205 lines
6.9 KiB
Python
205 lines
6.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Jun 6 11:40:07 2023
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@author: michael
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"""
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import os
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import tweepy
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import pandas as pd
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import numpy as np
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import glob
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import time
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## Setup directories
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# WD Michael
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wd = "/home/michael/Documents/PS/Data/collectTweets/"
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# WD Server
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# wd = "/home/yunohost.multimedia/polsoc/Politics & Society/TweetCollection"
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# WD Josie
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# wd = "/home/michael/Documents/PS/Data/"
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# WD Sam
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# wd = "/home/michael/Documents/PS/Data/"
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# Tweet-datafile directory
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td = "data/tweets/"
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os.chdir(wd)
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## Setup Api-connection
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bearer_token = "AAAAAAAAAAAAAAAAAAAAAMVDlQEAAAAAal9f5uZrM12CVPA4f4jr4mGH5Oc%3DuTg1Vd0YKYMwraA7ibX6LiGyd337OXkm3JwudEX7vatruswmoc"
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client = tweepy.Client(bearer_token, return_type = dict, wait_on_rate_limit = True)
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# Define time period of interest
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# Define time periods of interest
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time_slices = [
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{
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"start_time": "2020-01-01T00:00:00Z",
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"end_time": "2020-06-01T00:00:00Z",
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"suffix": "-slice1"
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},
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{
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"start_time": "2020-06-01T00:00:01Z",
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"end_time": "2021-01-01T00:00:00Z",
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"suffix": "-slice2"
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},
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{
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"start_time": "2021-01-01T00:00:01Z",
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"end_time": "2021-06-01T00:00:00Z",
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"suffix": "-slice3"
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},
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{
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"start_time": "2021-06-01T00:00:01Z",
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"end_time": "2023-01-03T00:00:00Z",
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"suffix": "-slice4"
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}
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]
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# gather keywords @chenTrackingSocialMedia2020
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# line80 ff: lamsalCoronavirusCOVID19Tweets2020
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# Initialize the keywords list
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keywords = []
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# Read the keywords from a file
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with open("data/keywords.txt", "r") as file:
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lines = file.readlines()
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for line in lines:
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keyword = line.strip() # Remove the newline character
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keywords.append(keyword)
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tweet_fields = [
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"id",
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"text",
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"attachments",
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"author_id",
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"context_annotations",
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"conversation_id",
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"created_at",
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"entities",
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"geo",
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"lang",
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"possibly_sensitive",
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"public_metrics",
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"referenced_tweets",
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"reply_settings",
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"source",
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"withheld",
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]
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# Get accounts & alt-accounts from Senators-Datafile
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accounts = pd.read_csv("data/senators-raw.csv")["twitter_handle"].tolist()
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alt_accounts = pd.read_csv("data/senators-raw.csv")["alt_handle"].tolist()
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print(accounts)
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print(alt_accounts)
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# Iterate over each Twitter account
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for handle in accounts:
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for slice_data in time_slices:
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start_time = slice_data["start_time"]
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end_time = slice_data["end_time"]
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suffix = slice_data["suffix"]
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query = "from:" + handle + " -is:retweet"
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tweetlist = []
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# Fetch tweets using Twitter API pagination
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try:
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for tweet in tweepy.Paginator(client.search_all_tweets,
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query=query,
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tweet_fields=tweet_fields,
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start_time=start_time,
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end_time=end_time,
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max_results=100).flatten(50):
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tweetlist.append(tweet)
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msg = f"trying to fetch tweets for {handle}{suffix} fetched"
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print(msg)
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except tweepy.error.TweepError as ex:
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timestamp = datetime.now().timestamp()
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msg = f"{timestamp} - raised exception {handle}{suffix}: " + str(ex) + " - sleeping..."
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print(msg)
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time.sleep(1)
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try:
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for tweet in tweepy.Paginator(client.search_all_tweets,
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query=query,
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tweet_fields=tweet_fields,
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start_time=start_time,
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end_time=end_time,
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max_results=100).flatten(50):
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tweetlist.append(tweet)
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msg = f"2nd try: tweets for {handle}{suffix} successfully fetched"
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print(msg)
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except tweepy.error.TweepError as ex:
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timestamp = datetime.now().timestamp()
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msg = f"{timestamp} - raised exception AGAIN {handle}{suffix}: " + str(ex) + " - sleeping..."
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print(msg)
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time.sleep(1)
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all_tweets = pd.DataFrame(tweetlist)
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# Check if no tweets fetched for the current time slice
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if len(tweetlist) == 0:
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msg = f"return empty in {handle}{suffix} - from {start_time} to {end_time}"
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print(msg)
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continue
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all_tweets['handle'] = handle
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# Extract referenced_tweet info from column
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all_tweets['referenced_tweet_type'] = None
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all_tweets['referenced_tweet_id'] = None
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if 'referenced_tweets' in all_tweets.columns:
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for index, row in all_tweets.iterrows():
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referenced_tweets = row['referenced_tweets']
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if isinstance(referenced_tweets, list) and len(referenced_tweets) > 0:
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referenced_tweet = referenced_tweets[0]
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referenced_tweet_type = referenced_tweet['type']
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referenced_tweet_id = referenced_tweet['id']
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all_tweets.at[index, 'referenced_tweet_type'] = referenced_tweet_type
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all_tweets.at[index, 'referenced_tweet_id'] = referenced_tweet_id
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# Check if tweet contains keyword
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if 'text' in all_tweets.columns:
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all_tweets['contains_keyword'] = (all_tweets['text'].str.findall('|'.join(keywords))
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.str.join(',')
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.replace('', 'none'))
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# Save two versions of the dataset, one with all fields and one without dict fields
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csv_path = f"data/tweets/{handle}{suffix}.csv"
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csv_path2 = f"data/tweets/{handle}{suffix}-LONG.csv"
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all_tweets.to_csv(csv_path2)
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all_tweets = all_tweets.drop(["context_annotations", "entities", "referenced_tweets"], axis=1)
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all_tweets.to_csv(csv_path)
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time.sleep(1) # sleep 1 second to not get over api limit
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# Merge CSV-Files
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# (it would also have been a possibility to build a dataframe with all senators' tweets but i found the other way around more useful)
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path_to_tweetdfs = wd + td
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os.chdir(path_to_tweetdfs)
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tweetfiles = glob.glob('*.{}'.format("csv"))
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print(tweetfiles)
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# save merged csv as two files
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df_all_senators = pd.DataFrame()
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df_all_senators_long = pd.DataFrame()
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for file in tweetfiles:
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if "LONG" in file:
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df = pd.read_csv(file)
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df_all_senators_long = pd.concat([df, df_all_senators_long])
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else:
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df = pd.read_csv(file)
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df_all_senators = pd.concat([df, df_all_senators])
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csv_path = td + "ALL-SENATORS.csv"
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csv_path2 = td + "ALL-SENATORS-LONG-LONG.csv"
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df_all_senators.to_csv(csv_path)
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df_all_senators_long.to_csv(csv_path2)
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os.chdir(wd) |