Because major corporations, Twitter included, tend to be ENORMOUSLY conservative. The only thing they care about is money and most of them are massive supporters of conservative politicians and parties.

The left wing of the Democratic Party has been reeling since Sen. Bernie Sanders’ collapse in the presidential primary. On Tuesday, it could finally mount a comeback.

Primaries in Kentucky and New York offer liberals significant opportunities to grow their influence in the party and chip away at the establishment’s grip.

By all accounts, progressives have the momentum in Kentucky, where Charles Booker has seized the energy around the protests over racial injustice and police brutality to make a once-unthinkable charge at party favorite Amy McGrath in the race to face Senate Majority Leader Mitch McConnell in November. McGrath, a powerhouse fundraiser endorsed by the national party apparatus, has said she would work with President Donald Trump and doesn’t have meaningful relationships with Kentucky Democratic powerbrokers, leaving her vulnerable for a primary upset.

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

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.

without explanation banned /r/rojava

Not The Party of Trump Greene & Gaetz !