dongyi1996 / 30daymapchallenge-bot Goto Github PK

Monitoring #30DayMapChallenge 2020 launched by Topi Tjukanov on Twitter. Trying to engage this wonderful event in a special way.

#30DayMapChallenge related tweets are collected via Twitter REST API with the hashtag #30DayMapChallenge. The example of raw .json data returned via Twitter REST API can be found in .\example folder. This repo only select serveral attributes from tweet metadata including: tweet_id, tweet_text, language, tweet_created_at, retweet, favorite, hashtags, user_id, user_name, user, user_loc, user_follower, friends_count, media and save as .csv file in .\data folder.

Note:

• #30DayMapChallenge with image attached
• search tweets by date since everyday has diffferent map theme
• cleanning process for late entry tweets is only conducted in first two days
• some tweets in certain time zones (such as Pacific Time) might be searched and archived in file next day

Tweets with #30DayMapChallenge hashtag from 2020-11-01 to 2020-12-02 are in 32 languages (Twitter account setting) including es, en, fr, und, de, ru, lt, lv, no, tr, in, nl, pt, ja, et, ro, ca, ht, tl, pl, sv, da, it, bg, fi, eu, ar, vi, cy, ko, is, uk.

By geocoding users location in their profile, I plot the map of distribution of users who involvs in this event. Geocoding process is conducted via https://geocode.localfocus.nl/

As Topi Tjukanov mentioned in 30DayMapChallenge page, this map challenge is NOT a competation. Although this repo sorts maps by their favorite count and retweet count, this repo never intend to compare all the wonderful maps but create a altas of inspiring maps for a reference. I am also interested in exploring topis in replies in those maps with more attention (if i have time).

Top favorited tweets refers to the tweets get many liked/:heart:. The faviorite count is extracted from favorite_count value in tweet metadata.

The grid graph (87*87) includes 7569 maps. The seperated maps(resized) can be found in .\maps folder. Since the maps were automatically collected from media url in tweets' metadata, you may find a few 'noise' or duplicate images.

Inspiration for the repo came from Kalle Hallden.