Balanced News

@mitchlillie there is a feature on product hunt called reviews

@mitchlillie Hey developer here, thanks for your feedback! We are always adding more news sources to our app (today we added Vox, USA Today and The Blaze). If you have any request for news sources please send it my way!

@karimmaassen Thanks :D

@vicky_buddie Sorry we do not support < IOS 11. Building the app for iPad is on my road map though! Thank you for your feedback :D

@staringispolite Hello Jonathan, thanks for your comment. I do agree with you that “bad faith arguments are designed to take advantage of this” but I would also say Balanced News is a tool to inform you of what the political spectrum is saying. Regardless of believing in the article or not doesn’t stop it from being published across the internet. Our counterparts will be consuming this content either way. Knowing what our counterparts may be consuming can help us as a community/country resolve our conflicts by better understanding the stance they take upon a certain subject. I also believe that is key to have effective debate. Merely deleting content I don’t believe in isn’t an effective solution and itself can be viewed as biased. To answer your questions: -Do they first remove articles that are outright bad faith or lying, THEN balance? No we don’t not remove any articles, the articles are aggregated from the home pages of each source. In these times of Fake News I think there has been a big push into figuring this problem out (i.e Facebook, Twitter, Google). It is a very difficult subject matter, I feel in my opinion the best way to combat this is for people to be cautious and take great care and consideration into the content they consume. -Do they measure bias per article? Author? Section? Or simply by outlet? Outlet-level labeling is insufficient. No, I label the general source as “Left”, “Center”, “Right”. I do agree that some authors may tailor to opposite sides of the fence. I have plans to let users vote on an article level on where it stands on the political spectrum. Utilizing that data we can start labeling at the article level instead. Theres also some ML we can use to have better predictions of where an article stands. -How do you deal with outrage journalism if it’s not strongly Left or Right? Same as the response above, I label sources generally, I think have a voting system + ML can help with this issue. Thanks again for your thoughtful comment, suggestions and questions!