I love how people are using data and data science to fight fake news these days (see also Identifying Dirty Twitter Bots), and I recently came across another great example.
Conspirador Norteño (real name unkown) is a member of what they call #TheResistance. It’s a group of data scientists discovering and analyzing so-called botnets – networks of artificial accounts on social media websites, like Twitter.
TheResistance uses quantitative analysis to unveil large groups of fake accounts, spreading potential fake news, or fake-endorsing the (fake) news spread by others.
They looked at the date of these accounts started following Shiva, offset by the date of their accounts’ creation. A remarkeable pattern appeared:
Although @va_shiva‘s recent followers look unremarkable, a significant majority of his first 5000 followers appear to have been created in batches and to have subsequently followed @va_shiva in rapid succession.
Looking at those followers in more detail, other suspicious patterns emerge. Their names follow a same pattern, they have an about equal amount of followers, followings, tweets, and (no) likes. Moreover, they were created only seconds apart. Many of them seem to follow each other as well.
If that wasn’t enough proof of something’s off, here’s a variety of their tweets… Not really what everyday folks would tweet right? Plus similar patterns again across acounts.
At first, I thought, so what? This Shiva guy probably just set up some automated (Python?) scripts to make Twitter account and follow him. Good for him. It worked out, as his most recent 10k followers followed him organically.
However, it becomes more scary if you notice this Shiva guy is (succesfully) promoting the firing of people working for the government:
Anyways, wanted to share this simple though cool approach to finding bots & fake news networks on social media. I hope you liked it, and would love to hear your thoughts in the comments!
Unsurprisingly, nouns like asylum, repatriation, displacement, persecution, plight, and crisis appear significantly more often in UNHCR speeches on refugees than in general English texts. The first visualization below shows the action-oriented verbs most commonly used in combination with these nouns.
This second visualization shows the most occurring verb-noun pairs.
Hannah used newsflash to retrieve the GDELT data on US TV news. Some channels seem to cover refugees more than others. I would have loved to see which topics occurred on each channel, but unfortunately she did not report on this.
Andrew Thompson was interested in what 10 topics a computer would identify in our daily news. He gathered over 140.000 new articles from the archives of 10 different sources, as you can see in the figure below.
In Python, Andrew converted the text of all these articles into a manageable form (tf-idf document term matrix (see also Harry Plotter: Part 2)), reduced these data to 100 dimensions using latent semantic analysis (singular value decomposition), and ran a k-means clustering to retrieve the 10 main clusters. I included his main results below, but I highly suggest you visit the original article on Medium as Andrew used Plotly to generate interactive plots!
The topics structure seems quite nice! Topic 0 involves legal issues, such as immigration, whereas topic 1 seems to be more about politics. Topic 8 is clearly sports whereas 9 is education. Next, Andres inspected which media outlet covers which topics most. Again, visit the original article for interactive plots!
In light of the fake news crisis and the developments in (internet) media, I believe Andrew’s conclusions on these data are quite interesting.
I suppose different people could interpret this data and these graphs differently, but I interpret them as the following: when forced into groups, the publications sort into Reuters and everything else.
Every publication in this dataset except Reuters shares some common denominators. They’re entirely funded on ads and/or subscriptions (Vox and BuzzFeed also have VC funding, but they’re ad-based models), and their existence relies on clicks. By contrast, Reuters’s news product is merely the public face of a massive information conglomerate. Perhaps more importantly, it’s a news wire whose coverage includes deep reporting on the affairs of our financial universe, and therefore is charged with a different mandate than the others — arguably more than the New York Times, it must cover all the news, without getting trapped in the character driven reality-TV spectacle that every other citizen of the dataset appears to so heavily relish in doing. Of them all, its voice tends to maintain the most moderate indoor volume, and no single global event provokes larger-than-life outrage, if outrage can be provoked from Reuters at all. Perhaps this is the product of belonging to the financial press and analyzing the world macroscopically; the narrative of the non-financial press fails to accord equal weight to a change in the LIBOR rate and to the policy proposals of a madman, even though it arguably should. Every other publication here seems to bear intimations of utopia, and the subtext of their content is often that a perfect world would materialize if we mixed the right ingredients in the recipe book, and that the thing you’re outraged about is actually the thing standing between us and paradise. In my experience as a reader, I’ve never felt anything of the sort emanate from Reuters.
This should not be interpreted as asserting that the New York Times and Breitbart are therefore identical cauldrons of apoplexy. I read a beautifully designed piece today in the Times about just how common bioluminescence is among deep sea creatures. It goes without saying that the prospect of finding a piece like that in Breitbart is nonexistent, which is one of the things I find so god damned sad about that territory of the political spectrum, as well as in its diametrical opponents a la Talking Points Memo. But this is the whole point: show an algorithm the number of stories you write about deep sea creatures and it’ll show you who you are. At a finer resolution, we would probably find a chasm between the Times and Fox News, or between NPR and the New York Post. See that third cluster up there, where all the words are kind of compressed with lower TfIdf values and nothing sticks out? It’s actually a whole jungle of other topics, and you can run the algorithm on just that cluster and get new groups and distinctions — and one of those clusters will also be a compression of different kinds of stories, and you can do this over and over in a fractal of machine learning. The distinction here is not the only one, but it is, from the aerial perspective of data, the first.
It would be really interesting to see whether more high-quality media outlets, like the New York Times, could be easily distinguished from more sensational outlets, such as Buzzfeed, when more clusters were used, or potentially other text analytics methodology, like latent Dirichlet allocation.