Code to Crawl Down Instagram data using A BFS from some starting user and also collect MIT popularity score along with every image.
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README.md
__init__.py
imaging_stuff.py
imaging_stuff.pyc
instagram_stuff.py
instagram_stuff.pyc
visualize.py

README.md

Crawling Down Instagram - A python code.

This is a code to crawl down instagram. You can use it to crawl down in a breadth first search from some starting user and also collect the MIT popularity score along with every image. This also collects edges between each user therefore builds following and follower network along with capturing comments, hash tags and other information along with the images.

Reason for this code.

I was briefly using this code, when I was getting a lot of rejects on my research and was searching for new project ideas. I took this course called 'social media mining' and as a class project I thought I would investigate emotions and sentiment on instagram images. I wanted a code to be able to crawl down instagram starting from a user along with user-user relationship information images, hastags of images, comments on images, image caption and along with images, the MIT media popularity score for each image [1].

Setup.

To run this code you need the following:

1. [InstagramAPI](https://github.com/Instagram/python-instagram)
2. urllib2
3. json

Go to Instagram Developer Page and setup yourself as a developer and create an app. sure to google this out and find out what is your client_id, client_secret and also setup a redirect_uri.

Supply these properly on the boiler plate in this section of the code within the ' ':

client_id = ' '         
client_secret = ' '     
redirect_uri = 'http://localhost'    

Other data is self-referential.

Outputs.

The outputs are saved in many files. In the media folder, you'll find all images for different users being saved as .jpg files. You'll also find number of comments, number of likes, the MIT popularity score[1], what filter is being used, the image_id and the acutal link to image saved in the 'data.csv' file for each user.

In the profilepictures folder you'll find the profile picture at the time of crawling of each person. In the file edges.csv you'll find pair-wise edges for each user. In the found_nodes you'll find the nodes we have 'visited' and in the known_nodes you'll find a list of nodes, we already know from BFS.

In the file user_details.csv, you'll find details about the userid, username, user full name, number of media uploaded, number of followers, number of people being followed and the popularity score of the profilepicture[1].

References

[1] A. Khosla, A. D. Sarma, and R. Hamid. What makes an image popular? In International World Wide Web Conference (WWW), Seoul, Korea, April 2014