This repository contains the auto-generated sources that we use in nimiq.com.
We are using Github Actions It will run every Monday, Thursday and Saturday at 03:00 UTC.
You should have the following environmental variables:
python3 ./src/tweets.py
python3 ./src/stats.py
python3 ./src/social_score.py
We like to know what the community is saying about Nimiq.
We use Twitter's API to get the latest tweets that contain the word nimiq
.
Then, we filter the tweets using finiteautomata/bertweet-base-sentiment-analysis model from Hugging Face 🤗.
We store two tweets datasets: All tweets and Positive tweets.
Compute the amount of commits and additions made in the last N_WEEKS
.
We use GitHub's statistics API to get the stats of the last year, and then we filter the data to get the stats of the last N_WEEKS
.
We store two files: Stats and Stats by repo.
Fetchs social stats from LunarCrush.
We use Lunarcrush's API to get the stats of NIM. These are the stats we are using:
social_score
: Sum of followers, retweets, likes, reddit karma... of social posts collectedsocial_score_24h_rank
: Position/rank of the output 24 hour social score relative to all other supported output, lower is best/highest social scoreaverage_sentiment
: Average sentiment of collected social postssentiment_absolute
: Percent of bullish or very bullish tweetssentiment_relative
: Percent tweets that are bullish (excluding neutral in the count)social_impact_score
: A proprietary score based on the relative trend of social_scoregalaxy_score
: A proprietary score based on technical indicators of price, average social sentiment, relative social activitysocial_contributors
: The number of unique accounts posting on socialsocial_volume_calc_24h
: Number of social posts over the last 24 hourssocial_score_calc_24h
: Sum of social engagement over the last 24 hours
We store the stats in social-score.json.