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
You can either run
pip3 install TrollHunter
or clone the project and run
pip3 install -r requirements.txt
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)
For crawl tweets and extract user's information we use Twint wich allow us to get many information without using Twitter api.
Some of the benefits of using Twint vs Twitter API:
- Can fetch almost all Tweets (Twitter API limits to last 3200 Tweets only);
- Fast initial setup;
- Can be used anonymously and without Twitter sign up;
- No rate limitations.
When we used twint, we encountered some problems:
- Bad compatibility with windows and datetime
- We can't set a limit on the recovery of tweets
- Bug with some user-agent
So we decided to fork the project.
With allow us to:
- get tweets
- get user information
- get follow and follower
- search tweet from hashtag or word
For this we use the open-source framework flask.
Four endpoints are defined and their
- get all informations of a user (tweets, follow, interaction)
- crawl every 2 hours tweets corresponding to research
- stop the search
- retrieve the origin of a tweets
Some query parameters are available:
tweet: set to 0 to avoid tweet (default: 1)
follow: set to 0 to avoid follow (default: 1)
limit: set the number of tweet to retrieve (Increments of 20, default: 100)
follow_limit: set the number of following and followers to retrieve (default: 100)
since: date selector for tweets (Example: 2017-12-27)
until: date selector for tweets (Example: 2017-12-27)
retweet: set to 1 to retrieve retweet (default: 0)
- search terms format "i search"
- for hashtag : (#Hashtag)
- for multiple : (#Hashtag1 AND|OR #Hashtag2)
tweet_interact: set to 1 to parse tweet interaction between users (default: 0)
depth: search tweet and info from list of follow
Information retrieve with twint is stored in elastic search, we do not use the default twint storage format as we want a stronger relationship parsing. There is currently three index:
The first and second index are stored as in twitter. The third is build to store interaction from followers/following, conversation and retweet.
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.
How it works
- Insert a sitemap that you want to crawl with
insert_sitemap(loc, lastmod, url_headers, id_trust)
- Then run
scheduler_news()which will retrieve all the sitemap that you have inserted in the database
- You can also run
scheduler_keywords()to extract the keywords that are missing from the url that have been fetched.
- Every urls found are inserted in elastic.
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.
We use grafana for visualizing and monitoring different events with the crawler/indexer as the insertion of an url in ElasticSearch and the extraction of keywords in an article.
Create new events.
- Create a new dashboard in grafana, save as json and add it to
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