TrollHunter is a Twitter Crawler & News Website Indexer. It aims at finding Troll Farmers & Fake News on Twitter.
It composed of three parts:
- Twint API to extract information about a tweet or a user
- News Indexer which indexes all the articles of a website and extract its keywords
- Analysis of the tweets and news
TrollHunter requires many services to run
- ELK ( Elastic Search, Logstash, Kibana)
- InfluxDb & Grafana
You can either launch them individually if you already have them setup or use our
- Install Docker
docker-compose up -d
.env with the required values
export $(cat .env | sed 's/#.*//g' | xargs)
You can either run
pip3 install TrollHunter
or clone the project and run
pip3 install -r requirements.txt
The second main part of the project is the crawler and indexer of news.
For this, we use the sitemap xml file of news websites to crawl all the articles. In a sitemap file, we extract the tag sitemap and url.
The sitemap tag is a link to a child sitemap xml file for a specific category of articles in the website.
The url tag represents an article/news of the website.
The root url of a sitemap is stored in a postgres database with a trust level of the website (Oriented, Verified, Fake News, ...) and headers. The headers are the tag we want to extract from the url tag which contains details about the article (title, keywords, publication date, ...).
The headers are the list of fields use in the index pattern of ElasticSearch.
In crawling sitemaps, we insert the new child sitemap in the database with the last modification date or update it for the ones already in the database. The last modification date is used to crawl only sitemaps which change since the last crawling.
The data extracts from the url tags are built in a dataframe then sent in ElasticSearch for further utilisation with the request in Twint API.
In the same time, some sitemaps don't provide the keywords for their articles. Hence, from ElasticSearch we retrieve the entries without keywords. Then, we download the content of the article and extract the keywords thanks to NLP. Finally, we update the entries in ElasticSearch.
For the crawler/indexer:
from TrollHunter.news_crawler import scheduler_news scheduler_news(time_interval)
For updating keywords:
from TrollHunter.news_crawler import scheduler_keywords scheduler_keywords(time_interval, max_entry)
Or see with the main use with docker.
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