We analyzed millions of Tweets from April 1 to August 15, 2020, from accounts operated by elected officials in seven countries. Are some news outlets amplified more by algorithms than others? Does news media algorithmic amplification favor one side of the political spectrum more than the other?.Are some types of political groups algorithmically amplified more than others? Are these trends consistent across countries?.How much algorithmic amplification does political content from elected officials receive in Twitter’s algorithmically ranked Home timeline versus in the reverse chronological timeline? Does this amplification vary across political parties or within a political party?.In our study, we examined algorithmic amplification of political content in the Home timeline by asking the following questions: We hope our findings will contribute to an evidence-based discussion of the role these algorithms play in shaping political content consumption on the internet. The purpose of this study was to better understand the amplification of elected officials’ political content on our algorithmically ranked Home timeline versus the reverse chronological Home timeline. As a result, what an individual sees on their Home timeline is a function of how they interact with the algorithmic system, as well as how the system is designed.
An algorithmic Home timeline displays a stream of Tweets from accounts you have chosen to follow on Twitter, as well as recommendations of other content we think you might be interested in based on accounts you interact with frequently, Tweets you engage with, and more. Since 2016, people on Twitter have been able to choose between viewing algorithmically ordered Tweets first in the Home timeline or viewing the most recent Tweets in reverse chronological order. Since Tweets from elected officials cover just a small portion of political content on the platform, we also studied whether our recommendation algorithms amplify political content from news outlets. The first part of the study examines Tweets from elected officials* in seven countries (Canada, France, Germany, Japan, Spain, the United Kingdom, and the United States). Today, we’re publishing learnings from another study: an in-depth analysis of whether our recommendation algorithms amplify political content.
We recently shared the findings of our analysis of bias in our image cropping algorithm and how they informed changes in our product. This effort is part of our ongoing work to look at algorithms across a range of topics. As we shared earlier this year, we believe it’s critical to study the effects of machine learning (ML) on the public conversation and share our findings publicly.
0 kommentar(er)
