Locus

Python web application to support the discovery of information flow on the Internet from online articles. It uses various citation and similarity detection techniques to collect data from the Internet, Twitter and article database to create an relationship graph. Results are presented in tables with statistics and an interactive association graph.

Table of Contents

• Usage
• Built-With
• Authors

Usage

Requirements

Python

Version 3.7.3

Redis server

Installation guide can be found at: https://redis.io/topics/quickstart.
Run server:

redis-server

Check if Redis is working properly by sending a PING command:

$ redis-cli ping
PONG

PostgreSQL

It can be installed with:

sudo apt update
sudo apt install postgresql postgresql-contrib python-psycopg2 libpq-dev python-psycopg2

Installing

We recommend to create virtual environment with venv

git clone git@github.com:matbut/Locus.git
pip3 install -r requirements.txt 
npm install

Setup

Before running application for the first time it is required to run the script setup.sh

cd locus_project
./setup.sh

Start

Run worker for searching components:

python3 manage.py runworker db_ftsearcher db_url_searcher twitter_url_searcher twitter_text_searcher google_searcher link_manager

Start server:

python3 manage.py runserver

Alternatively, you can run locus.sh script, which performs operations above.

Built-With

• Twint - Twitter Intelligence Tool
• Django - Python web development framework
• Django Channels - Extends Django to handle WebSockets

Authors

• Aleksandra Mazur
• Mateusz Buta