Datasets:
Sub-tasks:
multi-class-classification
Languages:
English
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
reddit
Tags:
natural-language-understanding
ideology classification
text classification
natural language processing
License:
| """This script is used to get many posts from the desired subreddit(s)""" | |
| import requests | |
| import time | |
| import random | |
| import copy | |
| import pandas as pd | |
| """This script is used to get many posts from the desired subreddit(s)""" | |
| subreddit_list = [ | |
| "theredpillrebooted", | |
| "RedPillWomen", | |
| "Feminism", | |
| "marriedredpill", | |
| "TheBluePill", | |
| "PurplePillDebate", | |
| "RedPillWives", | |
| "askMRP", | |
| "ForeverAloneWomen", | |
| ] | |
| url_template = "https://www.reddit.com/r/{}/.json?t=all{}" | |
| headers = {"User-Agent": "Testing Bot Gundam Wing"} | |
| params = "" | |
| str_log = [] | |
| original_counter = 10000 | |
| counter = original_counter | |
| post_list = [] | |
| for subreddit in subreddit_list: | |
| while counter > 0: | |
| print(f"Getting posts with params: {params}") | |
| print("\n\n\n\n") | |
| url = url_template.format(subreddit, params) | |
| response = requests.get(url, headers=headers) | |
| if response.ok: | |
| data = response.json() | |
| posts = data["data"]["children"] | |
| print(f"Got {len(posts)} posts") | |
| for post in posts: | |
| pdata = post["data"] | |
| post_id = pdata["id"] | |
| title = pdata["title"] | |
| text = pdata.get("selftext") | |
| score = pdata["score"] | |
| author = pdata["author"] | |
| date = pdata["created_utc"] | |
| url = pdata.get("url_overridden_by_dest") | |
| print(f"{post_id}: {title} - {url}") | |
| # prints for debugging | |
| # print("Keys are ", pdata.keys()) | |
| # post_list.append( | |
| # { | |
| # "id": post_id, | |
| # "title": title, | |
| # "text": text, | |
| # "url": url, | |
| # "score": score, | |
| # "author": author, | |
| # "date": date, | |
| # "pdata": pdata, | |
| # } | |
| # ) | |
| post_list.append( | |
| [subreddit, post_id, title, text, url, score, author, date, pdata] | |
| ) | |
| print(f"Got {len(posts)} posts") | |
| try: | |
| params = "&after=" + data["data"]["after"] | |
| except: | |
| print( | |
| "No more posts, broke on ", subreddit, "with counter at ", counter | |
| ) | |
| # write this log to a txt file | |
| str_log.append( | |
| "No more posts, broke on " | |
| + subreddit | |
| + "with counter at " | |
| + str(counter) | |
| ) | |
| break | |
| counter -= 1 | |
| time.sleep(random.randint(1, 45)) | |
| else: | |
| print(f"Error: {response.status_code}") | |
| counter = original_counter | |
| params = "" | |
| # make a copy of the list | |
| post_list_copy = copy.deepcopy(post_list) | |
| # save the list to a csv file, as a backup | |
| # to avoid running the script again | |
| df = pd.DataFrame(post_list_copy) | |
| df.columns = [ | |
| "subreddit", | |
| "id", | |
| "title", | |
| "text", | |
| "url", | |
| "score", | |
| "author", | |
| "date", | |
| "pdata", | |
| ] | |
| df.to_csv("reddit_posts.csv", index=False) | |
| # Add useful features to the dataframe | |
| def pull_info_from_reddit_dict( | |
| dict, | |
| fields=[ | |
| "subreddit_subscribers", | |
| "num_comments", | |
| "ups", | |
| "downs", | |
| "upvote_ratio", | |
| "is_video", | |
| ], | |
| ): | |
| """This function takes a dictionary from the Reddit API and returns a list of the values for the fields specified""" | |
| return [dict.get(field, "Not Found") for field in fields] | |
| # Create a new lists of posts with the additional fields | |
| processed_posts = [] | |
| for post in post_list_copy: | |
| fields_to_add = pull_info_from_reddit_dict(post[8]) | |
| temp_post = post[:-1] + fields_to_add | |
| processed_posts.append(temp_post) | |
| # save the final csv | |
| df = pd.DataFrame( | |
| processed_posts, | |
| columns=[ | |
| "subreddit", | |
| "id", | |
| "title", | |
| "text", | |
| "url", | |
| "score", | |
| # "author", author is not useful for the analysis | |
| "date", | |
| "subreddit_subscribers", | |
| "num_comments", | |
| "ups", | |
| "downs", | |
| "upvote_ratio", | |
| "is_video", | |
| ], | |
| ) | |
| df.to_csv("reddit_posts_fm.csv", index=False) | |