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Facebook Likes Tracking Database for Music Profiles - repost

This project received 8 bids from talented freelancers with an average bid price of $36 USD / hour.

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Total Bids
8
Project Description

I want to create a platform that let’s us organize/filter/track/ and sort thousands of artists and their social media activity. The first phase will include ONLY tracking Face book “likes” as it seems to be the most important indicator. Later on down the line we will implement Youtube, Twitter, and Soundcloud.

The platform will accomplish this by having a front-end / back-end system that will be hosted on a cloud-based virtual machine (which can run 24/7 and is a very cost-effective solution as well as having scalability depending on our needs as our data grows.)

The front-end (user experience) will be web-based and can be accessed with a username and password from anywhere in the world. The data will be presented in a very clean interface (bootstrap), with the data organized in rows and columns to keep it neat. We can sort the data by date ranges (two days, week, month, six months) as well as sort and filter by numerical ranges (small size artist (10,000 – 20,000 fans , medium size 80,000 -150,000 fans, or larger bands 500,000 – 1,000,000 fans ) and measure the percent growth ( y2-y1/y1) over that particular time period. It will also have a page that keeps track of when an artist hits a certain “milestone” as in (artist reaches 5,000 fans or 15,000 fans). There will be individual artist pages where we can add notes and focus in on an act at the micro level.

The back-end (system) will run around the clock to gather data and store it inside of a database, automating the whole process. There will be multiple scripts running to speed up the process in which we access the data. The goal is to create a large enough database that can tracks 100,000s of artists, and each day grab their “Facebook” likes. This will give us a picture of which artist’s fan base is growing on the internet.

Artists must be manually imported until we can come up with an automated system. To generate a large lists of artists, we will create a web script that can be accessed at ([url removed, login to view]). This web script will pull the RSS Feed information from ([url removed, login to view]) and segregate artists and tracks. From there we will search and input every artists face book page and track their growth. The goal is to import between 50-100 new artists per day from various sources (music blogs, word of mouth, venues etc.), once imported the system takes care of the rest and we can analyze the data.

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