Portfolio
Part 3: Putting it All Together
In part 2 of the project, I wireframed my story and tested the visuals, the last part of the project involved putting everything together using Shorthand to curate all the designed visuals along with the full story.
To view the Shorthand click here
Implementing User Feedback
In part 2, I created a wireframes by hand, some of the feedback I got was along building a more cohesive story and framing up more compelling call to actions. I created some of my visuals during part 2, but the majority of the visuals had to be developed. In response to the storytelling, I incorporated break sections in between the visuals to communicate more about context, this helped me communicate a more meaningful point throught the data visuals that followed. I also had to reframe headlines and text accompanying the visuals so that users could understand what each part meant at a glance. I still had a lot of work to do to develop the visuals, and this was because I could not find adequate datasets to support some of my points. Some of the claims I made such headlines have a negative bias and were backed by research but I couldn’t find the numbers to match these claims. Similarly, the point conveying short-form and long-form content also didn’t have supporting data. To make the visuals I used python to analyze the sentiment of political headlines shared on social media.
This was the data I was working with, it had headlines along with the comments these headlines generated. I was curious to mark these headlines by their bias, were they negative, positive or neatural.
I found some exsiting script on how this could be acomplished and was able to create a polarity score for each headline that would then be used to determine if the headlines were negative positive, or neutral.
There is the potenial for error with this process, but I was able to support my point. The limitation of using code to sort the headlines meant that most of the headlines were tagged as neutral.
The next piece of data I was curious about was analysing the sentiment expressed in a post along with the length of the post. For this part I used a data set that had the messages shared by the politicans, along with the type of message they were sharing tagged as (policy, personal, attack, etc)
Thinking About Audience
For this project I believe my primary audience would be people that use social media, Initially I wanted to address platform designers but I believe it would be more imporatant to imform how messaging on social media impacts the users of the platforms. Eventually it is the users that can advocate for themselves, which I followed up on in the call to action section. There are many organizations that are currently fighting for change and the support of people is what they really need!
Final Thoughts
I had a lot of fun creating the visuals, my skills in coding are quite limited and so I think it would be great to work on this project with a bigger dataset and more tools. Sentiment analysis is something that I would love to learn more about in the future. It was also fun coming up with a compelling storyline in 1 minute to present to the class.