Monthly Archives: January 2021

GameStop on Twitter: a quick squiz at the short squeeze

GameStop the press!

Remember GameStop? You know, the video game retailer whose decaying share price exploded after a bunch of Reddit users bought its stock and succeeded in bankrupting a hedge fund who was trying to short it? Yeah, that was nearly a week ago now, so my memory of it is getting hazy. I mostly remember all the explainers about how the share market works and what a short squeeze is. And the thought pieces about how this kind of coordinated market behaviour is nothing criminal, just ordinary folk playing the big boys at their own game and finally winning. And the memes: who can forget the memes? Well, me, for a start.

Somewhere amid the madness, I decided that I should harvest some Twitter data about this so-called GameStop saga (can something really only be a saga after only three days?) to capture the moment, and to see whose hot takes and snide remarks were winning the day in this thriving online marketplace of shotposts and brainfarts.

I confess that I had another motive for doing this as well, which was to provide some fodder for my TweetKollidR workflow, which turns Twitter datasets into pretty and informative pictures. The TweetKollidR is a workflow for the KNIME Analtyics Platform that I developed while locked down for three months in the latter half of 2020. I’ve made the workflow publicly available on the KNIME Hub, but it is still in need of road-testing, having been used (by me, at least) to analyse only two issues — the Covid-19 lockdown that spurred its genesis, and the wearisome public discourse about Australia Day. I felt that it was time to test the workflow on an issue that was not so close to home.

So, using the TweetKollidR workflow to connect to Twitter’s Search API, 1 I collected just over 50,000 tweets containing the terms gamestop or game stop. Because I am not paying for premium access to the API, I was only able to grab tweets that were made within about 24 hours of the search (usually you can go back in time up to a week, but the sheer volume of activity around this topic might have shortened the window offered by the API). The 50,000 tweets in the dataset therefore cover just two days, namely 28 and 29 January 2021.

Let’s take a squiz! (By which, for the non-Australians among you, I mean a look or glance, esp an inquisitive one.) Continue reading GameStop on Twitter: a quick squiz at the short squeeze


  1. API stands for application programming interface, which is essentially a protocol by which content can be requested and supplied in a machine-readable format, rather than as eye candy.

The day after the week before: mapping the Twitter discourse about Australia day

An ode to the 27th of January

The 27th of January is an important day in the Australian calendar. As the fog rises from the Christmas break and the last public holiday for at least eight weeks, this date marks the resumption of business as usual, the start of the new year proper. It is also the moment when millions of Australians breathe a sigh of relief, knowing that the divisive and tiresome debate about the date of Australia Day will now subside for another 358 days, give or take.

The 27th of January is the day after the day when, in 1788, Captain Arthur Phillip sailed into into Sydney Cove and planted a British flag. Trailing behind him was a fleet of 11 ships carrying an assortment of convicts, civil officers and free settlers, the first members of a new colonial outpost that would ultimately become the nation state of Australia. Watching the arrival from the shore were the land’s indigenous human inhabitants, custodians of more than 40,000 years of continuous culture and occupation.

Long recognised as the anniversary of the colony’s foundation, the day before the 27th of January was in 1935 adopted by all states and territories as Australia’s national day of celebration. For many years, most Australians were happy with this arrangement. Australia Day was a day of national pride and innocent celebration, a day to have a barbecue, drink some beer, listen to the Hottest 100 countdown and play some beach or backyard cricket — often all at the same time. But now, as the country has finally began to confront some of the darker chapters of its colonial past, the 26th of January is losing its lustre as a day when such simple pleasures can be enjoyed, let alone pursued in the name of national pride. It turns out that it is rather difficult to drink beer, play cricket and enjoy the last year’s top songs while at the same time contemplating the country’s legacy of dispossession and genocide against its first peoples. (Indeed, Triple J moved its Hottest 100 countdown from Australia Day to the fourth weekend of January in 2018, and this year Cricket Australia¬† chose not to mention Australia Day in its promotion of matches held on 26 January.)

And so, in the third decade of the 20th century, the 27th of January is now the day after tens of thousands of people partake in Invasion Day rallies to plead for meaningful reconciliation and to advocate changing the date of the national day, or to abolish it altogether. The 27th is the day after a day of exasperated commentary about the recipients of Australia Day honours, which in 2015 included Prince Phillip, inexplicably knighted by the then prime minister, Toby Abbott; and which this year included Margaret Court, whose legacy as one of the greatest ever tennis players has in recent times been overshadowed by her outspoken and controversial views about homosexuality, gay marriage and transgender people. In short, the 27th of January is the day after a wave of difficult, awkward, and at times ugly public debate peaks and subsides. Until next year. Continue reading The day after the week before: mapping the Twitter discourse about Australia day

TextKleaner – a Knime workflow for preparing large text datasets for analysis

This post describes the motivation for the TextKleaner workflow and provides instructions on how to use it. You can obtain the TextKleaner workflow from the KNIME Hub.

Confronting the first law of text analytics

The computational analysis of text — or text analytics for short — is a field that has come into its own in recent years. While computational tools for analysing text have been around for decades — the first notable example being The General Inquirer, developed in the 1960s — the need for such tools has become greater as the amount of textual data that permeates everyday life has increased. Websites, social media and other digital communication technologies have created vast and ever-expanding repositories of text, recording all kinds of human interactions. Meanwhile, more and more texts from previous eras are finding their way into digital form. While all kinds of scholarly, commercial and creative rewards await those who can make sense of this wealth of data, its sheer volume means that it cannot be comprehended in the old fashioned way (otherwise known as reading). Just as computers are largely responsible for generating and transmitting this data, they are indispensable for managing and understanding it.

Thankfully, computers and the people who program them have both risen to the challenge of grappling with Big Text. As computers have become more and more powerful, the ways in which we use them have become more and more sophisticated. No longer a synonym for glorified word counting, computational text analysis now includes or intersects with fields such as natural language processing (NLP), machine learning and artificial intelligence. Simple formulas that compare word frequencies now work alongside complex algorithms that parse sentences, recognise names, detect sentiments, classify topics, and even compose original texts.

Such technologies are not the sole domain of tech giants and elite scientists, even if they do sit at the core of digital mega-infrastructures such as Google’s search engine or Amazon’s hivemind of personal assistants. Many of them are available as free or open-source software libraries, accessible to anyone with a well-specced laptop and a basic competence in computer science.

And yet, no matter how powerful and accessible these tools are, they have not altered a fact that, in my opinion, could be enshrined as the first law of text analytics — namely, that analysing text with computers is really hard. Continue reading TextKleaner – a Knime workflow for preparing large text datasets for analysis