igorbrigadir

{"conversation_id": "1365618709222600705", "text": "RT @tradehunter305: While checking into my hotel, I seen this light skin dude behind me. We ended up on the same floor, I asked him if he w\u2026", "reply_settings": "everyone", "possibly_sensitive": false, "source": "Twitter for iPhone", "public_metrics": {"retweet_count": 1256, "reply_count": 0, "like_count": 0, "quote_count": 0}, "lang": "en", "referenced_tweets": [{"type": "retweeted", "id": "1353880904188506112", "entities": {"urls": [{"start": 274, "end": 297, "url": "https://t.co/9CovM6hF9P", "expanded_url": "https://twitter.com/tradehunter305/status/1353880904188506112/video/1", "display_url": "pic.twitter.com/9CovM6hF9P"}], "hashtags": [{"start": 228, "end": 243, "tag": "tradehunter305"}, {"start": 244, "end": 256, "tag": "miamifreaks"}], "annotations": [{"start": 134, "end": 141, "probability": 0.5495, "type": "Person", "normalized_text": "Ofocurse"}], "mentions": [{"start": 178, "end": 191, "username": "_BDhandsome1"}, {"start": 257, "end": 273, "

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

igorbrigadir

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

igorbrigadir

{
  "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": [

igorbrigadir

#!/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

igorbrigadir

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

igorbrigadir

{"text": "RT @qeensdale: But it's #Boris the fool who is to blame for the latest troubles in Northern Ireland his bloody #Brexit deal.\nBest if he fal\u2026", "lang": "en", "possibly_sensitive": false, "conversation_id": "1384644560446447617", "source": "Twitter for Android", "created_at": "2021-04-20T23:06:03.000Z", "referenced_tweets": [{"type": "retweeted", "id": "1384551429306781700", "text": "But it's #Boris the fool who is to blame for the latest troubles in Northern Ireland his bloody #Brexit deal.\nBest if he falls on his sword. \n\nBBC News - Brexit: Boris Johnson promises to end GB-NI trade barriers\nhttps://t.co/uTbvahkvcz", "lang": "en", "entities": {"urls": [{"start": 213, "end": 236, "url": "https://t.co/uTbvahkvcz", "expanded_url": "https://www.bbc.co.uk/news/uk-northern-ireland-56777985", "display_url": "bbc.co.uk/news/uk-northe\u2026"}], "hashtags": [{"start": 9, "end": 15, "tag": "Boris"}, {"start": 96, "end": 103, "tag": "Brexit"}], "annotations": [{"start": 68, "end": 83, "probability": 0.

igorbrigadir

"id": "1377313827226783745", "created_at": "2021-04-01T15:54:26.000Z", "text": "RT @ArborRidge1: We have a Wednesday Golden Egg Winner!! Even the dogs enjoy hunting for our golden egg!! Who will be the lucky person today!! Hint- the staff is watching it closely from the office! @ArborRidgeAptHomes https://t.co/VpdUTEMCW3", "reply_settings": "everyone", "possibly_sensitive": false, "entities": {"urls": [{"start": 212, "end": 235, "url": "https://t.co/VpdUTEMCW3", "expanded_url": "https://www.instagram.com/p/CNIQwHSjoWT/?igshid=1b0vhmepuh4ps", "display_url": "instagram.com/p/CNIQwHSjoWT/\u2026", "images": [{"url": "https://pbs.twimg.com/news_img/1377313827226783745/5jH4O4jW?format=jpg&name=orig", "width": 1080, "height": 1080}, {"url": "https://pbs.twimg.com/news_img/1377313827226783745/5jH4O4jW?format=jpg&name=150x150", "width": 150, "height": 150}], "status": 200, "title": "Arbor Ridge on West Friendly on Instagram: \u201cWe have a Wednesday Golden Egg Winner!! Even the dogs enjoy hunting for our golden egg!!

igorbrigadir

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

igorbrigadir

How I failed machine learning in medical imaging - shortcomings and recommendations

By Gaël Varoquaux, Veronika Cheplygina

Abstract:
Medical imaging is an important research field with many opportunities for improving patients' health. However, there are a number of challenges that are slowing down the progress of the field as a whole, such optimizing for publication. In this paper we reviewed several problems related to choosing datasets, methods, evaluation metrics, and publication strategies. With a review of literature and our own analysis, we show that at every step, potential biases can creep in. On a positive note, we also see that initiatives to counteract these problems are already being started. Finally we provide a broad range of recommendations on how to further these address problems in the future. For reproducibility, data and code for our analyses are available on https://github.com/GaelVaroquaux/ml_med_imaging_failures

Introduction: