Twitcher: Comprehensive Feed Searches

Lillian C, Megan W

This project aims to specify Twitter searches to an individual profile / user feed with recommended keywords that assist in professional searches. This is done by parsing user timelines, rather than utilizing Twitter's built-in search feature. Employers (hiring managers) and college admissions officers are the primary audience for this web application. The platform uses Regular Expressions to search for specified keywords once the user timeline is scraped using the Twitter API.

College Admissions Officers and job recruiters often have the job of looking through profiles of potential candidates to see if they are eligible. Our program could be a helpful tool for the students or individuals looking into cleaning up their profile.

Our program created an easy and efficient way to complete that with Twitter feeds. In the future, we would like to see the program expanded to other social media platforms such as Facebook or Instagram. Another tool that would be helpful is searching for images, which can easily be implemented with the allotted time. If there was more time, we also came up with the idea of having a feature to clear the data filtered. This could be a helpful tool for the students or individuals looking into cleaning up their profile.

When working the Python program into a website, we used the microframework Flask. Flask was difficult to understand, and required changes in our Python program such as in our RegEx. After looking up a lot of documentation and utilizing our teachers and TAs, we were able to execute the program.

Our group is comprised of Lillian and Megan; Lillian came to Atlanta from Ohio, and Megan from Florida. We came together because of our project and the idea around it.

This project was made by Girls Who Code Summer Immersion Program students at ATT (Atlanta 1).