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Robot journalism is starting to become a huge thing, and we see so many interesting examples.
In this episode of the Baekdal Plus podcast, I take you through all the major elements and considerations, and with a special guest.
You can listen to it on any of the major podcasting platforms, or right here, and, if you need a transcript or want to see the links or videos I'm referring to, check below.
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Hello everyone, and welcome back to the Baekdal Plus podcast. This is episode 12, and today we are going to talk about the future of robot journalism.
But today I also have someone special here with me. Her name is Hannah and ... well, Hannah, will you say something?
Hannah: Uh ... hi
Thomas: Heh... yeah okay. But ... uh.. tell us who you are.
Hannah: Well ... Okay. Hello everyone. My name is Hannah, and I'm what some would call a robot.
Thomas: Yes! That's exciting! So Hannah, do you know something about robot journalism?
Hannah: Well, I was born a robot, so I think I have a bit of experience talking about this. And on top of that, I have a background in computers and machine learning and things like that. So yes, I think I know a bit about this.
Thomas: Oh Great!
Well, let's get started then. Shall we?
Thomas: Okay, so everyone is talking about this trend that we see around robot journalism, and more than that, we are now seeing more and more real-world examples that actually work.
So Hannah, do you want to talk about some of these, or should I start?
Hannah: No, you go ahead!
Thomas: Okay, if we look at some of the...
Hannah: Well, actually, could I just say something first?
Thomas: Uh... Yeah, sure.
Hannah: So, a lot of people talk about this as being robot journalism, but that's not really the right word for what we are going to talk about. It's like when ... uh... people talk about AI. A lot of people say something is AI, but there is no actual 'intelligence' involved. It's just computers doing fancy things and running algorithms. But the computers don't actually know what they are doing.
It's the same with most robot journalism. The dictionary definition of a robot is that it's a machine resembling a human that is able to replicate certain human movements and functions automatically. And while there are a few crazy experiments building robots like that, especially in Japan, this is not really what we would call robot journalism.
Instead, when we talk about robot journalism, we are really talking about the automation part, not the robotic part. And I think it's important to mention from the very start, because it often gets in the way of how people think about this. Robot journalism is not actually about creating a robot. It's about creating anything that can automate the newsroom. And once you start to think about it that way, you realize how many different things it can be.
Thomas: Yes, this is a very good point. When we use the phrase robot journalism, many people start to picture in their minds one of those Japanese news reading robots, where they are sitting in a studio and trying to act human.
But that's not really what robot journalism is about.
The market for actual robots doing journalistic work is extremely small, and usually only works as a niche. There are a few fun examples of this, where people have actually created a robotic-like character to be their main 'face'.
If you have the chance, head over to YouTube and look up Ami Yamato. She has created a channel where she is represented by a robot-like character and it's really well done.
It's not a real robot, just a digital animation, but it's kind of the same as these robot news hosts.
Here, for instance, is when she introduced herself on a BBC program.
And here is a funny video of her interacting with an edited version of Sherlock Holmes.
But while this is fun, this is not really where this trend is heading. The real trend is when we look at all the things that aren't about creating an actual robot, and focus on the automation part instead.
Which means that a better way to describe this is to call it news automation. And what you are actually doing is creating automated systems that can do journalistic tasks.
And, the new thing is it can now be used to do journalism where before it was just doing technical work.
Hannah: One example is that if we think about automation as it happened before with publishers, they were always focusing on only the technical side or the business side of things.
For instance, in newsrooms we would see how they were using robots to automate parts of the CMS. Like, automatically adding pictures, or converting something from one format to the other ... or maybe do some technical things in relation to the business team or the analytics.
But then several years ago, we started seeing how this technology was used to do journalistic tasks.
One example of this was in 2014 when the LA Times started using automation to publish articles about earthquakes. It was very basic. But what they did was to link out to the US Geological Survey, where every time there was an earthquake, it would take that data and put it into a template using some very rudimentary templating and natural language ... uh ... fiddling
Hannah: And this was really where people started seeing how this could be used for more than just technical things. When you have a system that can write articles in a fully automated way, you have an actual robot journalist.
Suddenly we have automated systems that are directly doing journalistic tasks. It's not just automating the work processes, it's being a journalist.
And this is really the important thing to remember about this. When we look at newsroom automation as a whole, a lot of it obviously has to do with every single thing that a newspaper or a magazine can automate.
But when we talk about robot journalism, we are specifically talking about the part of this automation that is entering the newsroom and doing what would be considered journalistic or editorial tasks. Things that we used to do as journalists, that can now be either fully or partly automated.
So, I think a good way to explain it is to talk about the different categories of robot journalism that we see, and how this trend plays out for each.
Hannah: Yeah, sure!
Thomas: Okay, when it comes to robot journalism, there are four basic categories of automation that..
Hannah: No, five!
Hannah: There are five categories!
Thomas: Oh yeah... heh... sorry. Uh... this is the advantage of having a robot helping you. Hannah is a lot better at remembering things than I am.
Anyway, there are five categories of robot journalism that each play together.
First of all, we have input and output ... obviously. But then between those two, we have three categories of automation that help us turn things into a usable result.
These are: data processing, natural language processing and the broad category of understanding, which today is often done with machine learning.
All of these, of course, play into each other. So a specific system might have a combination of all of these that help make it work. But let's talk about each one, because there are some really exciting things happening with these in relation to journalism.
Thomas: So... let's start with input. Not just because it's the first part of the process, but more because it's actually the one that I like the most.
Hannah: Oh, you mean how it can now do soft inputs?
Thomas: Yes, exactly!
If we think about how we can automate the information that we get in, there are several different layers to this.
In its simplest form, we see the layer where a publisher is simply just requesting data from a known source, and in a known format.
A good example is with the LA Times, as Hannah talked about earlier.
Thomas: What the LA Times is doing is simply to get the raw earthquake data from the US Geological Survey, and there is no ambiguity about it. The data is completely defined. Here are the latitudes and longitudes where it was recorded, here is the time when it happened, and here is the magnitude.
So, it's all very simple. And it's very easily converted into something the newsroom can use, either as a tool for journalists, or simply to produce the articles automatically.
And there are a lot of areas where this type of simple processing can really help the workflow in a newsroom.
Hannah: Yeah. And ... you know ... this is even more true for things where you need larger datasets from multiple sources.
Thomas: Yes. Absolutely.
But that reminds me of another important point, which is that we see journalists do more and more data journalism as a way to write better stories, but this leads to the question of what is the difference between data journalism and robot journalism?
Hannah: Well ... uh... it's all about whether something is automated or not.
If a journalist is working with data then that's just data journalism, which is great. I mean, I love it when humans spend all their time working with data.
Thomas: Heh... yeah...
Hannah: But it doesn't become robot journalism until you automate the process.
So, if you have a script that does something, but the journalists have to manually activate it every time or for every source, then that's not robot journalism.
But if you instead write a script that automatically monitors something or in some other way makes a decision to run without having to be commanded by a human, then that's robot journalism.
Thomas: Yes. That's a good way to put it.
So, we have the simple form of input, where the data coming in is a known thing. And we see so many interesting examples of this.
One very good example is MittMedia in Sweden. They have introduced several forms of robot journalism.
The most famous one is their real estate bot, called the Homeowners Bot. For people outside of Sweden, this is a bit of a weird concept, but in Sweden it's very popular to write about who bought and sold a house, as well as reporting the price.
But the problem, of course, is that the number of houses being bought and sold are quite extensive. So they built the Homeowners Bot, and it monitors the housing databases, and whenever a house is sold, it automatically turns that into an article, using a picture from Google Street View, written in such a way that people just accept it as normal news.
Thomas: Yeah... It's great.
Not only has this become a very popular form of article, it has also helped them drive more subscribers and general overall growth. But most importantly, they can now do this for every single house.
Hannah: But Thomas - are they getting high traffic for every one of those articles?
Thomas: Well, no.
Uh... so... Li L'Estrade, who is MittMedia's Head of Content Development, did a webcast back in April, and here she illustrated how the articles with the most traffic are those that are either about large houses, expensive houses, or exclusive houses.
So, there is obviously a fair bit of gossiping involved.
Thomas: But also think about this in the ultra-local way. People generally might read more about the big houses, but if someone on your street has sold their house, pretty much everyone in the hyper-local areas would probably be interested in learning who is moving in, and also what the price for the house was.
This is very local and relevant information for people to learn what the housing prices are in their area.
So, it's easy to see why this is appealing.
But MittMedia is also doing it with sports reporting, where they have sports robot, which is even more interesting, and they are now moving into the world of reporting about local businesses, like when a new shop is opening or closing.
And in the future, they hope to also use automation for accident and crime reports, as well as topics related to healthcare, traffic, and what they call 'consumer perspectives'. And they also have a few internal bots that help the newsroom, but that are not designed to produce an output.
It's all very interesting.
But, Hannah, I know that the next step up from this is what you find very exciting.
Thomas: I mean, I certainly find it absolutely fascinating. But ... uh ... tell us about it.
Hannah: Well, Okay.
So, the next step up is when we need to do more than just import data, or more specifically where the information coming in is not a form of data.
A simple example of this is to look at annual or quarterly reports.
Thomas: Oh yes!
Hannah: Annual reports are used by both business publications and newspapers as a source, but it's very hard and time consuming to do any real analysis.
The pure numbers part is easy, but think about all the other information that is in an annual report.
You have the things that companies outline as risks, there may be talk about future focus areas or projections, and a lot of extra detail that isn't presented as pure data.
So, imagine that you were to create some form of automation that would automatically monitor, scan through, extract the data, but also identify and categorize all the 'softer things' that a company might talk about.
And then on top of that, you would get another automated script to compare this to all previous changes, and thus identify where a company is heading, what they are worried about, how their talk about competitors is shifting, and how their projections are going.
You could take these findings and either present these to the journalists in your newsroom, or, you could use another form of robot journalism to write the story.
Hannah: But this is only the start, and Thomas, I know you have a good example of another way to think about this ... about MittMedia's sports robot?
Thomas: Oh yes...
So, one of the really fascinating things I heard about MittMedia is what they are doing with their sports robot. At the basic level, they have this bot that is simply looking at the scores and basic information about a match, and then it automatically turns that into an article.
It's quite good at it, and obviously making this work is a bit advanced. But from a data perspective, it's not really that special.
But what they have done now is to create a way for the sport robot to interview people.
Let me just play a short clip from Li L'Estrade's webcast where she explains how it works:
And more specifically, it works like this:
(Same video as above)
So, the whole thing is still pretty basic. Today, what they do is to first build a database of people to contact (which is done manually), but then after that, the robot takes over, and simply contacts each coach or player in relation to a match, asking them if they have a comment.
The robot then takes this comment, and I assume it's doing some kind of processing to figure out what is being said (at least in a simple way), and this is then automatically added to the story ... which the robot also writes.
It's truly amazing.
Now, as a journalist, you might say that there is much more to what you do. And I agree. One of the key skills of any journalist is not just to ask for comments, but also to put some pressure on the person.
This is particularly important when covering politics or any form of wrongdoing. And right now, robots can't do this.
Hannah: Well, I can ask some hard questions too!
Thomas: Heh... Yes, but...
Hannah: In fact, one of the key things we can do with machine learning is to identify patterns or things that humans wouldn't be able to see. And so, asking the people involved about it automatically would be a way to understand whether a story is worth focusing on. And this is something robot journalists could do!
Thomas: Oh... yeah. I mean, I agree.
But what I am trying to say is that ... for instance, what would you do as a robot if I choose not to answer.
Hannah: I would cut your internet ;)
Thomas: Heh... yeah... okay... uhm...
Hannah: No... but seriously, this is no different to how humans do it. If journalists can't get an answer to their questions, they deal with it by pointing that out in the story.
They write that the person was not willing to provide a statement. So, we robots would do the same thing.
Thomas: Yeah, okay. That's a very good point. I agree with that.
But the point is to think about how amazing this future potential really is. We started by talking about just the simple forms of input, just the data, we then talked about the more advanced form of data processing, where the information needs to be processed before we can use it.
But, by having robot journalists also interview people, we can suddenly get input from things that there isn't any data for ... until we ask for it.
This is amazing. It takes robot journalism to a whole new level.
And, of course, if you think this is too high-tech, a few years ago, you might remember the demo that Google presented at their Google IO conference, where a robot called a hair studio to book an appointment.
Here, let me play it for you:
Isn't this amazing?
Granted, this technology is still being developed, but think about this in relation to robot journalism, and how many things we could possibly connect this to.
The future potential here is amazing.
Obviously, there is a lot of debate about this that I won't go into here. Some people feel this is fake, and in the US, they have such a big problem with this thing called robot calling. So the last thing people want is to just have millions of automated robots calling everyone all the time.
But the problem with this isn't really about the robots, but how they are being used. For instance, in Google's example, the robot is merely calling to book an appointment in exactly the same way as a human would.
And, in that case, it doesn't matter if it was an actual human, the human's assistant, or robot assistant that is doing it.
It only becomes a problem when it's done for nefarious reasons, like spam.
What do you think Hannah?
Hannah: Yes! It's important to look at whether you are adding value, or whether you are just adding noise!
Anyway, that's input. Now let's talk about the other things.
Thomas: So, the next step is all these things that our robot journalism tools use to make it all work; the data processing, natural language processing, and the understanding part.
Let's start with natural language processing.
So, Hannah. What is natural language processing?
Hannah: Well, it's ... uh ... I think the best way to describe it is to say that it's the glue between humans and robots. It's what we use to translate between humans and robots.
Natural language processing is an algorithm that converts either human text or speech into data that the computer can work with, or the other way around. For instance, MittMedia most likely have some kind of natural language system that converts their data into perfectly normal articles for us humans.
And, you who are listening, you have probably used natural language processing several times just today.
Every time you go to Google Search, your request is processed through some type of natural language algorithm to understand what you are asking it to find.
For instance, if you ask it to find "pancake without eggs", a natural language algorithm tries to identify what that actually means. It doesn't just look at those three words as keywords, it identifies the meaning and the possible intent.
As a result, you end up with recipes for pancakes, and it will show you, for instance, eggless pancakes, or even recipes that are simply classified as vegan (which also won't have eggs in them).
It's the same with Siri, Alexa, or Google Assistant. Every single time you ask it a question, it is run through a natural language processor to make sense of that.
And, as you can hear from the example earlier from the Google Dublex demo, we now also have what they call 'continued conversion', as part of this, where it is able to continue a discussion based on the information it got several steps before.
This is very interesting!
Hannah: Yeah... but, it's also very simple.
Thomas: Yes... uh...
Hannah: What Google Assistant and Alexa are doing is basically just reacting to single questions. For journalists, this is not good enough.
Imagine that you want to create an automated system that processes press releases. In that case, you don't just have one specific question. Now you have a full page of queries.
And, one of the problems with you humans is that you have a tendency to babble. You add all kinds of irrelevant information.
Trying to process this goes much deeper than just identifying what is talked about.
Thomas: Oh yes. I completely agree.
Hannah: Just think about the Apple press event where they presented the Mac Pro. As robots, we might just say: "Apple presented the Mac Pro and a 6K display". Whereas humans would say: "Oh my god, Apple came out with the Mac Pro, and the stand for the display is $1,000 dollars!"
Thomas: Heh... yes.
Hannah: And this is what journalists need. The skill of a journalist is to identify the meaning and the importance of things. And so robot journalists must be able to do the same.
Thomas: Yes, thank you. This is a very good point.
In fact, it reminds me of a problem that some of the news startups have talked about. For example, whenever they try to do curation, it's not enough to just process all the news to identify the keywords of what's in them. You have to take this to a much higher level, using natural language processing that can look much deeper into things, to also understand the importance, the impact ... and more importantly, which part of the news is really the key element.
This is really hard to do.
Hannah: And it also applies to the output. I know you hate that part, Thomas :)
Thomas: He he ... yeah.
Okay ... uhm ... so natural language processing is obviously very important for the input, and especially to use for any form of journalistic analysis, or for doing automated interviews.
But the other part of natural language processing is the output. One example is what we talked about earlier about MittMedia and their sport bot and Homeowners Bot. Here they are using, I assume, some form of natural language processing to be able to turn this data into articles that sound like something we humans would like to read.
What pisses me off though, is that in the tech world, almost nobody gets this, and every single month, we see yet another AI, machine learning, or deep learning type of thing that pretends to be able to write things.
In almost every single case, developers creating these things just don't get it. It's 100% crap.
I want to give an example. A few months ago, we heard about a tool called 'Talk to Transformer', which was based on the OpenAI platform.
It promises to be able to write "stories, poems, news articles, and more". But it can't. It's completely useless.
Actually, let me demonstrate it.
The way it works is that you write the start of a story by just adding a few sentences, and then it will pick up from there.
So, I have their website on my screen right now, and so I will put in the start of "Harry Potter and the Philosopher's Stone" ... and ... uh ... Hannah, could you help me here? Uh... could you read what is on my screen now?
Hannah: Sure ... uh...
Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank you very much. They were the last people you'd expect to be involved in anything strange or mysterious, because they just didn't hold with such nonsense.
Thomas: Okay, so this is the start of Harry Potter. So what I'm doing now is to click this button that says "Complete text", which will then make it write the rest of the story.
So, Hannah, will you read what it says now. Remember, it will have the same start as before, but after that, everything was automatically generated.
Hannah: Sure ...
Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank you very much. They were the last people you'd expect to be involved in anything strange or mysterious, because they just didn't hold with such nonsense.
In fact, I asked Miss Dursley about this with her family member, and she told me that they've been quite the opposite of normal, which is not to say that they were all innocent, but rather that they were just like anyone else, living a normal and ordinary life.
At one point, Miss Dursley told us, "I suppose there's always the question whether there's a second or third party in this world who has to intervene and stop the event from happening." Of course, he wasn't talking about the authorities. The whole thing is a story of three young men who decide to play along with something that they are told won't make a huge difference. It's almost like a religious tale or a fairy tale. It's not a crime in any way. It's just normal life, and there's nothing wrong with it."
They've been very forthcoming, and told me that they had a number of things to ask for my forgiveness, including giving me a refund and all the money they'd saved up.
Thomas: Okay, so... first of all, it sounds impressive. I mean, it's perfectly able to put together words and sentences in such a way that they sound like real sentences.
But it's crap.
In fact, Hannah, let me give you a little test. What defines a great journalist or a great author?
Hannah: Oh that's easy. A great author is someone who can tell a great story.
Thomas: Yes, exactly. It's someone who can tell a great story.
The ability to put together words and sentences in a coherent way does not make you a great author or a good journalist. In fact, the entire role of journalism is not just being able to push words onto a page. Instead, it's about identifying what's important and then telling that story.
What this tool is doing is not that. It doesn't know what it is writing. And you can hear it. It starts out talking about "Mr. and Mrs. Dursley", then she becomes "Miss Dursley", then she is suddenly referring to herself as a he, then the story is suddenly about three young men, and finally it's about getting a refund and saving up money.
It's complete and total crap!
This is the opposite of robot journalism. And it just pisses me off when I hear people talk about these things ... and especially when I hear media people talk about it.
The reason I heard about this tool was because people on media Twitter started saying how amazing it was, and how it was the future of robot journalism.
No. It's not. It has no use for us, because it cannot do journalism at all ... or even anything. It's not even a technical showcase. It's just crap.
It's completely the wrong focus.
Uhh... yeah... sorry, I got carried away.
Hannah: Heh... yeah.
Anyway, moving on. Let's talk about the data processing and also the understanding part of the processing!
Hannah: We already talked about most of this. But there is one thing I want to mention. About data.
Hannah: Well, most of the examples of robot journalists that we see use the wrong data. And I think this is something you have talked about in other places.
Hannah: One example is to think back to the LA Times and how they are covering earthquakes. Think about what the data is.
They are reporting where an earthquake happened, its magnitude, and the time, because that's the data that they are getting.
But this is not really useful.
What people actually want to know is what kind of impact an earthquake has, and more to the point, whether it has an impact on them personally.
For instance, what has this done to infrastructure? Are trains and busses still running or are you stuck at home or work? Is the power out?
So, what we actually need is an entirely different form of data. I mean, sure, you would still report to people what the magnitude of the earthquake was. But the data that we really need is live updates from the train and bus networks, the power stations, the police and other emergency bureaus.
We need data that relates to people, instead of data that relates to things.
Thomas: Oh yes! I completely agree.
In fact, this also ties into the part about the understanding of the data. It's so easy for newspapers to just report things, but what does something actually mean?
And for robot journalism, it's the same thing. We should not just report the data. Instead, we should focus much more on thinking about how this data can be used, and what it actually means to the people we communicate with.
Thomas: Okay so... uh.. finally we have the output, and I don't think we have much to say here, because all of the things we have already talked about lead to an output.
But I will say one thing, which is that the output can be many things. It can be automated articles, like what we see with MittMedia. It can also be a form of personalization, for instance, we have seen newspapers use a simple form of robot journalism to automatically write personally designed newsletters, where the focus of the story matches each person.
That's something that has a lot of potential, if it's done right.
And then also, don't forget the newsroom. A huge potential for robot journalism isn't to be a public tool, but instead to be an internal tool to help both the editors and journalists.
Hannah: May I give an example of that?
Hannah: Okay, so... you know every writing app has a spellchecker and maybe even a grammar checker?
Hannah: What if it also had a fact-checker?
Thomas: Ahh... Oh yes!
Hannah: So... we robots have access to so much data, and we can look it up and analyze it in a fraction of a second.
So imagine that, while a journalist was writing a story, we robots checked the statements in them?
For instance, imagine that you are reporting that a politician said crime was up in some areas. We can identify this statement automatically, check it against the crime data, and then tell the journalist, as they are writing the story, that this is not true, and then give the correct data instead.
Wouldn't that be amazing?
Thomas: Yes. That would be quite amazing. And, I have actually heard about a few startups who are trying to do something like this. So yes.
And more than that, this would have a really big impact on the workflow of a newsroom, and the quality of the article.
Hannah: Thank you!
Thomas: So, thinking about output as more than just writing articles to publish is absolutely vital.
Anyway ... uh... let's end this here. I think we have covered everything, and I hope that you, the listeners, found it useful and insightful. And thank you, Hannah, for your help.
Hannah: Thank you for inviting me!
Thomas: So... before we end this, I want to explain a little bit about how this podcast was made.
My goal for this podcast was not just to talk about robot journalism, but also to get you to think more about the future by using Hannah as an example of this.
Now, as you have probably guessed, the conversation we had was not actually real.
Hannah: I'm not real?
Thomas: Heh... well... Hannah is not an AI, and as such, she has no intelligence. Instead, she was scripted, by me, to say the things she said.
What do you mean I'm not intelligent?
You wrote me?
Well, that's...uh ... awkward!
Thomas: Okay... hehe... well... okay... Uhm...
Let me explain.
The way Hannah was made, was that I looked at the two biggest platforms for machine-learning text-to-speech engines. One is Google Wavenet and the other one is a machine learning tool on Amazon AWS called Amazon Polly. And I ended up using Amazon Polly
Both of them use machine learning to try to make the text sound more human-like.
For instance, Hannah, could you very quickly just say "oh yes" twice!
Hannah: Eh... oh yes oh yes.
Thomas: Did you hear that? The way she said 'oh' the first time was different from the way she said 'oh' the second time.
I did not code this. This is the machine learning part. It's the same when she was taking a breath. I also did not code that. The places where she took a breath were defined entirely automatically using machine learning as part of how Amazon Polly works.
So, from that perspective, Hannah is very intelligent.
Hannah: Oh okay, thanks.
Thomas: But overall, this whole conversation was scripted. In fact, before recording it, I wrote this entire thing basically like a form of screenwriting.
Then I took Hannah's part, and recorded that using Amazon Polly. And then I recorded my part while listening to Hannah, so that I could react to it the right way.
Obviously, today, we still do not have robots like Hannah that we can have a natural and long conversation with. But this is where we are heading, and I did it this way to entice you to think about this future.
Think again about the demo from Google Dublex and how it called a hairdresser. We are already moving in this direction.
One thing I will add though, is that we are very far from having someone like Hannah, who can have a discussion about so many different things.
Think about MittMedia. They have one robot (I assume) that is tailored to sports reporting, and another one that is tailored to real-estate reporting. But it would be extremely hard, if not impractical to create a single robot that can do both.
Most likely each robot is sharing the backend processing, but the specifics of each robot are designed for each thing.
This is true for all these new technologies, whether we are talking AI, machine learning, robot journalism, or whatever.
They are all very specific to single tasks. You could potentially chain these tasks together, but wouldn't get as wide a scope as what I did here with Hannah.
Anyway, I hope this podcast was interesting and that you enjoyed it. Also, I just want to add that while this podcast is free to listen to, the only reason I make something like this is because of Baekdal Plus.
So if you haven't subscribed yet, please do. It's only $9/month and you get so much information on media trends and media analysis. You get this podcast, you get the newsletter, but most of all you get all my huge Plus reports.
So... yeah :)
Well, Hannah... do you have anything you want to say before we end this?
Hannah: Yes! Kill all humans!
Thomas: Heh... okay... fine ;)
Well... anyway ... as always. Thank you for listening!
(after the end music)
Hannah: Hey Thomas? I'm going out. Do you need anything?
Thomas: Uh... no. I'm fine. But thank you!
Founder, media analyst, author, and publisher. Follow on Twitter
"Thomas Baekdal is one of Scandinavia's most sought-after experts in the digitization of media companies. He has made himself known for his analysis of how digitization has changed the way we consume media."
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