| """ |
| Removes near-duplicates and tweets from top pct. of users. |
| |
| original -> https://github.com/cardiffnlp/timelms/blob/main/scripts/preprocess.py |
| |
| optional arguments: |
| -h, --help show this help message and exit |
| --src SRC Path to set of input tweets (.jl). |
| --out OUT Path to output from preprocessing (.jl). |
| --blacklist_pct BLACKLIST_PCT |
| Percent of most frequent users to ignore. |
| Example: |
| python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2018.raw.jl --out dataset/tweets/2018.jsonline |
| python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2019.raw.jl --out dataset/tweets/2019.jsonline |
| python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2020.raw.jl --out dataset/tweets/2020.jsonline |
| python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2021.raw.jl --out dataset/tweets/2021.jsonline |
| python timelm_preprocessor.py --src /mnt/share/daniel_tweet_dump/2022.raw.jl --out dataset/tweets/2022.jsonline |
| """ |
|
|
| import argparse |
| import json |
| import logging |
| import os |
| import string |
| import re |
| from collections import Counter |
|
|
| from datasketch import MinHash, LeanMinHash |
| import xxhash |
|
|
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S') |
| re_url = re.compile(r'https?:\/\/[\w\.\/\?\=\d&#%_:/-]+') |
| re_user = re.compile(r'@\w+') |
| with open('verified_users.v091122.txt') as f: |
| verified_users = set([f"@{i}" for i in f.read().split('\n') if len(i)]) |
|
|
|
|
| def clean_text(text): |
| text = text.replace('\n', ' ').replace('\r', ' ').replace('\t', ' ') |
| text = re_url.sub('{URL}', text) |
| users = re_user.findall(text) |
| for user in users: |
| if user not in verified_users: |
| text = text.replace(user, '@user') |
| return text |
|
|
|
|
| def hash_tweet(target_tweet, num_perm=16): |
| def normalize_text(text): |
| text = text.translate(str.maketrans('', '', string.punctuation)) |
| text = text.lower() |
| return text |
|
|
| def minhash(seq): |
| |
| m = MinHash(num_perm=num_perm, hashfunc=xxhash.xxh64_intdigest) |
| for s in seq: |
| m.update(s.encode('utf8')) |
| return LeanMinHash(m) |
|
|
| tokens = normalize_text(target_tweet['text']).split() |
| return minhash(tokens) |
|
|
|
|
| if __name__ == '__main__': |
|
|
| parser = argparse.ArgumentParser(description='Removes near-duplicates and tweets from top pct. of users.') |
| parser.add_argument('--src', type=str, required=True, help='Path to set of input tweets (.jl).') |
| parser.add_argument('--out', type=str, required=True, help='Path to output from preprocessing (.jl).') |
| parser.add_argument('--blacklist_pct', type=float, required=False, default=0.01, |
| help='Percent of most frequent users to ignore.') |
| args = parser.parse_args() |
| os.makedirs(os.path.dirname(args.out), exist_ok=True) |
|
|
| logging.info('1st pass - Collecting username counts ...') |
| n_input_tweets = 0 |
| user_counter = Counter() |
| with open(args.src) as in_tweets_f: |
| for idx, jl_str in enumerate(in_tweets_f): |
| if idx % 1e6 == 0: |
| logging.info('1st pass - at idx %d' % idx) |
| tweet = json.loads(jl_str) |
| user_counter[tweet['username']] += 1 |
| n_input_tweets += 1 |
|
|
| logging.info('1st pass - Completed, found %d tweets' % n_input_tweets) |
| logging.info('1st pass - Found %d users' % len(user_counter.keys())) |
|
|
| top_users = [user for user, _ in user_counter.most_common()] |
| n_blacklisted_users = int(len(top_users) * args.blacklist_pct) |
| blacklisted_users = set(top_users[:n_blacklisted_users]) |
| n_users = len(user_counter.keys()) |
| pct_blacklisted_users = round((n_blacklisted_users / n_users) * 100, 2) |
| n_blacklisted_tweets = sum([user_counter[u] for u in blacklisted_users]) |
| pct_blacklisted_tweets = round((n_blacklisted_tweets / sum(user_counter.values())) * 100, 2) |
| logging.info( |
| f"1st pass - Blacklisted {len(blacklisted_users)} users ({pct_blacklisted_users}%), " |
| f"ignoring {n_blacklisted_tweets} tweets ({pct_blacklisted_tweets}%)" |
| ) |
|
|
| logging.info('2nd pass - Hashing and writing valid tweets ...') |
| written_hashes = set() |
| n_written = 0 |
| n_ignored_by_user = 0 |
| n_ignored_by_hash = 0 |
| with open(args.src) as in_tweets_f: |
| with open(args.out, 'w') as out_tweets_f: |
| for idx, jl_str in enumerate(in_tweets_f): |
| if idx % 1e5 == 0: |
| logging.info('2nd pass - at idx %d' % idx) |
| tweet = json.loads(jl_str) |
| tweet['text'] = clean_text(tweet['text']) |
| tweet_hash = hash_tweet(tweet) |
| if tweet['username'] in blacklisted_users: |
| n_ignored_by_user += 1 |
| elif tweet_hash in written_hashes: |
| n_ignored_by_hash += 1 |
| else: |
| out_tweets_f.write(json.dumps(tweet) + '\n') |
| n_written += 1 |
| written_hashes.add(tweet_hash) |
| logging.info(f"2nd pass - Completed, wrote {n_written} tweets.") |
| if n_ignored_by_user > 0: |
| logging.info(f"\tignored {n_ignored_by_user} by user blacklist") |
| if n_ignored_by_hash > 0: |
| logging.info(f"\tignored {n_ignored_by_hash} by hash collision") |
| logging.info("Done") |
|
|
