mischbeck/CollectUSSenatorTweets
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adds alt_accounts check and removes NANs from alt_accounts. Prints accounts to output more beautifully.

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Michael Beck
2023-06-23 16:54:57 +02:00
parent 5d0c41407e
commit 1a19fd407a
1 changed files with 87 additions and 77 deletions
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148
collect.py
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@@ -1,5 +1,5 @@
# -*- coding: utf-8 -*-
'''
"""
Created on Thu Jun 8 01:08:21 2023
@author: Michael
@@ -50,7 +50,7 @@ sliced in 6 time periods (to bypass twitters limitations). It will check whether
a tweet contains any of the keywords in 'data/keywords.txt' and add an indicator
in the datafile. It will then join all slices and create 'ALL-SENATORS.csv'
which is the final output.
'''
"""
import os
import pandas as pd
@@ -62,77 +62,76 @@ import concurrent.futures
## Setup directories
# WD Michael
wd = '/home/michael/Documents/PS/Data/collectTweets/'
wd = "/home/michael/Documents/PS/Data/collectTweets/"
# WD Server
# wd = '/home/yunohost.multimedia/polsoc/Politics & Society/TweetCollection/'
# Tweet-datafile output directory
td = 'data/tweets/'
td = "data/tweets/"
# Name of file that all tweets will be written to
file_alltweets = 'ALL-SENATORS-TWEETS.csv'
file_alltweets = "ALL-SENATORS-TWEETS.csv"
path_to_tweetdfs = wd + td
## Define Timespan
# Format: %Y-%m-%dT%H:%M:%SZ (https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes)
ts_beg = '2020-01-01T00:00:00Z' # start of scraping
ts_end = '2023-01-03T00:00:00Z' # end of straping
ts_beg = "2020-01-01T00:00:00Z" # start of scraping
ts_end = "2023-01-03T00:00:00Z" # end of straping
no_slices = 24 # Number of slices / time periods.
# Maximum tweets to be scraped by snscrape. Can be left untouched.
maxTweets = 5000
# Name of logfile
logfile = 'log/log_'
logfile = "log/log_"
## Install snscrape from local git repo to make shure that it fits the used version.
# If snscrape is already installed, uncomment the following lines:
'''
"""
import subprocess
os.chdir('snscrape/')
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-e', '.'])
os.chdir(wd)
'''
"""
# Columns for tweet dataframe
tweetDFColumns = [
'id',
'user.id',
'user.username',
'user.verified',
'user.created',
'user.favouritesCount',
'user.followersCount',
'user.friendsCount',
'user.url',
'rawContent',
'renderedContent',
'cashtags',
'coordinates',
'hashtags',
'inReplyToTweetId',
'inReplyToUser',
'media',
'mentionedUsers',
'links',
'place',
'quotedTweet',
'retweetedTweet',
'sourceLabel',
'sourceUrl',
'url',
'date',
'replyCount',
'retweetCount',
'likeCount',
'quoteCount',
'conversationId',
'lang',
'source']
##
"id",
"user.id",
"user.username",
"user.verified",
"user.created",
"user.favouritesCount",
"user.followersCount",
"user.friendsCount",
"user.url",
"rawContent",
"renderedContent",
"cashtags",
"coordinates",
"hashtags",
"inReplyToTweetId",
"inReplyToUser",
"media",
"mentionedUsers",
"links",
"place",
"quotedTweet",
"retweetedTweet",
"sourceLabel",
"sourceUrl",
"url",
"date",
"replyCount",
"retweetCount",
"likeCount",
"quoteCount",
"conversationId",
"lang",
"source",
]
## Import other files
from funs.TimeSlice import *
@@ -140,45 +139,50 @@ from funs.ClearDupes import deDupe
from funs.Scrape import scrapeTweets
# create logfile & log all outputs
logfilen = logfile + datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.txt'
logfileErrors = logfile + datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_err' + '.txt'
sys.stderr = open(logfileErrors, 'w')
sys.stdout = open(logfilen, 'w')
logfilen = logfile + datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".txt"
logfileErrors = logfile + datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + "_err" + ".txt"
sys.stderr = open(logfileErrors, "w")
sys.stdout = open(logfilen, "w")
## Create List of time-period-slices
time_slices = get_Tslices(ts_beg, ts_end, no_slices)
# Print slices
print('Time-period-slices:')
print("Time-period-slices:")
for slice in time_slices:
print(slice['suffix'] + ': ' + slice['beg_time'] + ' - ' + slice['end_time'])
print('---')
print(slice["suffix"] + ": " + slice["beg_time"] + " - " + slice["end_time"])
print("---")
## Keywords
keywords = []
# Remove duplicate Keywords and save all non-duplicates to 'data/keywords.txt'
deDupe('data/keywords-raw.txt', 'data/keywords.txt')
deDupe("data/keywords-raw.txt", "data/keywords.txt")
# Read the keywords from a file
with open('data/keywords.txt', 'r') as file:
with open("data/keywords.txt", "r") as file:
lines = file.readlines()
for line in lines:
keyword = line.strip() # Remove the newline character
keywords.append(keyword)
print('---')
print("---")
## Senator Accounts
# Get accounts & alt-accounts from Senators-Datafile
accounts = pd.read_csv('data/senators-raw.csv')['twitter_handle'].tolist()
alt_accounts = pd.read_csv('data/senators-raw.csv')['alt_handle'].tolist()
print('Accounts to be scraped:')
print(accounts)
print(alt_accounts)
print('---')
accounts = pd.read_csv("data/senators-raw.csv")["twitter_handle"].tolist()
alt_accounts = pd.read_csv("data/senators-raw.csv")["alt_handle"].tolist()
alt_accounts = [x for x in alt_accounts if str(x) != 'nan'] # remove empty alt_accounts fields
accounts.append(alt_accounts)
# Print accounts to be scraped
print("Accounts to be scraped:")
for i, acc in enumerate(accounts): # print 5 accounts per line
print(f"{acc:^17}", end = "") # twitter handle max length = 15 chars
if i % 5 == 4:
print "\n"
print("\n---")
## Scraping
timeStartScrape = datetime.now()
print("Starting scraping at:")
print(timeStartScrape.strftime('%Y-%m-%d_%H-%M-%S'))
print('---')
print(timeStartScrape.strftime("%Y-%m-%d_%H-%M-%S"))
print("---")
# Iterate over each Twitter account using multiprocessing
with concurrent.futures.ThreadPoolExecutor() as executor:
@@ -200,18 +204,22 @@ with concurrent.futures.ThreadPoolExecutor() as executor:
timeEndScrape = datetime.now()
print("---")
print("End of scraping at:")
print(timeEndScrape.strftime('%Y-%m-%d_%H-%M-%S'))
print(timeEndScrape.strftime("%Y-%m-%d_%H-%M-%S"))
## Merge CSV-Files to file_alltweets
## Merge CSV-Files to file_alltweets.
# fastest way is to save the slices seperately and then add every file to the output instead of using pandas or anything else.
os.chdir(path_to_tweetdfs)
tweetfiles = glob.glob('*.{}'.format('csv')) # get list of all csv files in folder
# At first check, whether all slices are present.
tweetfiles = glob.glob("*.csv") # get list of all csv files in folder - before: "*.{}".format("csv")
for handle
for tweetfile in tweetfiles:
# check if file_alltweets (previously scraped tweets that have been merged into one file) exists, if it exists, remove from list to not include it in the following merge
if file_alltweets in tweetfiles:
tweetfiles.remove(file_alltweets)
# Go through all csv files and merge them into file_alltweets
with open(file_alltweets,"wb") as fout:
with open(file_alltweets, "wb") as fout:
# first file (because of the header):
with open(tweetfiles[0], "rb") as f:
fout.write(f.read())
@@ -225,12 +233,14 @@ os.chdir(wd)
timeEndMerge = datetime.now()
print("---")
print("End of scraping at:")
print(timeEndMerge.strftime('%Y-%m-%d_%H-%M-%S'))
print(timeEndMerge.strftime("%Y-%m-%d_%H-%M-%S"))
print("---")
tThours, tTminutes, tTseconds = convertTime(timeEndMerge - timeStartScrape)
tShours, tSminutes, tSseconds = convertTime(timeEndScrape - timeStartScrape)
tMhours, tMminutes, tMseconds = convertTime(timeEndMerge - timeEndScrape)
print(f"Total execution time: {tThours} hours, {tTminutes} minutes and {tTseconds} seconds")
print(
f"Total execution time: {tThours} hours, {tTminutes} minutes and {tTseconds} seconds"
)
print(f"Scraping time: {tShours} hours, {tSminutes} minutes and {tSseconds} seconds")
print(f"Time merging: {tMhours} hours, {tMminutes} minutes and {tMseconds} seconds")