dtoledom / influencer-detector-crawler

Influencer Detector

Influencer Detector is a system designed with the purpose of minning Facebook pages info and analyzing their relations in order to calculate influence levels within certain category over a predefined graph.


• influencer-detector-front
• influencer-detector-back
• influencer-detector-crawler
• influencer-detector-analytics

About us

We developed Influencer Detector as a school project in the Advanced Databases course. The team:

• Monserrat Genereux
• Victor Garcia
• Diego Toledo

influencer-detector-crawler

Facebook Graph API mining. This module fetches data from the Graph API. It provides an API to enable requesting data over a POST request. It stores the results on Cassandra database. Go was chosen for this task to make the fetching process a lot faster by using goroutines and make concurrent calls to Facebook.

Requirements

• Go 1.7
• Cassandra 3.0

Setup

1. Run the cql scripts under cassandra_init.cql on your Cassandra instance

How to run locally

1. Clone repo under $GOPATH/src/github.com/dtoledo23
2. You need a Facebook Page Access Token. Get one from https://developers.facebook.com/docs/pages/access-tokens
3. Setup environment variables. Create a .env file based on the example under .env.example
4. go run server.go

How to deploy

1. The app is already dockerized. Make sure you have git and docker installed on your host server.
2. Create .env file with the configuration needed. Take .env.example format.
3. Build: docker build -t influencer-detector-crawler .
4. Run: docker run -d -p 8000:8000 influencer-detector-crawler