So I'm they are not I didn't have work as a postdoctoral research fellow researcher at the Oxford Internet Institute. And so I work in the Computational Propaganda Project, which is headed by Professor Phil Howard. So we're a group of political scientists, media scholars and computer scientists say training. So it's a highly interdisciplinary project and we track the spread of fake news, misinformation, disinformation on social media.
So I think I would like to describe my work as mapping the impact of A.I. on our democratic processes. So computational propaganda is within the project. We have a working definition of computational propaganda. So this is about how algorithms and the affordances of social media platforms come together to enable malicious actors to spread fake news and to influence public opinion,
manipulate elections. So you would notice that I've put down jump use here instead of fake news, and this was a deliberate attempt to move away from the term fake news because it was being weaponized and being used by authoritarian figures to to attack publications, mainstream media that were critical of government policy and so on.
So when when we first started this project, we were looking, we were looking after Twitter, so we would track politically relevant hashtags on Twitter as we would study elections around the world. We started with, I think, us 2016, the presidential elections that, you know some of you might have heard about. So, you know, under Brexit. So Brexit before Brexit and 2016 elections.
So we would track politically relevant hashtags on Twitter extract to extract tweets that were posted with these with these hashtags and then extract links to news sources from these tweets. And then we had a classification classification scheme. And we would try to separate our professional news sources from more problematic use. So, I mean, there was nothing that, you know, we were not using advanced air.
This was all done. It was just we use it mainly to track the spread of problematic new sources of junk users on social media platforms. But but just so you know, we would classify them into professional news sources and junk news sources. Then we would track the spread of job sources on social media platforms, and we had a detailed we had five criteria to determine if a news source was in fact problematic. So this would be, you know, professionalism if adapted to professional standards.
If there were open about editorial policy, who their funders were science. So this could include, you know, ad hominem attacks, excessive use of capitalisation, the type of stuff, you know, the type of sensationalism and so on. And then you have credibility and then bias that could be hyperpartisan. You could, you know, you have these screaming news headlines purporting to be news, but it's actually commentary and so on.
And then to come to feed new sources, they would mimic the branding and other features more well established news organisations. So if they fail, if a new source fail, three of these five criteria that we would classify them as junk news sources and then not and then find out in our sample what percentage of news sources were junk professional and so on. So this served us quite well when we were studying Western democracies.
But when we move to Latin America in 2017, you know, 2000 18 and then ended early 2019. Things change fairly dramatically. First of all, I think, you know, it coincided with the rise of encrypted platforms like in messaging apps like WhatsApp. So people. So I think, you know, that is it speaks to different cultures of internet use and so on. So platforms like WhatsApp afforded a level of privacy that this absent and not absent on Twitter, Facebook and so on.
We saw that we saw these apps grow in popularity, both in Brazil, particularly in Brazil and in India, and these were being used by political actors to directly to bypass traditional filters direct. You reach water populations. And with the rise of these messaging apps sort of need to to use dubious new sources to push propaganda out reduced and people are increasingly using visual media.
For instance, names, images and data and videos to to convey that to reach audience groups and to manipulate voters. So for the first time when we studied the Brazilian election, we found very low levels of junk use on Twitter using our classifications and so on. And of course, that doesn't mean that there was no junk use in the Brazilian elections. It simply meant that it had migrated to other platforms.
So, of course, that there are a number of issues with accessing information with that same data on what's meant to be an encrypted platform. So we had to actually ask to join these groups. So we rejoined these. First of all, they had to be public WhatsApp groups, which meant that we could only join using a link that was published on the on the web.
And then once we joined these groups, we had to announce that we had researchers and we had to be very upfront about that and seek that consent of consent before using using that data. And of course, we've had run ins with WhatsApp, and they would question if if this was ethical because it was designed as safe as a private, encrypted platform.
And they would also question whether this was representative perfectly valid questions, except that, you know, WhatsApp, that they knew very well that we had no other way of accessing the data. And, you know, we were trying to shed light on what was going going on within these groups that. But that raises some very significant ethical questions about, you know, privacy and data protection. And how do we and whether researchers have an automatic right?
I mean, in the interests of serving public good and so on to access the sector. But coming back to this before we were because we saw an increasing amount of propaganda taking the shape of means and images, we developed different visual typology to to classify the kind of material that we were seeing propaganda material. So because that was the standard campaign stuff that was satire, religion played a big part that was junk, you know, similarly styled credibility.
But crucially, there was no more this distinction between professional versus non-professional or dubious news sources. Because, you know, this was this was moving away from attribution. People did know that was not from a professional news source, this particular image or meme, and that was part of the appeal.
It was still talking about was making that, you know, it was either critiquing policy decisions, it was making fun of politicians and they seemed to strike a chord with audience groups, although people knew that it was not professionally produced. So our entire definition of junk news versus professional use and our methodology had to be adapted to be able to describe what was meant as propaganda and this in this context.
Yeah. And so in the Brazilian context, again, we saw a lot of hate gold and porn, and that raised a whole different set of questions ethical questions about what do we do when we see, you know, say, call to action within a WhatsApp group? What is our responsibility as far as researchers? I mean, we've promised not to intervene, but if we if we thought that there was actually some danger to a group or to an individual, what do we do in that case and so on?
And also, you know, our responsibility to our own research students and other researchers because they would be exposed to fairly dire material on social media and how would it impact their well-being? So these are some of the questions that we have to grapple with when we were doing this research. So in the Indian context, of course, there are research period coincided with the Pulwama attack. The Indian Pakistan almost were on the brink of war and so on the exchange of hostilities.
So, you know, nationalism and support for armed forces, we saw a whole lot of pictures celebrating the Indian Army and patriotism and so on. And so so both. So the key takeaway for us as a research group was that propaganda had shifted from from open platforms to more encrypted platforms like WhatsApp and from using attribution and dubious new sources to spread. Gander malicious actors were increasingly using means and images and visual media to influence to reach audience groups.
So into this mix, OK, so I could skip this. This is just WhatsApp. These are just some results from the Indian elections. So into this week. So it's already it's already tricky to classify these, these images and so on and then a whole host of ethical questions already. But then, you know, I think some some of our speakers have already referred to generated adversarial networks and deepfakes and so on.
And you know, the potential that they could have on future misinformation disinformation campaigns was what was beginning to exercise our interest. And because, you know, my work above, I've done some work on reinforcement learning and so on. This was that this was something that was of huge interest to me personally as a researcher as well. So I mean, we saw we saw Katrina talking about GP2, for instance.
And you know, you have speech to text, you have image to image, you have you have a whole host of advanced A.I. techniques that good that can generate misinformation and disinformation and in the wrong hands, one could only imagine the impact that would have on future elections. And in fact, in our even in the recent UK elections, we saw future advocacy produce videos of Boris Johnson and and Jeremy Corbyn each endorsing the other just to demonstrate the power of these these technologies.
So, yeah, of course, this was researchers. The intention is not to generate misinformation, but it's to advance the field of A.I. itself and to endow computers with an intrinsic understanding of the world, which is what I suppose is all about it. And if you can generate new datasets from existing datasets, that means your reliance on data itself produces. So this is an example of a progressive Ghana's as they're and this company, Nvidia in the Silicon Valley managed to perfect this technique.
So this is a completely Computer-Generated image, and it's indistinguishable from a human image. And it's it's not possible to detect that it's it's an art. It's an artificially generated image using, you know, current techniques because by definition, it is an image. It's just not a real person. So. So yeah, it completely alters our perception of reality, particularly amongst groups that are that are vulnerable, that have come that have little or no technical know how so.
So it process a lot of questions for us as a society, and we do identify if there are groups that are more vulnerable to this kind of generated misinformation and what is as as stakeholders, what can we do about research as well as governments and the Big Tech companies? So with that, I'd like to continue. Thank you. Very.