Igor Brigadir igorbrigadir

C:\Users\USER>curl -v -H "Authorization: Bearer
AAA...zzz"
"
https://api.twitter.com/2/tweets/search/recent?expansions=author_id%2Cin_reply_to_user_id%2Creferenced_tweets.id%2Creferenced_tweets.id.author_id%2Centities.mentions.username%2Cattachments.poll_ids%2Cattachments.media_keys%2Cgeo.place_id&user.fields=created_at%2Cdescription%2Centities%2Cid%2Clocation%2Cname%2Cpinned_tweet_id%2Cprofile_image_url%2Cprotected%2Cpublic_metrics%2Curl%2Cusername%2Cverified%2Cwithheld&tweet.fields=attachments%2Cauthor_id%2Ccontext_annotations%2Cconversation_id%2Ccreated_at%2Centities%2Cgeo%2Cid%2Cin_reply_to_user_id%2Clang%2Cpublic_metrics%2Ctext%2Cpossibly_sensitive%2Creferenced_tweets%2Creply_settings%2Csource%2Cwithheld&media.fields=duration_ms%2Cheight%2Cmedia_key%2Cpreview_image_url%2Ctype%2Curl%2Cwidth%2Cpublic_metrics&poll.fields=duration_minutes%2Cend_datetime%2Cid%2Coptions%2Cvoting_status&place.fields=contained_within%2Ccountry%2Ccountry_code%2Cfull_name%2Cgeo%2Cid%2Cname%2Cplace_type&max_results=10&query=end

Twitter Undocumented Bookmark API (WIP)

I found out the endpoints for bookmark with Chrome Developer Tools: GET timeline/bookmark, POST bookmark/entries/remove, POST bookmark/entries/remove. The rate limits below are values returned by an official endpoint GET application/rate_limit_status.

This document is still a work in progress because I got stuck in GET timeline/bookmark. Please let me know if you find how to use it.

Notes

It is necessary that x-csrf-token in a request header and ct0 in a cookie are the same value. Twitter uses them to avoid CSRF attacks. I recommend that you extract the values from your browsers.
All of the endpoints requires OAuth2 Authorizations. Note that they refuse OAuth2 Bearer tokens obtained from POST oauth2/token.
You can easily reach the rate limit and get HTTP 429 Error (too many requests), so you should be careful about how many requests you send.

	The Values Encoded in Machine Learning Research

	by Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, Michelle Bao

	Abstract:

	Machine learning (ML) currently exerts an outsized influence on the world, increasingly affecting communities and institutional practices. It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing. In this paper, we present a rigorous examination of the values of the field by quantitatively and qualitatively analyzing 100 highly cited ML papers published at premier ML conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: how they justify their choice of project, which aspects they uplift, their consideration of potential negative consequences, and their institutional affiliations and funding sources. We find that societal needs are typically very loosely connected to the choice of project, if mentioned a

	[
	"francemusique.fr",
	"sumikai.com",
	"yadokari.net",
	"epoca.globo.com",
	"airedesantafe.com.ar",
	"nemgym.com",
	"numerama.com",
	"analgeria.com",
	"bobiannews.com",

	#!/bin/bash
	# run this like ./mutuals.sh screen_name

	# Get all the accounts you follow
	twarc2 followers "$1" "$1_followers.jsonl"

	# Get all the accounts that follow you
	twarc2 following "$1" "$1_following.jsonl"

	# Get mutuals ids

	{
	"primary_text": {
	"text": "Follow your favorite Topics",
	"entities": []
	},
	"secondary_text": {
	"text": "You’ll see top Tweets about them in your timeline. Don’t see your favorite Topics yet? New Topics are added every week.",
	"entities": []
	},
	"top_category_ids": [

	{
	"data": [
	{
	"entities": {
	"annotations": [
	{
	"start": 33,
	"end": 39,
	"probability": 0.9758,
	"type": "Place",