Results for Minor Planet Names in Google News, Refreshed Daily

Early narrative explaining results as of May 13, 2022 (presentation for OPA Research):

The slide set:

Whiteboard explainer video:

Download the Powerpoint slide set which includes the video:

minor_planets_slideshow_2_.pptx
File Size:	42989 kb
File Type:	pptx

Download File

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The below is automated daily data cultivation with automated daily aggregate analysis in Wolfram Mathematica:

Daily Data [click to download csv file]

To download or use a copy of the code immediately above, click on the three lines in the bottom left and then choose "make your own copy" or "download". There is a free time-limited version of Mathematica that you can use with the download, or making your own copy will open up an online version of the software that you can use for free.

Finally, use the below to cultivate the daily asteroid namesake article counts:

names_to_search_fiverr_a.csv
File Size:	12 kb
File Type:	csv

Download File

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Python code for cultivation of daily data

import requests
from bs4 import BeautifulSoup
import pandas as pd
from datetime import date
from datetime import timedelta
#import numpy as np


#dataset containing 1200 names
df = pd.read_csv('names to search Fiverr A.csv')

#creating a list of all 1200 names
all_names = df['NAME'].values
#input_date = '2022-02-16'

input_date0 = date.today()-timedelta(days =1)#gives yesterday date
input_date = input_date0.isoformat()
#print(input_date)


#input_date = input('Enter the date in format yyyy-mm-dd example 2022-02-03:  ')

#Generating Urls from given list of names
def get_url(name):
    url_template = 'https://news.google.com/search?q={}'
    url = url_template.format(name)
    return url



#Scraping news title and date
def get_news(article,name):
    #title = article.h3.text
    title_date = article.div.div.time.get('datetime').split('T')[0]
#    print(title_date)
    if title_date == input_date:
        all_data = (title_date,name)
    return all_data


#Main function to run all code

main_list = []
def Main_task():
    for news_name in all_names:
        records = []
        count = 0
        url = get_url(news_name)
        response = requests.get(url)
        soup = BeautifulSoup(response.text,'html.parser')
        articles = soup.find_all('article','redacted')

        for article in articles:
            try:
                all_data = get_news(article,news_name)
                records.append(all_data)

            except:
                continue
        count = len(records)
#        print("---")
        main_list.append((news_name,count))

Main_task()
mynamedata = pd.DataFrame(main_list,columns= ['NAMES',input_date])
mynamedata.to_csv(input_date+'.csv')