Am analizat folowerii lui Bufnici de pe twitter and found this

Pornind de la urmatoarele afirmatii

* [https://twitter.com/gbuhnici/status/1548726211366494210](https://twitter.com/gbuhnici/status/1548726211366494210)
* [https://twitter.com/gbuhnici/status/1548804197541449728](https://twitter.com/gbuhnici/status/1548804197541449728)
* [https://twitter.com/gbuhnici/status/1548680342118219778](https://twitter.com/gbuhnici/status/1548680342118219778) m-am gandit sa vedem cam cati din followerii lui gbuhnici s-ar incadra si ei in acesta categorie.

Pentru asta, folosind API-ul de la Twitter am accesat urmatoarele informatii publice:

1. Lista de followers
2. Pentru fiecare follower informatii despre contul respectiv cum ar fi cand a fost creat, daca are sau nu poza de profil, daca a dat cel putin un like la vreun tweet si daca are cel putin un follower. ​ Mai intai am sa afisez datele obtinute si apoi si scripturile care m-au ajutat sa obtin aceste date.

**Top 15 date la care au fost create cele mai multe conturi care i-au dat follow:**

|Data|Numar de conturi create la data respectiva|
|:-|:-|
|2022-02-24|2811|
|2022-02-25|2511|
|2022-02-26|3388|
|2022-02-27|4446|
|2022-02-28|3380|
|2022-03-01|2212|
|2022-03-02|2045|
|2022-03-03|1460|
|2022-03-04|1215|
|2022-03-05|1186|
|2022-03-06|1096|
|2022-04-26|1838|
|2022-04-27|1097|
|2022-04-28|922|
|2022-04-29|950|

Se poate observa cum in anumite zile succesive (24, 25, 26, 27, 28, etc.) apar conturi noi cu miile/sutele, care mai apoi ii dau follow. Sa fie o metoda de a-si “cumpara/face” followeri intr-un mod artificial ca sa ajunga la un numar mai mare per total? Sa fie persoane reale? Nu stiu, va las pe voi sa va dati cu parerea.

**Followeri care nu au poza la profil** Din cei 638081 de followeri (la data la care a rulat scriptul) **287865** au default\_profile\_image=True lucru care indica (conform [https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user](https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user)) ca followerii respectivi nu au poza la profil (nu au deloc, deci nici nu vorbim daca ar fi reala sau nu). ​

**Followeri care nu au dat nici macar un like la vreun tweet** Din cei 638081 de followeri (la data la care a rulat scriptul) **384357** au favourites\_count=0 lucru care indica (conform [https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user](https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user)) ca followerii respectivi nu au dat nici macar un singur like la vreun oricare tweet de la oricare user. ​

**Followeri care la randul lor nu au nici macar un follower** Din cei 638081 de followeri (la data la care a rulat scriptul) **254701** au followers\_count=0 lucru care indica (conform [https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user](https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user)) ca followerii respectivi nu au dat nici macar un singur follower. ​

**Followeri care nu au postat nici macar un tweet/retweet** Din cei 638081 de followeri (la data la care a rulat scriptul) **349573** au statuses\_count=0 lucru care indica (conform [https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user](https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/user)) ca followerii respectivi nu au postat nici macar un tweet sau retweet. ​ Putem trage vreo concluzie daca ne uitam la toate informatiile si numerele de mai sus? Va las pe voi sa decideti. ​

Mai jos, scripturile cu care puteti si voi obtine aceleasi rezultate:

import os
import httpx
import time
​
from dotenv import load_dotenv
​
load_dotenv()
​
TWITTER_SCREEN_NAME = os.getenv(“TWITTER_SCREEN_NAME”)
TWITTER_BEARER_TOKEN = os.getenv(“TWITTER_BEARER_TOKEN”)
​
cursor = “-1″
while True:
print(f”Cursor => {cursor}”)
​
res = httpx.get(
“https://api.twitter.com/1.1/followers/ids.json”,
params={“cursor”: cursor, “screen_name”: TWITTER_SCREEN_NAME, “count”: 5000, “stringify_ids”: “true”},
headers={“authorization”: f”Bearer {TWITTER_BEARER_TOKEN}”},
)
​
data = res.json()
cursor = data.get(“next_cursor_str”)
​
with open(“followers.txt”, “a”) as f:
ids = data.get(“ids”)
​
if len(ids) < 1:
break
​
for i in ids:
f.write(f”{i}\n”)
​
# Rate limited? Yes
# Requests / 15-min window? 15
time.sleep(60)
​
​
​
&nbsp;
​
​
​
import os
import httpx
import csv
import time
​
from dotenv import load_dotenv
​
load_dotenv()
​
TWITTER_BEARER_TOKEN = os.getenv(“TWITTER_BEARER_TOKEN”)
​
followers: list[str] = []
with open(“followers.txt”, “r”) as f:
for line in f.readlines():
follower_id = line.strip()
if len(follower_id) > 0:
followers.append(follower_id)
​
# id_str – The string representation of the unique identifier for this User.
# screen_name – The screen name, handle, or alias that this user identifies themselves with. screen_names are unique but subject to change.
# created_at – The UTC datetime that the user account was created on Twitter.
# followers_count – The number of followers this account currently has.
# friends_count – The number of users this account is following.
# favourites_count – The number of Tweets this user has liked in the account’s lifetime.
# verified – When true, indicates that the user has a verified account.
# statuses_count – The number of Tweets (including retweets) issued by the user.
# default_profile – When true, indicates that the user has not altered the theme or background of their user profile.
# default_profile_image – When true, indicates that the user has not uploaded their own profile image and a default image is used instead.
​
with open(“data.csv”, “a”) as f:
csv_w = csv.writer(f)
csv_w.writerow(
[
“id_str”,
“screen_name”,
“created_at”,
“followers_count”,
“friends_count”,
“favourites_count”,
“verified”,
“statuses_count”,
“default_profile”,
“default_profile_image”,
]
)
​
followers_chunks = [followers[i : i + 100] for i in range(0, len(followers), 100)]
for followers_idx, followers_chunk in enumerate(followers_chunks):
print(f”Chunk {followers_idx + 1} of {len(followers_chunks)}…”)
​
res = httpx.get(
“https://api.twitter.com/1.1/users/lookup.json”,
params={“user_id”: “,”.join(followers_chunk)},
headers={“authorization”: f”Bearer {TWITTER_BEARER_TOKEN}”},
)
​
users = res.json()
for u in users:
try:
csv_w.writerow(
[
u.get(“id_str”),
u.get(“screen_name”),
u.get(“created_at”),
u.get(“followers_count”),
u.get(“friends_count”),
u.get(“favourites_count”),
u.get(“verified”),
u.get(“statuses_count”),
u.get(“default_profile”),
u.get(“default_profile_image”),
]
)
except Exception as ex:
print(res.text)
raise ex
​
# Rate limited? Yes
# Requests / 15-min window? 300
time.sleep(3)
​
​
​
​
&nbsp;
​
​
​
​
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# liniile de cod de mai jos trebuie rulate in Jupyter Notebook
import pandas as pd
​
data = pd.read_csv(“data_uniq.csv”)
data[“created_at_days”] = pd.to_datetime(data[“created_at”]).dt.strftime(“%Y-%m-%d”)
​
data.created_at_days.value_counts().head(15).sort_index()
data.default_profile_image.value_counts()
data[data.favourites_count == 0].favourites_count.value_counts()
data[data.followers_count == 0].followers_count.value_counts()
data[data.statuses_count == 0].statuses_count.value_counts()