In 2018, I took a class that focused on why hate thrives online, and why the very design of social media platforms inherently enables hate, dehumanization & other behavior that has a significant detrimental impact on society, outside of social media and the internet.
However, research has shown that disinformation, falsehoods and different types of bias in reporting hugely contribute to the negative sentiments that ultimately lead to & fuel people expressing hatred towards others online.
This, in addition to news audiences becoming increasingly more fragmented, is one of the core problems that needs to be addressed first. Making the vast differences regarding quality of different news websites / media outlets more visible might be a good start.
After Elon Musk tweeted about a rating system for the media, I decided it would be interesting to explore how this could be implemented, how user identification could be solved, what the rating system would be based on and the app's overall user experience.
Pravduh borrows its core functionality from the Apple News app, but includes the aforementioned rating system, so you can immediately see whether an article is biased or based on poor research. The Top Stories algorithm takes these ratings into account, pushing down articles that got worse ratings than others.
At the beginning of the project, I considered including a forum – essentially be a Reddit & Twitter hybrid of sorts –, where people would be able to discuss issues, either related to a specific article or a topic in general, and based on the principles of deliberation theory.
However, when taking the complexity of content moderation into account – an issue that even giant social media entities such as Facebook repeatedly struggle with –, I eventually made the decision not to include this feature. Even if there was an easily scalable solution to this issue, it's not clear whether another space/platform for discussion would be able to live up to the expectations.
Recent studies (Esau et al. 2017, Thimm et al. 2014) suggest that while asynchronous communication – such as in forums – and clearly defined topics will promote a rather deliberative discussion, but the overall participation is much lower than on major social media platforms like Twitter or Facebook.
By submitting their personal rating at the bottom of each article, users are helping to ensure neutral, fact-based and balanced articles are the ones getting the most attention, so people have a solid base of information to form their own opinion.
To reduce the likelihood of trolling and other low quality content, users have to submit their ID for verification when they're first signing up (similar to when you’re opening a bank account online). Ratings will still be anonymous.
This will probably not put an end to spam and users maintaining multiple accounts, but it keeps the barrier to entry reasonably high to discourage trolling & other harmful behavior, but also low enough to promote participation.
By displaying each article’s individual rating on a scale from 1 (the worst) to 10 (the best), the user can quickly determine whether or not an article is worth reading while they’re scrolling through their news feed.
As a side effect, if less people click on articles that are poorly researched / biased etc, media outlets with low quality standards will, in the long run, need to deal with shrinking advertising revenue, eventually forcing them to improve or continue losing market share.
For additional details on how the article performed in different categories, and background knowledge on how those categories are defined, the user can simply tap on the rating to open the overlay shown in the mockup above. This seemed to be the best way to convey as much information as possible while not disrupting the user’s reading flow too much.
You can play around with the prototype here.
K. Esau, D. Frieß, C. Eilders (2017). Design Matters! An Empirical Analysis of Online Deliberation on Different News Platforms. Policy & Internet.
Thimm, C., Dang-Anh, M., & Einspänner, J. (2014). Mediatized politics—Structures and strategies of discursive participation and online deliberation on Twitter. Mediatized worlds.
McCarthy, K. J., & Dolfsma, W. (2014). Neutral media? Evidence of media bias and its economic impact. Review of Social Economy.