of the rest don't have that energy, progressive or not, and a lot of them are seen by the base as old and stuffy. Most Republican Senators lack strong online followings as well for similar reasons but still certainly have tweets get attention from resist Twitter and whomever else calling them out. It does fit.

Does all the data fit that though? I don't know. I need more information on dynamics. Especially on of the non Anglosphere countries before I can make that judgement and I am not really sure this is a silver bullet where someone can argue that wipes away all of my sides grievances with the platform. Still, thanks for the link, it was a interesting read and I hope to read through it further later when I have more time.


> Experimental Setup
> Below we outline this experimental setup and its inherent limitations. We then introduce a measure of algorithmic amplification in order to quantify the degree to which di erent political groups benefit from algorithmic personalization. When Twitter introduced machine learning to personalize the Home timeline in 2016, it excluded a randomly chosen control group of 1% of all global Twitter users from the new personalized Home timeline. Individuals in this control group have never experienced personalized ranked timelines. Instead their Home timeline continues to display Tweets and Retweets from accounts they follow in reverse-chronological order. The treatment group corresponds to a sample of 4% of all other accounts who experience the personalized Home timeline. However, even individuals in the treatment group do have the option to opt out of personalization (SI Section A). The experimental setup has some inherent limitations. A first limitation stems from interaction effects between individuals in the analysis [3].
>
> In social networks, the control group can never be isolated from indirect effects of personalization as individuals in the control group encounter content shared by users in the treatment group. Therefore,
although a randomized controlled experiment, our experiment does not satisfy the well-known Stable Unit Treatment Value Assumption (SUTVA) from causal inference [14]. As a consequence, it cannot provide unbiased estimates of causal quantities of interest, such as the average treatment effect (ATE). In this study, we chose to not employ intricate causal inference machinery that is often used to approximate causal quantities [17], as these would not guarantee unbiased estimates in the complex setting of
>
> Twitter’s home timeline algorithm. Building an elaborate causal diagram of this complex system is well beyond the scope of our observational study. Instead, we present findings based on simple
comparison of measurements between the treatment and control groups. Intuitively, we expect peer effects to decrease observable differences between the control and treatment groups, thus, our reported statistics likely underestimate the true causal effects of personalization.
>
> A second limitation pertains to the fact that differences between treatment and control group were previously used by Twitter to improve the personalized ranking experience. **The treatment, i.e., the ranking experience, has therefore not remained the same over time.** Moreover, the changes to the treatment depend on the experiment itself.

This also could be issues with the data. Especially if their algorithm was changed around over time.

[@Penguin Dragneel](/forums/generals/topics/tartarus?post_id=5236145#post_5236145)
It's still a big website that can't simply be moderated 100% all the time. The way Twitter works, I am not sure it ever could. I don't think it is any pro-right wing bias among it's employees, that is for sure.

[@BlitztheDragon](/forums/generals/topics/tartarus?post_id=5236157#post_5236157)
> without explanation banned /r/rojava
I believe it was suspected that Turkish accounts had mass reported the sub but I heard that accusation second hand.
Reason: The new syntax I am still getting the hang of part 4
Edited by doloresbridge
doloresbridge
Solar Supporter - Fought against the New Lunar Republic rebellion on the side of the Solar Deity (April Fools 2023).
Non-Fungible Trixie -
Preenhub - We all know what you were up to this evening~
My Little Pony - 1992 Edition

Peace to all
[@Kiryu-Chan](/forums/generals/topics/tartarus?post_id=5236117#post_5236117)
Heard about this but didn't get the chance to read it at the time. This is a interesting read, though I am not sure this means exactly what a lot of people would say it means.
> **This effect is strongest in Canada (Liberals 43% vs Conservatives 167%) and the United Kingdom (Labour 112% vs Conservatives 176%). In both countries the Prime Ministers and members of the Government are also Members of the Parliament and are thus included in our analysis.** We therefore recomputed the amplification statistics after excluding top government officials. Our findings, shown in SI Fig. S2.,remained qualitatively similar. When studying amplification at the level of individual politicians (Fig. 1C), we find that amplification varies substantially within each political party: **while Tweets from some individual politicians are amplified up to 400%, for others amplification is below 0%, meaning they reach fewer users on ranked timelines than they do on chronological ones.**
>
> We repeated the comparison between major left-wing and right-wing parties, comparing the distribution of individual amplification values between parties. When studied at the individual level, a permutation test detected no statistically significant association between an individual’s party affiliation and their amplification. We see that comparing political parties on the basis of aggregate amplification of the entire party (Fig. 1A-B) or on the basis of individual amplification of their members (Fig. 1C) leads to seemingly different conclusions: while individual amplification is not associated with party membership, the aggregate group amplification may be different for each party. **These findings are not contradictory, considering that different politicians may reach overlapping audiences. Even if the amplification of individual politicians is uncorrelated with their political affiliation, when we consider increases to their combined reach, group-level correlations might emerge.** For a more detailed discussion please refer to SI Section 1.E.3.
>
> Our fine-grained data also allows us to evaluate whether recommender systems amplify extreme ideologies, far-left or far-right politicians, over more moderate ones [37]. **We found that in countries where far-left or far-right parties have substantial representation among elected officials (e.g. VOX in Spain, Die Linke and AfD in Germany, LFI and RN in France) the amplification of these parties is generally lower than that of moderate/centrist parties in the same country (see Fig. S1)**. Finally, we considered whether personalization consistently amplifies messages from governing coalition or the opposition, and found no consistent pattern across countries. For example, in the United Kingdom amplification favors the governing Conservatives, while in Canada the opposition Conservative Party of Canada is more highly amplified
*Note, I am quoting from [the PDF](https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf), there was significant formatting issues copy pasting parts of this and I tried to fix it as best I can but some could have still slipped past me.*

Lots of interesting stuff to highlight here that would probably get lost in the argument that I would imagine play it. So, before I give my two bits on the US portions of this I think it is worth to consider that this shows a lot of _wide_ reaching effects of there machine learning algorithm versus there old control group in the old system.

[It was argued by some at the time that the algorithm was boosting](https://archive.ph/gDiBd) tweets based on outrage. Kind of twitters MO. conservative politicians would be more likely than to be mocked/argued with and is still based on real audience interaction over some sort of artificial favoritism out of manipulation for conservative causes. Look at the graphs on page 4 for example. Notice how the Democrats in the House are much closer to Republicans in amplification then the ones in the Senate? The Democrats have far more super stars in the house and that is where the energy is while in the Senate, you may have Bernie Sanders and Elizabeth Warren, but the majority of the rest don't have that energy, progressive or not, and a lot of them are seen by the base as old and stuffy. Most Republican Senators lack strong online followings as well for similar reasons but still certainly have tweets get attention from resist Twitter and whomever else calling them out. It does fit.

Does all the data fit that though? I don't know. I need more information on dynamics. Especially on of the non Anglosphere countries before I can make that judgement and I am not really sure this is a silver bullet where someone can argue that wipes away all of my sides grievances with the platform. Still, thanks for the link, it was a interesting read and I hope to read through it further later when I have more time.


> Experimental Setup
> Below we outline this experimental setup and its inherent limitations. We then introduce a measure of algorithmic amplification in order to quantify the degree to which di erent political groups benefit from algorithmic personalization. When Twitter introduced machine learning to personalize the Home timeline in 2016, it excluded a randomly chosen control group of 1% of all global Twitter users from the new personalized Home timeline. Individuals in this control group have never experienced personalized ranked timelines. Instead their Home timeline continues to display Tweets and Retweets from accounts they follow in reverse-chronological order. The treatment group corresponds to a sample of 4% of all other accounts who experience the personalized Home timeline. However, even individuals in the treatment group do have the option to opt out of personalization (SI Section A). The experimental setup has some inherent limitations. A first limitation stems from interaction effects between individuals in the analysis [3].
>
> In social networks, the control group can never be isolated from indirect effects of personalization as individuals in the control group encounter content shared by users in the treatment group. Therefore,
although a randomized controlled experiment, our experiment does not satisfy the well-known Stable Unit Treatment Value Assumption (SUTVA) from causal inference [14]. As a consequence, it cannot provide unbiased estimates of causal quantities of interest, such as the average treatment effect (ATE). In this study, we chose to not employ intricate causal inference machinery that is often used to approximate causal quantities [17], as these would not guarantee unbiased estimates in the complex setting of
>
> Twitter’s home timeline algorithm. Building an elaborate causal diagram of this complex system is well beyond the scope of our observational study. Instead, we present findings based on simple
comparison of measurements between the treatment and control groups. Intuitively, we expect peer effects to decrease observable differences between the control and treatment groups, thus, our reported statistics likely underestimate the true causal effects of personalization.
>
> A second limitation pertains to the fact that differences between treatment and control group were previously used by Twitter to improve the personalized ranking experience. **The treatment, i.e., the ranking experience, has therefore not remained the same over time.** Moreover, the changes to the treatment depend on the experiment itself.

This also could be issues with the data. Especially if their algorithm was changed around over time.

[@Penguin Dragneel](/forums/generals/topics/tartarus?post_id=5236145#post_5236145)
It's still a big website that can't simply be moderated 100% all the time. The way Twitter works, I am not sure it ever could. I don't think it is any pro-right wing bias among it's employees, that is for sure.

[@BlitztheDragon](/forums/generals/topics/tartarus?post_id=5236157#post_5236157)
> without explanation banned /r/rojava
I believe it was suspected that Turkish accounts had mass reported the sub but I heard that accusation second hand.
Reason: The new syntax I am still getting the hang of part 3
Edited by doloresbridge
doloresbridge
Solar Supporter - Fought against the New Lunar Republic rebellion on the side of the Solar Deity (April Fools 2023).
Non-Fungible Trixie -
Preenhub - We all know what you were up to this evening~
My Little Pony - 1992 Edition

Peace to all
[@Kiryu-Chan](/forums/generals/topics/tartarus?post_id=5236117#post_5236117)
Heard about this but didn't get the chance to read it at the time. This is a interesting read, though I am not sure this means exactly what a lot of people would say it means.
> **This effect is strongest in Canada (Liberals 43% vs Conservatives 167%) and the United Kingdom (Labour 112% vs Conservatives 176%). In both countries the Prime Ministers and members of the Government are also Members of the Parliament and are thus included in our analysis.** We therefore recomputed the amplification statistics after excluding top government officials. Our findings, shown in SI Fig. S2.,remained qualitatively similar. When studying amplification at the level of individual politicians (Fig. 1C), we find that amplification varies substantially within each political party: **while Tweets from some individual politicians are amplified up to 400%, for others amplification is below 0%, meaning they reach fewer users on ranked timelines than they do on chronological ones.**
>
> We repeated the comparison between major left-wing and right-wing parties, comparing the distribution of individual amplification values between parties. When studied at the individual level, a permutation test detected no statistically significant association between an individual’s party affiliation and their amplification. We see that comparing political parties on the basis of aggregate amplification of the entire party (Fig. 1A-B) or on the basis of individual amplification of their members (Fig. 1C) leads to seemingly different conclusions: while individual amplification is not associated with party membership, the aggregate group amplification may be different for each party. **These findings are not contradictory, considering that different politicians may reach overlapping audiences. Even if the amplification of individual politicians is uncorrelated with their political affiliation, when we consider increases to their combined reach, group-level correlations might emerge.** For a more detailed discussion please refer to SI Section 1.E.3.
>
> Our fine-grained data also allows us to evaluate whether recommender systems amplify extreme ideologies, far-left or far-right politicians, over more moderate ones [37]. **We found that in countries where far-left or far-right parties have substantial representation among elected officials (e.g. VOX in Spain, Die Linke and AfD in Germany, LFI and RN in France) the amplification of these parties is generally lower than that of moderate/centrist parties in the same country (see Fig. S1)**. Finally, we considered whether personalization consistently amplifies messages from governing coalition or the opposition, and found no consistent pattern across countries. For example, in the United Kingdom amplification favors the governing Conservatives, while in Canada the opposition Conservative Party of Canada is more highly amplified
*Note, I am quoting from [the PDF](https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf), there was significant formatting issues copy pasting parts of this and I tried to fix it as best I can but some could have still slipped past me.*

Lots of interesting stuff to highlight here that would probably get lost in the argument that I would imagine play it. So, before I give my two bits on the US portions of this I think it is worth to consider that this shows a lot of _wide_ reaching effects of there machine learning algorithm versus there old control group in the old system.

[It was argued by some at the time that the algorithm was boosting](https://archive.ph/gDiBd) tweets based on outrage. Kind of twitters MO. conservative politicians would be more likely than to be mocked/argued with and is still based on real audience interaction over some sort of artificial favoritism out of manipulation for conservative causes. Look at the graphs on page 4 for example. Notice how the Democrats in the House are much closer to Republicans in amplification then the ones in the Senate? The Democrats have far more super stars in the house and that is where the energy is while in the Senate, you may have Bernie Sanders and Elizabeth Warren, but the majority of the rest don't have that energy, progressive or not, and a lot of them are seen by the base as old and stuffy. Most Republican Senators lack strong online followings as well for similar reasons but still certainly have tweets get attention from resist Twitter and whomever else calling them out. It does fit.

Does all the data fit that though? I don't know. I need more information on dynamics. Especially on of the non Anglosphere countries before I can make that judgement and I am not really sure this is a silver bullet where someone can argue that wipes away all of my sides grievances with the platform. Still, thanks for the link, it was a interesting read and I hope to read through it further later when I have more time.


> Experimental Setup
> Below we outline this experimental setup and its inherent limitations. We then introduce a measure of algorithmic amplification in order to quantify the degree to which di erent political groups benefit from algorithmic personalization. When Twitter introduced machine learning to personalize the Home timeline in 2016, it excluded a randomly chosen control group of 1% of all global Twitter users from the new personalized Home timeline. Individuals in this control group have never experienced personalized ranked timelines. Instead their Home timeline continues to display Tweets and Retweets from accounts they follow in reverse-chronological order. The treatment group corresponds to a sample of 4% of all other accounts who experience the personalized Home timeline. However, even individuals in the treatment group do have the option to opt out of personalization (SI Section A). The experimental setup has some inherent limitations. A first limitation stems from interaction effects between individuals in the analysis [3].
>
> In social networks, the control group can never be isolated from indirect effects of personalization as individuals in the control group encounter content shared by users in the treatment group. Therefore,
although a randomized controlled experiment, our experiment does not satisfy the well-known Stable Unit Treatment Value Assumption (SUTVA) from causal inference [14]. As a consequence, it cannot provide unbiased estimates of causal quantities of interest, such as the average treatment effect (ATE). In this study, we chose to not employ intricate causal inference machinery that is often used to approximate causal quantities [17], as these would not guarantee unbiased estimates in the complex setting of
>
> Twitter’s home timeline algorithm. Building an elaborate causal diagram of this complex system is well beyond the scope of our observational study. Instead, we present findings based on simple
comparison of measurements between the treatment and control groups. Intuitively, we expect peer effects to decrease observable differences between the control and treatment groups, thus, our reported statistics likely underestimate the true causal effects of personalization.
>
> A second limitation pertains to the fact that differences between treatment and control group were previously used by Twitter to improve the personalized ranking experience. **The treatment, i.e., the ranking experience, has therefore not remained the same over time.** Moreover, the changes to the treatment depend on the experiment itself.

This also could be issues with the data. Especially if their algorithm was changed around over time.

[@Penguin Dragneel](/forums/generals/topics/tartarus?post_id=5236145#post_5236145)
It's still a big website that can't simply be moderated 100% all the time. The way Twitter works, I am not sure it ever could. I don't think it is any pro-right wing bias among it's employees, that is for sure.

[@BlitztheDragon](/forums/generals/topics/tartarus?post_id=5236157#post_5236157)
> without explanation banned /r/rojava
I believe it was suspected that Turkish accounts had mass reported the sub but I heard that accusation second hand.
Reason: The new syntax I am still getting the hang of part 2
Edited by doloresbridge
doloresbridge
Solar Supporter - Fought against the New Lunar Republic rebellion on the side of the Solar Deity (April Fools 2023).
Non-Fungible Trixie -
Preenhub - We all know what you were up to this evening~
My Little Pony - 1992 Edition

Peace to all
[@Kiryu-Chan](/forums/generals/topics/tartarus?post_id=5236117#post_5236117)
Heard about this but didn't get the chance to read it at the time. This is a interesting read, though I am not sure this means exactly what a lot of people would say it means.
> **This effect is strongest in Canada (Liberals 43% vs Conservatives 167%) and the United Kingdom (Labour 112% vs Conservatives 176%). In both countries the Prime Ministers and members of the Government are also Members of the Parliament and are thus included in our analysis.** We therefore recomputed the amplification statistics after excluding top government officials. Our findings, shown in SI Fig. S2.,remained qualitatively similar. When studying amplification at the level of individual politicians (Fig. 1C), we find that amplification varies substantially within each political party: **while Tweets from some individual politicians are amplified up to 400%, for others amplification is below 0%, meaning they reach fewer users on ranked timelines than they do on chronological ones.**
>
> We repeated the comparison between major left-wing and right-wing parties, comparing the distribution of individual amplification values between parties. When studied at the individual level, a permutation test detected no statistically significant association between an individual’s party affiliation and their amplification. We see that comparing political parties on the basis of aggregate amplification of the entire party (Fig. 1A-B) or on the basis of individual amplification of their members (Fig. 1C) leads to seemingly different conclusions: while individual amplification is not associated with party membership, the aggregate group amplification may be different for each party. **These findings are not contradictory, considering that different politicians may reach overlapping audiences. Even if the amplification of individual politicians is uncorrelated with their political affiliation, when we consider increases to their combined reach, group-level correlations might emerge.** For a more detailed discussion please refer to SI Section 1.E.3.
>
> Our fine-grained data also allows us to evaluate whether recommender systems amplify extreme ideologies, far-left or far-right politicians, over more moderate ones [37]. **We found that in countries where far-left or far-right parties have substantial representation among elected officials (e.g. VOX in Spain, Die Linke and AfD in Germany, LFI and RN in France) the amplification of these parties is generally lower than that of moderate/centrist parties in the same country (see Fig. S1)**. Finally, we considered whether personalization consistently amplifies messages from governing coalition or the opposition, and found no consistent pattern across countries. For example, in the United Kingdom amplification favors the governing Conservatives, while in Canada the opposition Conservative Party of Canada is more highly amplified
*Note, I am quoting from [the PDF](https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf), there was significant formatting issues copy pasting parts of this and I tried to fix it as best I can but some could have still slipped past me.*

Lots of interesting stuff to highlight here that would probably get lost in the argument that I would imagine play it. So, before I give my two bits on the US portions of this I think it is worth to consider that this shows a lot of _wide_ reaching effects of there machine learning algorithm versus there old control group in the old system.

[It was argued by some at the time that the algorithm was boosting](https://archive.ph/gDiBd) tweets based on outrage. Kind of twitters MO. conservative politicians would be more likely than to be mocked/argued with and is still based on real audience interaction over some sort of artificial favoritism out of manipulation for conservative causes. Look at the graphs on page 4 for example. Notice how the Democrats in the House are much closer to Republicans in amplification then the ones in the Senate? The Democrats have far more super stars in the house and that is where the energy is while in the Senate, you may have Bernie Sanders and Elizabeth Warren, but the majority of the rest don't have that energy, progressive or not, and a lot of them are seen by the base as old and stuffy. Most Republican Senators lack strong online followings as well for similar reasons but still certainly have tweets get attention from resist Twitter and whomever else calling them out. It does fit.

Does all the data fit that though? I don't know. I need more information on dynamics. Especially on of the non Anglosphere countries before I can make that judgement and I am not really sure this is a silver bullet where someone can argue that wipes away all of my sides grievances with the platform. Still, thanks for the link, it was a interesting read and I hope to read through it further later when I have more time.


> Experimental Setup
> Below we outline this experimental setup and its inherent limitations. We then introduce a measure of algorithmic amplification in order to quantify the degree to which di erent political groups benefit from algorithmic personalization. When Twitter introduced machine learning to personalize the Home timeline in 2016, it excluded a randomly chosen control group of 1% of all global Twitter users from the new personalized Home timeline. Individuals in this control group have never experienced personalized ranked timelines. Instead their Home timeline continues to display Tweets and Retweets from accounts they follow in reverse-chronological order. The treatment group corresponds to a sample of 4% of all other accounts who experience the personalized Home timeline. However, even individuals in the treatment group do have the option to opt out of personalization (SI Section A). The experimental setup has some inherent limitations. A first limitation stems from interaction effects between individuals in the analysis [3].
>
> In social networks, the control group can never be isolated from indirect effects of personalization as individuals in the control group encounter content shared by users in the treatment group. Therefore,
although a randomized controlled experiment, our experiment does not satisfy the well-known Stable Unit Treatment Value Assumption (SUTVA) from causal inference [14]. As a consequence, it cannot provide unbiased estimates of causal quantities of interest, such as the average treatment effect (ATE). In this study, we chose to not employ intricate causal inference machinery that is often used to approximate causal quantities [17], as these would not guarantee unbiased estimates in the complex setting of
>
> Twitter’s home timeline algorithm. Building an elaborate causal diagram of this complex system is well beyond the scope of our observational study. Instead, we present findings based on simple
comparison of measurements between the treatment and control groups. Intuitively, we expect peer effects to decrease observable differences between the control and treatment groups, thus, our reported statistics likely underestimate the true causal effects of personalization.
>
> A second limitation pertains to the fact that differences between treatment and control group were previously used by Twitter to improve the personalized ranking experience. **The treatment, i.e., the ranking experience, has therefore not remained the same over time.** Moreover, the changes to the treatment depend on the experiment itself.

This also could be issues with the data. Especially if their algorithm was changed around over time.

[@Penguin Dragneel](/forums/generals/topics/tartarus?post_id=5236145#post_5236145)
It's still a big website that can't simply be moderated 100% all the time. The way Twitter works, I am not sure it ever could. I don't think it is any pro-right wing bias among it's employees, that is for sure.

[@BlitztheDragon](/forums/generals/topics/tartarus?post_id=5236157#post_5236157)
> without explanation banned /r/rojava
I believe it was suspected that Turkish accounts had mass reported the sub but I heard that accusation second hand.
Reason: The new syntax I am still getting the hang of part 2
Edited by doloresbridge
doloresbridge
Solar Supporter - Fought against the New Lunar Republic rebellion on the side of the Solar Deity (April Fools 2023).
Non-Fungible Trixie -
Preenhub - We all know what you were up to this evening~
My Little Pony - 1992 Edition

Peace to all
[@Kiryu-Chan](/forums/generals/topics/tartarus?post_id=5236117#post_5236117)
Heard about this but didn't get the chance to read it at the time. This is a interesting read, though I am not sure this means exactly what a lot of people would say it means.
> **This effect is strongest in Canada (Liberals 43% vs Conservatives 167%) and the United Kingdom (Labour 112% vs Conservatives 176%). In both countries the Prime Ministers and members of the Government are also Members of the Parliament and are thus included in our analysis.** We therefore recomputed the amplification statistics after excluding top government officials. Our findings, shown in SI Fig. S2.,remained qualitatively similar. When studying amplification at the level of individual politicians (Fig. 1C), we find that amplification varies substantially within each political party: **while Tweets from some individual politicians are amplified up to 400%, for others amplification is below 0%, meaning they reach fewer users on ranked timelines than they do on chronological ones.**
>
> We repeated the comparison between major left-wing and right-wing parties, comparing the distribution of individual amplification values between parties. When studied at the individual level, a permutation test detected no statistically significant association between an individual’s party affiliation and their amplification. We see that comparing political parties on the basis of aggregate amplification of the entire party (Fig. 1A-B) or on the basis of individual amplification of their members (Fig. 1C) leads to seemingly different conclusions: while individual amplification is not associated with party membership, the aggregate group amplification may be different for each party. **These findings are not contradictory, considering that different politicians may reach overlapping audiences. Even if the amplification of individual politicians is uncorrelated with their political affiliation, when we consider increases to their combined reach, group-level correlations might emerge.** For a more detailed discussion please refer to SI Section 1.E.3.
>
> Our fine-grained data also allows us to evaluate whether recommender systems amplify extreme ideologies, far-left or far-right politicians, over more moderate ones [37]. **We found that in countries where far-left or far-right parties have substantial representation among elected officials (e.g. VOX in Spain, Die Linke and AfD in Germany, LFI and RN in France) the amplification of these parties is generally lower than that of moderate/centrist parties in the same country (see Fig. S1)**. Finally, we considered whether personalization consistently amplifies messages from governing coalition or the opposition, and found no consistent pattern across countries. For example, in the United Kingdom amplification favors the governing Conservatives, while in Canada the opposition Conservative Party of Canada is more highly amplified
*Note, I am quoting from [the PDF](https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf), there was significant formatting issues copy pasting parts of this and I tried to fix it as best I can but some could have still slipped past me.*

Lots of interesting stuff to highlight here that would probably get lost in the argument that I would imagine play it. So, before I give my two bits on the US portions of this I think it is worth to consider that this shows a lot of _wide_ reaching effects of there machine learning algorithm versus there old control group in the old system.

[It was argued by some at the time that the algorithm was boosting](https://archive.ph/gDiBd) tweets based on outrage. Kind of twitters MO. conservative politicians would be more likely than to be mocked/argued with and is still based on real audience interaction over some sort of artificial favoritism out of manipulation for conservative causes. Look at the graphs on page 4 for example. Notice how the Democrats in the House are much closer to Republicans in amplification then the ones in the Senate? The Democrats have far more super stars in the house and that is where the energy is while in the Senate, you may have Bernie Sanders and Elizabeth Warren, but the majority of the rest don't have that energy, progressive or not, and a lot of them are seen by the base as old and stuffy. Most Republican Senators lack strong online followings as well for similar reasons but still certainly have tweets get attention from resist Twitter and whomever else calling them out. It does fit.

Does all the data fit that though? I don't know. I need more information on dynamics. Especially on of the non Anglosphere countries before I can make that judgement and I am not really sure this is a silver bullet where someone can argue that wipes away all of my sides grievances with the platform. Still, thanks for the link, it was a interesting read and I hope to read through it further later when I have more time.


> Experimental Setup
> Below we outline this experimental setup and its inherent limitations. We then introduce a measure of algorithmic amplification in order to quantify the degree to which di erent political groups benefit from algorithmic personalization. When Twitter introduced machine learning to personalize the Home timeline in 2016, it excluded a randomly chosen control group of 1% of all global Twitter users from the new personalized Home timeline. Individuals in this control group have never experienced personalized ranked timelines. Instead their Home timeline continues to display Tweets and Retweets from accounts they follow in reverse-chronological order. The treatment group corresponds to a sample of 4% of all other accounts who experience the personalized Home timeline. However, even individuals in the treatment group do have the option to opt out of personalization (SI Section A). The experimental setup has some inherent limitations. A first limitation stems from interaction effects between individuals in the analysis [3].
>
> In social networks, the control group can never be isolated from indirect effects of personalization as individuals in the control group encounter content shared by users in the treatment group. Therefore,
although a randomized controlled experiment, our experiment does not satisfy the well-known Stable Unit Treatment Value Assumption (SUTVA) from causal inference [14]. As a consequence, it cannot provide unbiased estimates of causal quantities of interest, such as the average treatment effect (ATE). In this study, we chose to not employ intricate causal inference machinery that is often used to approximate causal quantities [17], as these would not guarantee unbiased estimates in the complex setting of
>
> Twitter’s home timeline algorithm. Building an elaborate causal diagram of this complex system is well beyond the scope of our observational study. Instead, we present findings based on simple
comparison of measurements between the treatment and control groups. Intuitively, we expect peer effects to decrease observable differences between the control and treatment groups, thus, our reported statistics likely underestimate the true causal effects of personalization.
>
> A second limitation pertains to the fact that differences between treatment and control group were previously used by Twitter to improve the personalized ranking experience. **The treatment, i.e., the ranking experience, has therefore not remained the same over time.** Moreover, the changes to the treatment depend on the experiment itself.

This also could be issues with the data. Especially if their algorithm was changed around over time.

[@Penguin Dragneel](/forums/generals/topics/tartarus?post_id=5236145#post_5236145)
It's still a big website that can't simply be moderated 100% all the time. The way Twitter works, I am not sure it ever could. I don't think it is any pro-right wing bias among it's employees, that is for sure.

[@BlitztheDragon](/forums/generals/topics/tartarus?post_id=5236157#post_5236157)
> without explanation banned /r/rojava
I believe it was suspected that Turkish accounts had mass reported the sub but I heard that accusation second hand.
Reason: The new syntax I am still getting the hang of
Edited by doloresbridge