josh_hawthorne

It can be useful to have discussion with others, but if it is your only source of information then you probably have a limited view of what is going on in the world.

Transparency and showing my work.

I try to be reflective and open about my biases in my research and in the classroom. Most peer-reviewed academic journals (where I publish) require you to let people know if you have received funding to complete the research in order to publish.

I'm open with my data, code/models, and results. If someone wants to see them I'm happy to share so they can independently verify results.

I often find myself arguing that everyone has their own biases and that it is impossible to completely separate ourselves from our beliefs. Therefore, transparency and showing your work become necessary practices to help minimize bias in results.

Be aware that people on the Internet are trying to trick you is the first step. In particular be wary of things that confirm your prior beliefs about political opponents and politics (this is the easiest way that you can be tricked).

Next avoid news aggregators as they serve you information that you are going to like, or mostly agree with, in an attempt to keep you on the platform longer. Go directly to news source websites to stay informed.

Form a relationship with and consume media from different ideological perspectives to get a view of the different sides of an argument.

Right now, not very well. But that's the goal of this project to build capacity to study questions like this. I'm hesitant to name a culprit before I have evidence to support my claim.

There are two major steps to doing this: collecting/monitoring the networks and coding content as fake news.

On the network side we can collect transcripts of news reports and put them into the computer for analysis if we want to do a post-hoc or after the fact analysis. If we are aiming for real-time dash boarding then we would have to monitor the networks in real-time, convert the video to text, and then run the analysis in an automated fashion.

As for coding the content as fake, that is the tricky part. We could train a model on third-party fact checkers to catch fraudulent news stories that are emerging from the major outlets. Another way is to involve human coders to "teach" a machine learning algorithm or model to catch fake stories that are circulating.

However, the major networks are not the biggest problem when it comes to fake news. Fake news mostly circulates online and then it might bubble up into the network coverage if there is enough online attention.

It would certainly be possible to create a definition of news, but as with any definition there would always be cases of stories that are useful but that we don't characterize as news. Therefore, strict definitions may not produce the best outcomes for the public.

Professional societies for journalists exist. I could imagine that they have some sort of criteria for what news is and what it is not. This could be a body that proactively defines news.

You are totally right that historically the news was more uniform and that the explosion of content has made it difficult to tell between news and opinion.

Given the current levels of partisanship in the US think that any effort to classify and define news result in claims of partisanship, censorship, and increasing our polarization problems.

In some ways, President Trump is attempting to do this by calling some news sources (those mostly critical of his administration) as "fake news."