Attention Seeker

Inspiration

After many sad days of less than ten likes on our instagram posts, we wanted to use technology to help maximize our social media presence. Realizing that the timing of the post can be as important as the content itself, we set out to find the optimal time for a user to post a picture, based on their followers' activity.

What it does

Uses a Python Wrapper for the Instagram API to query different types of data.

How we built it

We built our front end in Kivy for Python, and made our backend with Python3 and the Python-Instagram API Wrapper. Most of the queries returned JSON objects that we would then parse to get our required data.

Challenges we ran into

the Instagram API is not very well documented and few tutorials are available. In addition, the API has undergone lots of restructuring in the last two years and much of the older documentation and community created tutorials have been deprecated. Finding information that was relevant to the current version of the API was difficult but we were eventually able to find a supported Python Wrapper.

Accomplishments that we're proud of

Learning Kivy and how to use the Instagram API, as well as improving our skills with Python.

What we learned

Instagram likes to make developers work.

What's next for Attention Seeker

Improve the algorithms for determining best posting time. For example, basing our recommendation based on additional stats like hashtags used in the caption, a given user's received like history, etc.

Built With

• bootstrap
• instagram-api
• javascript
• json
• kivy
• node.js
• python