So What? Top News and Web Stories

Well, hey everyone, happy Thursday. Welcome to So What the Marketing Analytics and Insights Live Show. I'm Katie, joined by Chris and John. Hey guys, how's it going? Over here.

It's going. It's the the time in December is flying by. Agreed. Um, yeah, I think we have what, like basically another full week and a half before we can expect anyone to kind of get back to us and help us be productive. And that's about it.

If that. I was actually gonna start keeping a a tally of how many times either I said or someone else said, let's revisit that in the new year. Because I feel like that's where we're at now. Um well anyway, so we are gonna be productive today. So on today's show, we are covering top news and web stories.

Um so we're gonna look at how much content is being produced for the web, uh, how to use SEO tools to measure the effectiveness and how AI tools play a role in top news uh and web stories. And so if you weren't aware, every year Chris puts out the 12 days of data, and he starts on December 1st and he goes through, you know, 12 days going through various topics. And one of the topics, Chris, that you always cover is how much content is being produced in the form of top news and web stories. So where would you like to start today? I think we should probably start off with um talking a bit about the data sources.

Because depending on your perspective, uh you get very different numbers and very different types of what constitutes news. So what constitutes news, Katie? You know, that's I think that's the question because it's gonna be a different answer for everyone. If you you know, uh we can ask John the same question. Um, but for me, it's information about what's happening in the areas that I care about, and so I would love to say that I care a little bit more about what's going on politically.

I think I'm just kind of burnt out, so I don't check those particular stories as often as maybe somebody else would, but I do tend to care more about like what's going on with the environment and climate change and you know, things related to health and seasonality. So though for me, those are news stories. It should be articles that you know present information about current affairs, essentially. Um John, what's news to you? Yeah, that's a great you know, this is one of those things we really need a journalist on here to like explain this because no matter how, as a layman you try to, you know, explain it, the journalists have a better view of this.

I mean, it's really recording history, right? It's recording what is happening around us at the time, and then the challenge though that you have is there's just an infinite assortment of topics that people are interested in, right? Like, I mean, there's a whole gigantic industry of sports journalism, which I just don't have any time for. I mean, it's not that I don't like it or don't care, but I just that's not anything I'm looking at today. But uh, but uh there's plenty over there.

So, yeah, it's the big the thing that gets me the most is you know, it's generating content about what's going on in the world, and with all of the tools we have now, it just seems to be exploding at you know at an exponential rate. And so that's why I'm always excited to see what happens with this because that we have had, I don't know, over the past few years, there were a couple years where it finally did trend down where the automo automated stuff had to have the dial turn back on it a little bit. And I think it was we had just reached such saturation that all of the spam tools were no longer doing anything good. Um but now we're entering a next generation of stuff with AI writing much better copies. So we may be stepping on the gas again and maybe going from absolutely horrible to even worse than that.

But um uh I'm excited to see what the numbers show here. So from a technological perspective, news is who your data providers say news is, right? Which is a thoroughly unsatisfying answer. But when as a marketer, if you're trying to figure out like what constitutes news, it's who the data provider says news is. So let me give you a couple of examples.

We're gonna go first to uh Google's G Delt database. So for those who are unfamiliar, GDELT is an open source uh project. If you go to Gdel.org, uh this is a global database of news powered by Google Jigsaw, the contents of which are uh a substantial portion of what is in Google News, right? So the the Google the G Delt database is this gigantic huge database ingesting news in real time from all over the world, and as with so many different news you know sources, it's based mostly on URLs. Like these, you know, for example, CNN.com would be a newsworthy domain.

Some dudes blogspot.com blog, not a news domain. Um, and so from that perspective, news is essentially who Google says news is. Um, and this varies from provider to provider. So let me show you another example uh from the hrefs software, uh hrefs is SEO software. I put in uh a search for stop words, like basic stop words A or and or the because that's the easiest way to scoop up essentially the entire database.

In 2022, according to hrefs, there have been 489,000 million pages published this year that are in some way notable. But they added a button this year saying, you know, there let's focus on news. And now you this this slims down to about 65 million pages. So for them, they think this about 65 million pages of news in this year. If we go to the GDELT database, we run the same query.

I want to count the number of URLs uh where the publication date was this year, you get about 53.6 million URLs, uh, which is about uh you know, it's what about a 16% difference between the two. And when you quickly inspect the the data to see like what's in there, the the data varies. You know, the URLs vary, but you do see uh, I would say more traditional accredited news sources, I think would be the best way to put it in the G Delt database. Whereas the HREFs database tends to be a little bit more uh a little bit less restricted. It's not in not entirely, but you see some things here like you know, Microsoft documents, right?

For the Microsoft documentation website. I would not personally call that a news source, right? Even though that is technically news, like this is this is you know what's changed. So that's the first and probably the most difficult place to start is what constitutes news. For the 12 days of data, when we look at what constitutes news, we used to use the HRES database, and then two years, three years ago, we'd switched over to the G Delt database uh as the source of news like things, partly because it scales better and it's easy to work with, and partly because they do have a lot of interesting features in there that you don't get out of an SEO tool.

Such as there's a system of encoding news events uh called cameo. Uh cameo stands for conflict and mediation uh event observations created by the uh by Penn State University. This is uh a way for you for news organizations to encode uh what kinds of news stories there are, right? So there's uh all sorts of things, meetings and you know, armed invasions, you name it. And this encoding combined with Google's assessment of the news, um, they can they rate news in the G Delt database based on what's called the Goldstein scale.

The Goldstein scale is a as a number between minus 10 and positive 10 of uh how impactful a piece of news is. Minus 10 means uh negative global consequences, right? Uh example, Russia invades Ukraine, right? You know, that's that's a pretty substantial negative uh uh impact piece of news. Some dope parks a ship in the Suez Canal the wrong way and you know blocks international trade for 14 days out of that event will be a minus 10 on the Goldstein scale on a positive 10 scale uh would be uh the rollout of a uh an mRNA vaccine for COVID, right?

It's gonna have strong positive effect, and then zero in the middle of the scale means it has no impact, right? Um we put out a press release announcing that that Chris cleaned his desk. That's a zero. That's a 10. If you've if you've ever seen Chris's desk and it gets cleaned and perhaps disinfected, that's a 10.

That's a anyway. Who all right? So let's say I'm following these codes. Who is deciding what's a negative 10 and what's a positive 10? Google.

And we don't know how. And that I take issue with. Because, you know, as we've discussed in previous episodes, if it's you know, if they're using some sort of automation or AI to code based on the words that are written, or you know, uh databases that have been trained and those kinds of things, we know that there's bias introduced into those things. And so, you know, the way that I feel about, you know, as a terrible example, terrible, terrible example. The way that I feel about a Russian invasion might be on a different, like I might see it as a negative two, whereas John might see it as like a negative four, and Chris sees it as a negative ten.

And yet somehow all of that is supposed to be, you know, telling us what is good and what's bad. Like I take issue with that. And therein lies the challenge with it's probably a separate show entirely about how AI is an intermediary between between ourselves and reality. Uh recommendation engines are literally telling us what to see and read and watch and listen to. And it may not necessarily be conscious decisions on our part anymore.

So these codes are all inside this the G DELT database. And among other things, let's talk about some of the data you get in here, because I think there's it's that itself is useful. Uh all these tables provide you know lovely schemas that explain uh what's involved. You get the date the news was published, of course. And generally speaking, in the encoding of the data, there are actors.

Um so you have actor one and all the the encoding about it, actor two and all the encoding about it. So for example, um when Russia invades Ukraine, right? Russia it would be actor one as a nation state, and actor two would be Ukraine. Uh and you would see all that uh data in there. You have the event code based on those cameo lists of you know what kind of uh event is it, and then you'd have things at the Goldstein scale, how important is it?

How many times within the first 15 minutes of that event being occurring was it mentioned, how many sources mentioned it, how many articles mentioned it, what was the the sentiment, the tone of that, uh and you know, more data about the uh the the actors and the geographies, and all this is put in these lovely tables. Now, here's the part where I think for anyone who's an aspiring data scientist, or for anyone who's just curious and and likes to poke around. One of the wonderful things about the G Delt system is that it is completely and totally free of cost. Anyone, as long as you can use Google cons Google Cloud and you know how to use BigQuery, can go in, work with the data in the database, or export it and put it into your own software. Totally free of cost, paid for by Google and the G Delt uh Foundation.

And it's one of like 300 uh free data sets that Google offers, which I think is pretty cool. So if you when you compare that to say SEO software, SEO software, not free. Uh, and there are some pretty strict quotas, pretty strict limits on how much data you can export from the systems depending on the plan that you're paying for. Um in HRES, I think you have to have like the pro premium platinum ultra plan or whatever to download you know millions of records. Otherwise, you're you're limited to I think uh 250,000 records per month, which when you're dealing with tens of millions of news stories a year, uh you run out of runway real fast.

That was one of the other reasons we moved to the G DELT database when we're doing our our 12 days of data was except for the processing cost, um if you make a copy of it, you can work with this data for free. Gotcha. That's I mean, that is interesting because you don't often see data sets that big, just like and here we are with documentation, by the way. With documentation schemas, and it's in real time. I think there's a 15-minute lag between when a piece of news appears on the wire and when it shows up in the G DELT database.

Um, we have actually spoken to clients in the past uh recommending that in addition to their regular media monitoring, they may want to you know search for URLs, you know, mentioning brand names. It might not be the worst idea in the world to have that uh, you know, a simple monitor set up in there. So that's the data sources where we get this information. The next part is what do you do with it? Um typically you it depends on your level of skill.

Um there is no friendly interface to this software at all. Um, it's it's just a SQL tape database. Um the G Delt project does offer some basic UI stuff if you want to try and and mess around on their site, but it does not scale particularly well. They mostly recommend hey, you should probably go and and write some code, um, which uh is is not super helpful. So if you have skills with uh with SQL, the the SQL query language, um, you can just you know edit straight in the database.

Oh, that's the wrong tip query. Um you can't just do your edits straight into the database, like what I was counting, the number of news stories. I said, show me the number of URLs in the database for this year. Very straightforward um SQL queries, and then it's up to you then to decide how you want to work with that data and process it. We do it with um unsurprisingly, the R programming language.

So we take uh in this case, we take our the data out of the G Delt database, uh, which takes a a lot of effort, and then we format it, uh, we process it and format it into essentially um some reasonably nice looking uh charts and graphs, which is substantially easier. So, for example, this year we took the Goldstein codes, the ones that were what marked either minus 10 or plus plus 10, and said, what categories were they? And the use of conventional military force uh and fighting with small arms and light weapons, those were predominantly around two big events. One was the invasion of Ukraine, and the other was the ongoing genocide in Ethiopia. And between those two, that comprised the majority of the serious events this year, the things either minus 10 or plus 10.

And you see a few other things. When we look at the general events, right, you get a lot more variety, right? You know, made a statement, made an appeal request, uh, made a visit, praised or endorsed, hosted a visit, consulted, um, expressed intent to cooperate, had a meeting, right? These these codes are a lot more varied, but they allow us to see you know more of what the news was. Even still, there's still a lot of armed conflict and things.

2022 was a very violent year. So for the purposes of this exercise, what you are trying to establish is how much more news was published than previous years. If I'm not trying to establish that, what are some other use cases for this kind of data? It depends on what the purpose would be. Um, one thing that would that we've used this for, and we and this is actually uh a subject of the next two days worth of of uh 12 days of data, is because the source URLs are built into uh the G Delt system, you can actually narrow down uh specific kinds of news.

Uh, one of the ones that is you know my personal favorite is identifying press releases, right? Press releases uh from like business wire uh and so on and so forth. We can extract and count the number of of those those things per uh domain so this year in 2022 uh as of last night we've done 297 thousand press releases this year which is um down a little from 2021 uh still up from 2020 and yeah we're only what eight days into December so we still got some we we still got time for marketers and communicators to issue uh couple uh tens of thousands more press releases things that they're pleased to announce exactly things are pleased to announce um and so if there's specific types of news that you're looking for or specific publications and you want to dig in and see what just with the volume of that publication this database is it if it's in there is very helpful so I could say let's see and source URL like CNN.com and let's go ahead and run this now I know that you said that G Delt uh scales better for what you're looking for but if I was just looking purely to count the number of press releases uh or something that contained the red press relief could I go back to my SEO tool to do that kind of work it depends on how good the tool is um a lot of the SEO tools do very basic filtering and they don't allow you to do very elaborate queries. Um but what if it's not an elaborate query? What if I just want to literally find how many press releases, like press release?

That's a an elaborate query. Um I'll show you why. Yes, this is this is what the query looks like to identify a press release. It's it's selects your count from the table, and then these are all the different URL conditions you'd want to identify domains and then certain types of words like you know for immediate release and so on and so forth within the URLs so that you can export them out. And this is not this is something that your an SEO tool is gonna really struggle with because most tools don't focus on press releases.

Um it's technically a piece of content though, exactly. They're out there, but this there's a lot of of different news wire services. One of the things we have to do uh every year when we do this stuff is check, you know, do a quick bit of Googling. Have there been any new press release companies that have you know come onto the scene this year, and then we add them to our queries. You know, it's just amazing to me that with this whole database, you could easily do a front end of completely customized news, right?

You could say, like, here's the 35 topics I want, and then filter out everything, you know, below a seven in either direction, and you could have completely tailored to you news, but because we're clickbait driven, like nobody cares. That's true. I mean, and again, there is still the limiting factor of what constitutes news, right? Uh your site may not be in here, even if it you you may have a very trustworthy publication that is not in here, or it's very difficult to identify. For example, I read uh Dr.

Jeremy Faust's inside medicine uh bulletin. Uh he is a Harvard-trained physician, he is you know one of the the top experts in infectious disease, and he writes on Substack. Uh let's see if Substack is in here. Well, I mean, and this goes back to the question of what is news. Um, like so for you, that is a source of news for you.

Um, you know, I could start publishing to my website every day, like how many cookies I did or didn't eat. That's news, technically. But I don't think I don't think it needs to be in there. Yep. Um, and there's very few limit, there's very few uh pages from the Substack domain in here.

So I'm pretty sure his is not in there, given that there are thousands of Substack newsletters, including my own, right? I have my my lunchtime pandemic newsletter is in there. That is a roundup of news. Uh I is a a credible news source. I mean, no, I don't think so.

And I and I say that because I'm a marketing guy and a data analytics guy putting together a COVID newsletter. I am in no way, shape, or form a qualified health care practitioner. You should not be taking medical advice from me for any reason whatsoever. But I try to share stuff that you could go ask a qualified healthcare practitioner, hey, is this a good idea for me to do? I mean, beyond the obvious, like, hey, you should probably wear a mask because filtration of air is important.

But in this database, you know, when we looked when we did the CNN one, the CNN one had 117,000 pages in here. Uh Substack has 620. Are those both valid news sources? It depends. Uh, and therein therein lies the the challenge.

So, from a a marketing perspective, you can zoom in on specific data sources and you can get a sense of what data is available about those news sources to help you gauge sort of its importance and how a system like Google sees it, which again is one of those things that you don't really get the inside scoop from the front end of Google News. You don't have any indication. Let's take a look at, let's do select star from the let's substack one. I would imagine another use case for this kind of query would be research in a way. So for example, if you are trying to write a press release or trying to write an article about something, theoretically, you want to know how many times this topic has already been covered and what's been said about it so that you're not just regurgitating the same information.

Like, could you use this to try to figure out a different angle on something that's been covered to death? Possibly, but what the one thing the GDELT database does not include is the actual content. You get the URLs, but you don't get the text of the content uh itself, at least not in the events table. It might actually be in a different table, in which case you'd have to do a join on it and extract that out. Let's see what we got here.

We have from Substack, uh, we have only a couple of newsletters that are in there. Uh, but the fields that we were looking for are the number of mentions, the number of sources, and the number of articles. So, as we talked about at the beginning, number of mentions is the number of times this article, this URL, was referenced within the first 15 minutes of its publication. The number of sources is the number uh places that uh cited, and the number of articles, the number of uh um articles that this article appeared in within the first 15 minutes of publication. So, from a an impact perspective, uh again, you can see you for a specific publication, uh, how Google kind of sees uh the weight of that news.

So, in terms of if we go back to our original topic, which is top news and web stories, what metrics would I be looking at to determine this is a top news story, like the number of times it was shared, the number of times it appeared, you know, what are the metrics that because there's a lot of data here. Where do I want a lot of data here? Where do I want to specifically focus? The the two I would pick would be the number of uh mentions that a piece of news gets, I think is is probably your your first piece that's important. And the second is the Goldstein scale, which is how impactful does a system like Google think this news is, right?

Because you can get a lot of mentions on a story that might not necessarily be super impactful uh to the world that might be consequential to, for example, um Taylor Swift's tickets were selling out, right? It was very difficult to get Taylor Swift tickets. There was a lot of mentions of it, but in the grand scheme of things, and please don't kill me, uh in the grand scheme of things, I don't know that that is world-impacting news, a way that's gonna think it's gonna have you know massive consequences on society. I would I would agree with you. I'll support you on that one, Chris.

So having those two numbers, your number of mentions and your Goldstein scale, I think are good starting benchmarks. The other thing that you might want to do is bring data from G Delt into a custom system of some kind so that you can actually read the article text and process it, right? So you could get uh some data on that. And then if you really want to get ambitious, uh if you have if you have the bandwidth and the budget to do so, if you're also getting data out of your SEO tools, you can cross-reference them and see you know, uh the big stories from one source, the big stories from another source. The SEO tool is gonna have different metrics, right?

Your SEO tool is gonna have things like uh page traffic and traffic value and uh domain authority that you can cross-reference your news with and say, okay, are are these two things the same? Are these two things uh equally relevant? I would also so as you're talking about this, I could imagine you know, a PR firm who puts out, you know, news and articles all the time on behalf of their clients using methodology like this to see where the stuff that they put out stands against everything else. Like, how good did we do this year of making sure that the information we're publishing is newsworthy, is relevant. And I feel like that if I were, you know, in charge of an agency like that, I would want to know that information.

Like, I'm paying all of this money to get this news out there, but is it just getting buried in the noise compared to everything else? And obviously, you'd have to put it in context of similar topics. Like if you're putting out that, you know, trust Insights, you know, Katie and Chris swapped their roles against the war in Ukraine. Yeah, I would expect that our news probably wouldn't be that important. But if you look at it in the scheme of tech startups, for example, maybe I would want to know that it's a little bit more newsworthy.

Exactly. News like politics is all local. And you can see here just from like some of the top performing news stories in the HRES database, a lot of these are not news that would necessarily be relevant to us, like the winning lottery numbers in a certain part of India, right? That was a big story. 5.2 million visits to that page.

Probably not. So that's the other consideration with news. And to your point, Kate, you might want to be a little more focused about which news you pay attention to. One of the things that again, you can do with either of these tools, it's easier to do it with G Dell that we saw first popularized by our friend Justin Levy back when he was working at Citrix was uh paying attention to share voice, which generally we hate as a measure because it's a stupid measure in aggregate. But within the innovation he put together that I thought was a brilliant twist was paying attention to your share of voice within a specific set of publications, right?

If you are in an industry and you know there's 10 newspapers and magazines that cover you know uh virtualization software or 10 bloggers or something like that, using these tools, you could then go. They'll be back. You could use these tools to say, okay, I want to just focus on those 10 publications and then find you know how much presence do we have within that subset. Again, these tools be uh plus some of the data, you know, your custom tooling to extract the data would be a very useful way of applying this technology. Which is essentially what I was getting at.

So I mean, I think that you know Justin's smarter than me, so that makes a lot of sense. Um the other thing that, and this kind of goes back to where um we were talking about at the beginning of the show, is a lot of these publications are um you know pretty well vetted. And so should we expect to see a massive explosion of content from AI? Not necessarily. I mean, some publications probably will be experimenting more with you know uh more first drafts and more uh trivial content generated by machine, but for the most part, um, a lot of the content explosion is happening on non-news sites, right?

You know, if we put up more blog posts, you know, some machine written blog posts and things on our site, we're not an accredited news source. So that's probably not a uh it's gonna be uh impactful to the amount of news that's detected here. It will show up in the SEO tools, right? Particularly for just you know web content in general. You're going to have to see a lot more content in the SEO tools because there's so much more publishing.

One thing that you may want to think about as an organization is are you uh have you tried, and and it's a long slow process, but have you tried uh applying for to become a registered news source? Uh if in fact your your blog or your website, whatever is in fact news. Um you would do this um uh interestingly enough, you normally just in Google Search Console. Uh Search Console is where you would sub uh identify Am I a news source to begin with, and then there is an application process for you to be able to go in and say, like, yes, uh here's here's how the news works. So to go back to the AI piece, John, you had started to bring up a little bit about you know how AI is gonna impact the amount of content being created.

Have you been so in the past week? So as we're recording this, it's uh mid-December 2022. In the past week, there have been a lot of AI tools front and center that are doing a really good job of writing content based on prompts. Have you played with any of this, John? Do you think that it will change how you are creating some of your content?

No, I haven't done dug into any of this really, because it's I you know, it seems like it's great for um the kind of stuff you do at the corporate level. You know, if you basically need I need 35 101 articles on a specific topic or a specific area, it's great for generating um, you know, a new take on stuff that already exists. Um and it'll be interesting. I you know, uh there's definitely a dividing line between it's very easy to do all of that ongoing content stuff versus news. You know, and I know there's already some news generation AI stuff for like for sporting events.

You know, there's whole leagues of baseball where there's no human that does the reporting, you know, they just feed the box scores into the machine and it spits back a story based on what it can do. Um so it's gonna be interesting to see how fast this stuff can scale to to a more news-worthy um approach. And how does that get managed? You know, like I I I would love to think that there's some real-time editing going on and at least you know some kind of proofing, but we also know that you know, as we saw during the blog explosion, that there's plenty of people that are just gonna post, you know, and look and review later. The sports, the sports piece of it is interesting because you're right.

It's here's the scores, here's what happened, and then the AI takes it and writes up whatever the news story is. So, you know, to your point, John, it sounds like AI is starting to play a larger role in news, just that particular segment of news, maybe not political news or other news. I mean, who knows? It might be. So, this is where I would turn to Chris to say, what don't John and I know about where AI is in writing the news.

Um people have not seen really what the the newest language models are capable of and what the next generation ones will be capable of. Um, they are so much better now than they even were six months ago in terms of of their capabilities. For example, this is the Da Vinci uh Da Vinci 3 models, part of GPT 3, which is OpenAI's product. And I put together um this prompt. This is a very complex prompt.

Write a press release uh about us, right? And and these facts uh here. Uh let's put this one more thing. Uh trust insights URL is trust insights.ai. I don't say, I want you to write me a press release about this thing.

This is uh again, it's a very detailed prompt. Um, there's a lot of extra stuff in here that you you don't normally think of. And what you get is uh you know a decently written press release. Now there's some things here that are factually incorrect, right? Because the trust insights CEO is that's incorrect.

So I was gonna say, who the heck is John Smith and when was I getting replaced? That's Smith. And so let's take that out. But we can see like this prompt is essentially more or less kind of like factual coding. But I would, you know, and it's funny, as you're doing this prompt, you're taking the time to write all these facts.

Wouldn't it just be the same as you actually just writing the press release because you just listed everything that the AI is gonna regurgitate back to you? It is, but it this is a a lot easier because a good chunk of it is going to be templated in ways that are gonna be incorporated uniquely within the output that you're not wouldn't just get from you know writing it yourself. I mean you absolutely could write it yourself, but think about this by varying a few of the facts uh and then feeding this in not just through the web interface, but through the actual API, you could generate a thousand of these, two thousand of these with in about the same amount of time. Um I'm sure that this is probably uh a conversation to have later, but uh we have a new product. Did you make that up for the sake of this?

This is this is totally made up. Okay. I was like, wait a minute, what don't I know? Not only am I being replaced, and we have new products. This is this is a a a bit of tongue-in-cheek fun.

Artificial intelligence is machine-based, natural intelligence of humans. Ah so your brain has 330 trillion neurons, right? So you that is the largest neural network platform ever deployed, which is true, technically true. Um and now it's gotten those facts correct. Right.

Now we've d we've used this. Um we were doing this in Analytics for Marketers, writing song lyrics and poems uh about Google Analytics 4, writing you know, rap lyrics, uh, and the model, the underlying model is extremely powerful and very, very flexible uh in ways that people do not fully understand um the capabilities of these tools. And this is uh generation sort of 3.5 of this particular model. While the generation 4 model will be out sometime next year. And this is untrained, right?

So this is this is the the big generic model. Um there are ways for organizations to fine-tune this to say I I want to feed in all of our existing blog content, or I could feed in all the transcripts from marketing over coffee, and that would add weight to have the output sound more like the kinds of things that that you the content you've already generated, making it very difficult to distinguish. So, from a news perspective, if you wanted to capture the tone of CNN to make your own news site, you could absolutely fine-tune a model like this and say, okay, I want to train on the way CNN publishes stuff and put it in here, or a Scientific American, or Fox News, or whatever the the news source you want from G Delt, the G Delt database that we saw we saw, you with some code, you could extract that data and then use that to fine-tune these models. All right, so John, let's let's start placing bets on how long before news is 100% AI written and people aren't needed anymore. Or rather, how long before John decides to you upload all of the old marketing over coffee transcripts and have the AI pretend to be uh John and Chris for a few episodes.

Yeah, you know, it's the thing with all this is it's always built on existing stuff. You know, I mean there's there's no exploration of the frontier, you know. I the thing where it's amazing for is yeah, here in code being able to validate code and fix bugs, you know, as be able to do pair programming without a second person, or um in research, you know, if there's just research where you you can determine how to do an experiment to be able to have a machine run 50 billion different, you know, um variations on a single thing, that's amazing there. But as far as yeah, I don't I don't see it doing a lot of creative writing on current topics. That that's gonna be a challenge.

I mean you can't see how it would work here though, if you basically just put the key story points in there, you would be able to bang out an article. Plus, I think the other and another angle of that is to be able to bang it out in 25 languages all at the same time. You know, that's now you're talking about something pretty interesting pretty quick. And what's interesting about this is um, and and this is gonna it is already a problem. We're already seeing this happening online, but we're seeing it happen with very unsophisticated tools, is as the language models evolve, you can do a lot of things with them that are ethically questionable, right?

Um or just outright illegal. So let's take a story like this from CNN. Right, let's take this here. Copy that a professional tone, a reading level of grade 12, and correct any grammar and spelling issues. And so you can take something that's it perhaps was written um for a sixth grade audience and upscale it.

You can you can change the language, you can move things up. And now from a if you were to if we put these two pieces of text side by side, that's not how we go away. So here's the rewritten text, and here's the original text. These are structurally different enough that a search engine is going to see this is separate content, different content. So far has been really rudimentary, like swapping out one adjective for another, really clumsy, and you can tell very easily by reading it.

Okay, I know exactly which blog this was scribbled from. Like when uh the folks over at Content Marketing Institute put out a new blog post, instantly in our social media mentions uh and media monitoring tools, we get notifications of all the the copycats that are taking that content, either republishing it as is, or making you know really, really awkward adjustments. This is going to change that game. This is going to change that game to the point where it's now it's now unique enough that it will pass muster. So to the question of top news and web stories.

If I were running CNN and I said, okay, I want six different versions of this story for different audiences, this would be a good use case for using something like OpenAI. I write the initial story with all the facts in it, and then I can put in this prompt to say rewrite it for a reading level of grade three, grade six, grade 12, so on so forth. Exactly right. Okay. And then that helps me scale the amount of content that I'm then creating and putting out there.

And because I'm a trusted news source, then I'm dominating the field in terms of the content that's going out. Look what happened when I said make it grade three with a more casual tone. Mortgage rates have gone down again this week. It's the fourth week in a row that rates have dropped, right? It's a very it is textually a very different tone, different content.

So to your point, Katie, you can absolutely re-spin your existing content programmatically into very, very different formats, um, into very different uh ways of sound uh things sound that is still preserving the factual data, right? This uh this article, these rewrites, all are still preserving the data correctly, but they're creating different variations. So, yes, as a as a marketer, as a content creator, these this offers you an incredible amount of of potential. Um, as an intellectual property defendant, this is kind of a nightmare scenario because the rewritten stuff, you can plausibly say if it's based on facts and facts cannot be copyrighted, that yeah, somebody cribbed your work, respawn it into something brand new, and it may do better than your content. Well, on that happy note.

Yeah, I've seen more than one report that this is the end of the college and high school essays. Like it's game over with this. It's gonna destroy it. Um I was in one of my Discord servers the other day, and a friend of mine was saying, I'm really stuck trying to write a paper about uh is it um write a five paragraph analysis of the artistic techniques of School of Athens by Raphael. Focus on the techniques and the cultural context.

Right, and they were like, I'm not really sure what to write. I'm supposed to you know, focus on uh you know this or that. And I said, here, I copy paste out of OpenAI, so that's that's a starting point for your paper. Um there have been some interesting conversations, uh, particularly on Reddit of students whose grades are fantastic now because they have AI just generating their papers for them. Uh, and the the teachers give the stamp approval, these pass plagiarism checks because they're original text.

Um, and so yes, it is a hundred percent the end of essay, you know, college essays, which raises the very valid question, what's the point? Right? What is the point of having a student write an essay when a machine can write it better? Well, I think that is a topic for another show and something for us to ponder. It is.

But I want you to think about that. What is the point of your content marketing? Right? If a machine can generate better content than you, then what do you need to do to rise above what the machines are capable of? We've been saying now, uh, and you can go onto the Trust Insights website or our YouTube channel for the last five years that the bar of competency uh keeps going up.

Five years ago, when we started Trust Insights, machine generated language was pretty awful, it was word salad, right? And so even the drunk intern would can do would was going to do a better job. Three years ago. We don't have a drunk intern for the record. We don't even have a sober intern.

Three years ago, the GPT series came out, and some of the machines could do okay, not great. GPT 2 came out, you know, two years ago. Now the machines are starting to write reasonably well. And now with GPT 3 and 3.5, the machines are writing well. Machines are writing very well.

And so the challenge for us as marketers and as creators is what do we need to do to up level our skills to stay ahead of the machines? Because as of right now, like with a college student writing a paper, the machine's gonna do a better job, period. Well, I think that you know, you've brought a coup brought up a couple of things that we can cover on other podcasts and shows, including, you know, recommendation engines, you know, AI powered recommendation engines, and what is the point of content marketing if AI can write it better? I think we can absolutely dig more into those topics uh at another time. So for today, uh, in terms of your top news and web stories, it sounds like AI is definitely gonna play a part in it, but if you're looking to just do general research, your SEO tools are pretty good.

Something like a G Delt database is better. Uh, but know that it doesn't have a user-friendly interface. So be prepared to at least start uh doing some research on basic SQL commands. Those aren't very hard to put together and they're pretty straightforward if you know what you're looking for, which you know what's the question you're trying to answer before you start all of this. Uh so Chris, final thoughts, John, final thoughts.

I'll enjoy first. Yeah. Cameo rates the articles, but only Google can say word up. How are you gonna top that, Chris? I'm not.

I'm not gonna even try it. Um folks for hours. Until next week. Thanks, folks. Bye.

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