Also involving Assoc. Prof. Dan Angus and Dr Tobias Keller, the team is currently investigating the origins and spread of major conspiracy theories associated with the COVID-19 crisis across various social media platforms. Such conspiracy theories include false stories about coronavirus as a bioweapon created either in a Wuhan lab or by researchers associated with the Gates Foundation, and about connections between coronavirus and the roll-out of 5G mobile telephony technology.

Early results from this research point to the presence of a small but sustained coordinated effort by a network of Twitter accounts that pushed the bioweapon conspiracy story; such accounts were often associated with fringe political perspectives especially in the United States. Further, the research indicates that these conspiracy theories typically spread beyond the fringes of public discussion only once they are picked up and amplified by tabloid media exploiting them as clickbait, or by celebrities from the fields of music, movies, and sports who share them with their substantial social media audiences.

The research, which will be presented in extended form in a report for the Centre for Responsible Technology and subsequent scholarly publications, points to important inflection points in the trajectory of conspiracy theories from the fringes to the mainstream, and highlights a need both for further platform intervention against coordinated inauthentic behaviour and for the development of greater digital literacies not least also amongst influential social media users.

In early 2020, we substantially updated the underlying ATNIX architecture. Most importantly, we now reduce the various URLs that may lead to the same story to a single canonical URL, in order to arrive at a reliable count for how often a story has been shared (rather than just how often a particular URL variation has been shared). This has become necessary because many news sites incorporate part of the story headline into its URL – but headlines may change after publication, and so multiple different URLs may point to the same story in the end.

ATNIX tracks the sharing of stories from most major Australian news outlets – from ABC News to New Matilda and beyond. We exclude international outlets with an Australian presence (such as The Guardian Australia or Mail Online Australia), because the majority of their content originates from outside of Australia, but we continue to include The Conversation because it remains Australian-based and sources a substantial amount of its content from Australian authors. The data gathered for ATNIX include all tweets, by Australian as well as international Twitter accounts, that link to the domains of these Australian news outlets. From these, we exclude links to their homepages as well as to non-news content.

The ATNIX Twitter account (@_ATNIX_) posts half-daily, daily, and weekly updates on trending Australian news stories, and ATNIX also provides an interactive dashboard with live and historical data on sharing patterns for Australian news, at and above. In earlier years, ATNIX analysis was published in a regular column in The Conversation (https://theconversation.com/columns/axel-bruns-1433).

The Australian Twitter News Index has reported on patterns in the sharing of Australian news content through Twitter for many years; it has documented the overall stability of Australian Twitter users’ preferences for specific news sources (especially ABC News and the Sydney Morning Herald), tested the overlap in content-sharing audiences between news outlets with different editorial and ideological orientations, and reported on the most popular news stories during specific timeframes.

Datasets analogous to ATNIX are also being collected for Germany, the Nordic countries, Spain, and a selection of suspected sources of mis- and disinformation.

Key scholarly discussions of ATNIX and its data can be found in:

Bruns, A. (2016). Big Data Analysis. In T. Witschge, C. W. Anderson, D. Domingo, & A. Hermida (Eds.), The Sage Handbook of Digital Journalism (pp. 509-527). Sage. https://eprints.qut.edu.au/102642/

Bruns, A. (2017). Making Audience Engagement Visible: Publics for Journalism on Social Media Platforms. In B. Franklin & S. A. Eldridge II (Eds.), The Routledge Companion to Digital Journalism Studies (pp. 325-334). Routledge. https://eprints.qut.edu.au/102644/

For further questions about ATNIX and its datasets, please contact the project leader, Prof. Axel Bruns.

Rafael Grohmann from the Brazilian blog DigiLabour has asked me to answer some questions about my recent work – and especially my new book Are Filter Bubbles Real?, which is out now from Polity –, and the Portuguese version of that interview has just been published. I thought I’d post the English-language answers here, too:

1. Why are the ‘filter bubble’ and ‘echo chamber’ metaphors so dumb?

The first problem is that they are only metaphors: the people who introduced them never bothered to properly define them. This means that these concepts might sound sensible, but that they mean everything and nothing. For example, what does it mean to be inside an filter bubble or echo chamber? Do you need to be completely cut off from the world around you, which seems to be what those metaphors suggest? Only in such extreme cases – which are perhaps similar to being in a cult that has completely disconnected from the rest of society – can the severe negative effects that the supporters of the echo chamber or filter bubble theories imagine actually become reality, because they assume that people in echo chambers or filter bubbles no longer see any content that disagrees with their political worldviews.

Now, such complete disconnection is not entirely impossible, but very difficult to achieve and maintain. And most of the empirical evidence we have points in the opposite direction. In particular, the immense success of extremist political propaganda (including ‘fake news’, another very problematic and poorly defined term) in the US, the UK, parts of Europe, and even in Brazil itself in recent years provides a very strong argument against echo chambers and filter bubbles: if we were all locked away in our own bubbles, disconnected from each other, then such content could not have travelled as far, and could not have affected as many people, as quickly as it appears to have done. Illiberal governments wouldn’t invest significant resources in outfits like the Russian ‘Internet Research Agency’ troll farm if their influence operations were confined to existing ideological bubbles; propaganda depends crucially on the absence of echo chambers and filter bubbles if it seeks to influence more people than those who are already part of a narrow group of hyperpartisans.

Alternatively, if we define echo chambers and filter bubbles much more loosely, in a way that doesn’t require the people inside those bubble to be disconnected from the world of information around them, then the terms become almost useless. With such a weak definition, any community of interest would qualify as an echo chamber or filter bubble: any political party, religious group, football club, or other civic association suddenly is an echo chamber or filter bubble because it enables people with similar interests and perspectives to connect and communicate with each other. But in that case, what’s new? Such groups have always existed in society, and society evolves through the interaction and contest between them – there’s no need to create new and poorly defined metaphors like ‘echo chambers’ and ‘filter bubbles’ to describe this.

Some proponents of these metaphors claim that our new digital and social media have made things worse, though: that they have made it easier for people to create the first, strong type of echo chamber or filter bubble, by disconnecting from the rest of the world. But although this might sound sensible, there is practically no empirical evidence for this: for example, we now know that people who receive news from social media encounter a greater variety of news sources than those who don’t, and that those people who have the strongest and most partisan political views are also among the most active consumers of mainstream media. Even suggestions that platform algorithms are actively pushing people into echo chambers or filter bubbles have been disproven: Google search results, for instance, show very little evidence of personalisation at an individual level.

Part of the reason for this is that – unlike the people who support the echo chamber and filter bubble metaphors – most ordinary people actually don’t care much at all about politics. If there is any personalisation through the algorithms of Google, Facebook, Twitter, or other platforms, it will be based on many personal attributes other than our political interests. As multi-purpose platforms, these digital spaces are predominantly engines of context collapse, where our personal, professional, and political lives intersect and crash into each other and where we encounter a broad and unpredictable mixture of content from a variety of viewpoints. Overall, these platforms enable all of us to find more diverse perspectives, not less.

And this is where these metaphors don’t just become dumb, but downright dangerous: they create the impression, first, that there is a problem, and second, that the problem is caused to a significant extent by the technologies we use. This is an explicitly technologically determinist perspective, ignoring the human element and assuming that we are unable to shape these technologies to our needs. And such views then necessarily also invite technological solutions: if we assume that digital and social media have caused the current problems in society, then we must change the technologies (through technological, regulatory, and legal adjustments) to fix those problems. It’s as if a simple change to the Facebook algorithm would make fascism disappear.

In my view, by contrast, our current problems are social and societal, economic and political, and technology plays only a minor role in them. That’s not to say that they are free of blame – Facebook, Twitter, WhatsApp, and other platforms could certainly do much more to combat hate speech and abuse on their platforms, for example. But if social media and even the Internet itself suddenly disappeared tomorrow, we would still have those same problems in society, and we would be no closer to solving them. The current overly technological focus of our public debates – our tendency to blame social media for all our problems – obscures this fact, and prevents us from addressing the real issues.

2. Polarisation is a political fact, not a technological one. How do you understand political and societal polarisation today?

To me, this is the real question, and one which has not yet been researched enough. The fundamental problem is not echo chambers and filter bubbles: it is perfectly evident that the various polarised groups in society are very well aware of each other, and of each other’s ideological positions – which would be impossible if they were each locked away in their own bubbles. In fact, they monitor each other very closely: research in the US has shown that far-right fringe groups are also highly active followers of ‘liberal’ news sites like the New York Times, for example. But they no longer follow the other side in order to engage in any meaningful political dialogue, aimed at finding a consensus that both sides can live with: rather, they monitor their opponents in order to find new ways to twist their words, create believable ‘fake news’ propaganda, and attack them with such falsehoods. And yes, they use digital and social media to do so, but again this is not an inherently technological problem: if they didn’t have social media, they’d use the broadcast or print media instead, just as the fascists did in the 1920s and 1930s and as their modern-day counterparts still do today.

So, for me the key question is how we have come to this point: put simply, why do hyperpartisans do what they do? How do they become so polarised – so sure of their own worldview that they will dismiss any opposing views immediately, and will see any attempts to argue with them or to correct their views merely as a confirmation that ‘the establishment’ is out to get them? What are the (social and societal, rather than simply technological) processes by which people get drawn to these extreme political fringes, and how might they be pulled back from there? This question also has strong psychological elements, of course: how do hyperpartisans form their worldview? How do they incorporate new evidence into it? How do they interpret, and in doing so defuse, any evidence that goes against their own perspectives? We see this across so many fields today: from political argument itself to the communities of people who believe vaccinations are some kind of global mind control experiment, or to those who still deny the overwhelming scientific evidence for anthropogenic climate change. How do these people maintain their views even – and this again is evidence for the fact that echo chambers and filter bubbles are mere myths – they are bombarded on a daily basis with evidence of the fact that vaccinations save lives and that the global climate is changing with catastrophic consequences?

And since you include the word ‘today’ in your question, the other critical area of investigation in all this is whether any of this is new, and whether it is different today from the way it was ten, twenty, fifty, or one hundred years ago. On the one hand, it seems self-evident that we do see much more evidence of polarisation today than we have in recent decades: Brexit, Trump, Bolsonaro, and many others have clearly sensitised us to these deep divisions in many societies around the world. But most capitalist societies have always had deep divisions between the rich and the poor; the UK has always had staunch pro- and anti-Europeans; the US has always been racist. I think we need more research, and better ways of assessing, whether any of this has actually gotten worse in recent years, or whether it has simply become more visible.

For example, Trump and others have arguably made it socially acceptable in the US to be politically incorrect: to be deliberately misogynist; to be openly racist; to challenge the very constitutional foundations that the US political system was built on. But perhaps the people who now publicly support all this had always already been there, and had simply lacked the courage to voice their views in public – perhaps what has happened here is that Trump and others have smashed the spiral of silence that subdued such voices by credibly promising social and societal sanctions, and have instead created a spiral of reinforcement that actively rewards the expression of extremist views and leads hyperpartisans to try and outdo each other with more and more extreme statements. Perhaps the spiral of silence now works the other way, and the people who oppose such extremism now remain silent because they fear communicative and even physical violence.

Importantly, these are also key questions for media and communication research, but this research cannot take the simplistic perspective that ‘digital and social media are to blame’ for all of this. Rather, the question is to what extent the conditions and practices in our overall, hybrid media system – encompassing print and broadcast as well as digital and social media – have enabled such changes. Yes, digital and social platforms have enabled voices on the political fringes to publish their views, without editorial oversight or censorship from anyone else. But such voices find their audience often only once they have been amplified by more established outlets: for instance, once they have been covered – even if only negatively – by mainstream media journalists, or shared on via social media by more influential accounts (including even the US president himself). It is true that in the current media landscape, the flows of information are different from what they were in the past – not simply because of the technological features of the media, but because of the way that all of us (from politicians and journalists through to ordinary users) have chosen to incorporate these features into our daily lives. The question then is whether and how this affects the dynamics of polarisation, and what levers are available to us if we want to change those dynamics.

3. How can we continue critical research in social media after the APIcalypse?

With great tenacity and ingenuity even in the face of significant adversity – because we have a societal obligation to do so. I’ve said throughout my answers here that we cannot simplistically blame social media for the problems our societies are now facing: the social media technologies have not caused any of this. But the ways in which we, all of us, use social media – alongside other, older media forms – clearly play a role in how information travels and how polarisation takes place, and so it remains critically important to investigate the social media practices of ordinary citizens, of hyperpartisan activists, of fringe and mainstream politicians, of emerging and established journalists, of social bots and disinformation campaigns. And of course even beyond politics and polarisation, there are also many other important reasons to study social media.

The problem now is that over the past few years, many of the leading social media platforms have made it considerably more difficult for researchers even to access public and aggregate data about social media activities – a move I have described, in deliberately hyperbolic language, as the ‘APIcalypse’. Ostensibly, such changes were introduced to protect user data from unauthorised exploitation, but a convenient consequence of these access restrictions has been that independent, critical, public-interest research into social media practices has become a great deal more difficult even while the commercial partnerships between platforms and major corporations have remained largely unaffected. This limits our ability to provide an impartial assessment of social media practices and to hold the providers themselves to account for the effects of any changes they might make to their platforms, and increasingly forces scholars who seek to work with platform data into direct partnership arrangements that operate under conditions favouring the platform providers.

This requires several parallel responses from the scholarly community. Of course we must explore the new partnership models offered by the platforms, but we should treat these with a considerable degree of scepticism and cannot solely rely on such limited data philanthropy; in particular, the platforms are especially unlikely to provide data access in contexts where scholarly research might be highly critical of their actions. We must therefore also investigate other avenues for data gathering: this includes data donations from users of these platforms (modelled for instance on ProPublica’s browser plugin that captures the political ads encountered by Facebook users) or data scraping from the Websites of the platforms as an alternative to API-based data access, for example.

Platforms may seek to shut down such alternative modes of data gathering (as Facebook sought to do with the ProPublica browser plugin), or change their Terms of Service to explicitly forbid such practices – and this should lead scholars to consider whether the benefits of their research outweigh the platform’s interests. Terms of Service are often written to the maximum benefit of the platform, and may not be legally sound under applicable national legislation; the same legislation may also provide ‘fair use’ or ‘academic freedom’ exceptions that justify the deliberate breach of Terms of Service restrictions in specific contexts. As scholars, we must remember that we have a responsibility to the users of the platform, and to society as such, as well as to the platform providers. We must balance these responsibilities, by taking care that the user data we gather remain appropriately protected as we pursue questions of societal importance, and we should minimise the impact of our research on the legitimate commercial interests of the platform unless there is a pressing need to reveal malpractice in order to safeguard society. To do so can be a very difficult balancing act, of course.

Finally, we must also maintain our pressure on the platforms to provide scholarly researchers with better interfaces for data access, well beyond limited data philanthropy schemes that exclude key areas of investigation. Indeed, we must enlist others – funding bodies, policymakers, civil society institutions, and the general public itself – in bringing that pressure to bear: it is only in the face of such collective action, coordinated around the world, that these large and powerful corporations are likely to adjust their data access policies for scholarly research. And it will be important to confirm that they act on any promises of change they might make: too often have the end results they delivered not lived up to the grand rhetoric with which they were announced.

In spite of all of this, however, I want to end on a note of optimism: there still remains a crucial role for research that investigates social media practices, in themselves and especially also in the context of the wider, hybrid media system of older and newer media, and we must not and will not give up on this work. In the face of widespread hyperpartisanship and polarisation, this research is now more important than ever – and the adversities we are now confronted with are also a significant source of innovation in research methods and frameworks.

Filter bubbles and echo chambers have become very widely accepted concepts – so much so that even Barack Obama referenced the filter bubble idea in is farewell speech as President. They’re now frequently used to claim that our current media environments – and in particular social media platforms such as Facebook or Twitter – have affected public debate and led to the rise of hyperpartisan propagandists on the extreme fringes of politics, by enabling people to filter out anything that doesn’t agree with their ideological position.

But these metaphors are built on very flimsy foundations, and it’s high time that we examined the actual evidence for their existence with a critical eye. That’s what my book Are Filter Bubbles Real? sets out to do. There are several recent studies that claim to have identified filter bubbles and echo chambers in search results and social media discussions, yet there are just as many that find no evidence or report contradictory results, so what’s really going on here? Is the impact of these phenomena on public opinion really as significant as common sense seems to suggest?

As it turns out, neither concept is particularly well-defined, and even the authors who first introduced these metaphors to media and communication studies rarely ventured far beyond anecdote and supposition. In the book, I introduce more rigorous definitions, and re-evaluate some of the key research findings of recent studies against these new criteria – and as it turns out, most claims about echo chambers and filter bubbles and their negative impacts on society are significantly overblown. These concepts are very suggestive metaphors, but ultimately they’re myths.

This shouldn’t actually surprise us. Imagine how difficult it would be to completely encapsulate yourself in an echo chamber or filter bubble, in order to receive only information that fits your existing worldview – not just on a single Facebook group or Twitter hashtag, not just on a single social media platform, but in every aspect of your life. To do so is not impossible, strictly speaking – cult members do it. But it requires a level of effort that few ordinary people are likely to commit to.

And in fact, it turns out that those whom we most expect to be caught in filter bubbles – hyperpartisans on the political fringes – are also most actively engaged with the mainstream media, even if they read them from a critical, oppositional perspective. The filter bubble and echo chamber myths have kept us from seeing this more clearly; they’ve sought to blame technology for problems that are, unfortunately, all too human – the unwillingness of polarised political groups in society to engage with one another in order to develop mutual understanding and consensus.

It’s high time we cut through those myths and shifted our focus to the cognitive processes and ideological mindsets that produce such polarisation – and I hope that the critical re-appraisal presented in Are Filter Bubbles Real? can contribute to that shift.

Axel Bruns is an ARC Future Fellow and Professor in the Digital Media Research Centre at Queensland University of Technology in Brisbane, Australia. He is the author and editor of several books, including Gatewatching and News Curation: Journalism, Social Media, and the Public Sphere and Twitter and Society. His latest book, Are Filter Bubbles Real?, is now available from Polity.

The Nordicom Review article was published under an open access licence – here’s the full abstract:

Previous research into social media platforms has often focused on the exceptional: key moments in politics, sports or crisis communication. For Twitter, it has usually centred on hashtags or keywords. Routine and everyday social media practices remain underexamined as a result; the literature has overrepresented the loudest voices: those users who contribute actively to popular hashtags. This article addresses this imbalance by exploring in depth the day-to-day patterns of activity within the Australian Twittersphere for a 24-hour period in March 2017. We focus especially on the previously less visible everyday social media practices that this shift in perspective reveals. This provides critical new insights into where, and how, to look for evidence of onlife traces in a systematic way.

This builds on our prior work to identify Australian Twitter accounts and map the network structure of the Australian Twittersphere (covered at The Conversation and published in Social Media + Society), and tracks the public posting activities of some 500,000 Australian Twitter accounts on a continuous, real-time basis. For this general overview, we’re pulling out the major hashtags and the most mentioned accounts (counting both @mentions and retweets) – but of course the underlying dataset captures far more than this.

If you’re interested in further research that builds on this dataset, please get in touch!

(Click ‘full screen’ to enlarge.)

The Australian Twittersphere in 2016: Mapping the Follower/Followee Network

Twitter is now a key platform for public communication between a diverse range of participants, but the overall shape of the communication network it provides remains largely unknown. This article provides a detailed overview of the network structure of the Australian Twittersphere and identifies the thematic drivers of the key clusters within the network. We identify some 3.72 million Australian Twitter accounts and map the follower/followee connections between the 255,000 most connected accounts; we utilize community detection algorithms to identify the major clusters within this network and examine their account populations to identify their constitutive themes; we examine account creation dates and reconstruct a timeline for the Twitter adoption process among different communities; and we examine lifetime and recent tweeting patterns to determine the historically and currently most active clusters in the network. In combination, this offers the first rigorous and comprehensive study of the network structure of an entire national Twittersphere.

A summary of some of the study’s key findings was published in The Conversation in May 2017. Meanwhile, a paper by Axel Bruns at the Future of Journalism conference in Cardiff in September 2017 built on this new Twittersphere map to test for the existence of echo chambers and filter bubbles in Australian Twitter – and found little evidence to support the thesis:

Echo Chamber? What Echo Chamber? Reviewing the Evidence

The success of political movements that appear to be immune to any factual evidence that contradicts their claims – from the Brexiteers to the ‘alt-right’, neo-fascist groups supporting Donald Trump – has reinvigorated claims that social media spaces constitute so-called ‘filter bubbles’ or ‘echo chambers’. But while such claims may appear intuitively true to politicians and journalists – who have themselves been accused of living in filter bubbles –, the evidence that ordinary users experience their everyday social media environments as echo chambers is far more limited.

For instance, a 2016 Pew Center study has shown that only 23% of U.S. users on Facebook and 17% on Twitter now say with confidence that most of their contacts’ views are similar to their own. 20% have changed their minds about a political or social issue because of interactions on social media. Similarly, large-scale studies of follower and interaction networks on Twitter show that national Twitterspheres are often thoroughly interconnected and facilitate the flow of information across boundaries of personal ideology and interest, except for a few especially hardcore partisan communities.

Building on new, comprehensive data from a project that maps and tracks interactions between 4 million accounts in the Australian Twittersphere, this paper explores in detail the evidence for the existence of echo chambers in that country. It thereby moves the present debate beyond a merely anecdotal footing, and offers a more reliable assessment of the ‘echo chamber’ threat.

The aborted Twitter Archive project was established after Twitter, in a well-publicised move, gifted the Library access to its tweet archive and live feed in 2010. But unfortunately, beyond the fanfare, the Library never provided the project with the support it required.

Released on Boxing Day, in a period where public scrutiny of official announcements is generally limited, news of the project’s failure arrives at a time when Twitter’s public and political importance has never been greater. The project’s end creates substantial concerns both for present analysts and for future historians.

Exhibit A: Donald J. Trump. The President’s use of Twitter is as famous as it is infamous, and the status of his tweets as official government statements remains a matter of debate. More specifically, some analysts following the Mueller probe into the Trump campaign’s alleged collusion with Russian operatives have already pointed to Trump’s tweets as a potential admission of obstruction of justice.

Clearly, the tweets posted by @realdonaldtrump, @POTUS, and other accounts associated both with the administration itself and with its political opponents in Congress and beyond must be preserved for further study in the shorter and longer term. An impartial public service institution like the Library of Congress is inherently best-placed to address that task; whether the LoC’s much-reduced approach to archiving selected tweets can still achieve it remains unclear.

Similarly, well beyond the United States there is considerable concern at present about the role of fake and automated accounts on Twitter and other social media platforms. Acting in unison and controlled by nefarious state and commercial actors, these accounts are suspected of seeking to affect the public perception of particular issues and individuals. Their mis- and disinformation, their ‘fake news’, their ‘computational propaganda’, has been implicated in the context of the Brexit referendum and various national elections.

Many research projects around the world are now attempting to unravel the networks and activities of the accounts engaged in such practices, on Twitter and elsewhere. But in the absence of a comprehensive dataset on contemporary social media activities, each is picking only at a handful of strands of the network, and there is a real sense that the bot-herding information warriors behind it all remain one step beyond those who would seek to stop them.

A comprehensive, all-inclusive Twitter Archive in the shape initially envisaged by the Library of Congress would have provided a major boost to such efforts to protect public debate from outside manipulation. While methods to analyse the activities of Twitter accounts have much improved in recent years, access to the large-scale datasets to which such methods could be applied remains the major bottleneck. The LoC archive could have provided a master dataset to this critical effort.

Both these examples relate to our current needs. But however the world manages its many contemporary challenges, future historians will no doubt also want to retrace how we got here, and what role social media played in all this. Are the critics right, and did social media lead to a fragmentation of the public into different echo chambers, or did we also use these tools to organise and fight back against those who sought to undermine civic society to further their own interests?

This is not a question which can be answered by studying a Twitter archive, however comprehensive, on its own. Ideally, we would need similar archives for Facebook and other social media platforms, as well as large-scale archives of mainstream media around the globe. But we crucially need the LoC’s Twitter Archive, or something like it, as part of the mix, and the future’s understanding of our troubled present will be all the poorer for its absence.

All this makes the Library’s decision to give up on the project all the more tragic. That said, it is not unexpected: especially under its recent leadership, the Library of Congress has remained a deeply traditional institution with a limited understanding of new technologies. Beyond the initial fanfare about the project, LoC updates on its progress have remained scant and infrequent. Visiting researchers who had expected to work with the dataset found it unavailable.

A variety of grassroots, scholarly, and commercial projects have already sprung up around the world to support social media archiving attempts of their own, at varying scales: they range from capturing only the deleted tweets of selected politicians to tracking entire national Twitterspheres, as my colleagues and I have attempted in Australia.

Even in combination, though, none of these can possibly come close to what the Library of Congress Twitter Archive as originally conceived had promised: a live, comprehensive archive of all tweets, from Twitter’s launch in March 2006 to the present day, and continuing into the foreseeable future. That goal can only be achieved with the active support of Twitter itself.

Now that the Library of Congress, after years of prevarication, has finally given up on its Twitter Archive project, it is therefore high time that Twitter found one or more new partners for this initiative — the Internet Archive or other national libraries, perhaps. Ideally these would be partners with a greater affinity for digital content than the Library of Congress has shown to date.

To do so would also provide a major signal of Twitter’s commitment to corporate social responsibility, at a time when its reputation is under siege both from Donald Trump’s exploits and from substantial concerns about the volume of malicious human and automated actors present on the platform. The establishment of a comprehensive Twitter Archive — properly this time — might even shame Facebook and other platform providers, traditionally even more reluctant than Twitter to engage with public-interest social media research, into following suit.

So, now that the Library of Congress has failed to deliver, Twitter has an opportunity to regain the initiative. But whether it seizes that opportunity or not, we must not allow the role of social media in contemporary society to remain poorly documented. With Twitter’s support or without it, we must continue to push forward.

Stephen Harrington, Axel Bruns, and Tim Highfield. “Infotainment and the Impact of ‘Connective Action’: The Case of #MilkedDry.” Paper presented at the Australia New Zealand Communication Association conference, Sydney, 6 July 2017.

Axel Bruns, Brenda Moon, and Ehsan Dehghan. “Dynamics of a Scandal: The Centrelink Robodebt Affair on Twitter.” Paper presented at the Australia New Zealand Communication Association conference, Sydney, 7 July 2017.

Part of the problem here is that we all design our own filter bubbles, as the journalism researcher Paul Bradshaw has put it: what two random users see of Twitter might be entirely different, depending on what other accounts they choose to follow. If all you ever see is food porn, perhaps you’d care to make some new connections. Or perhaps that’s what you’re there for.

But if we could look beyond our own, personal networks, what would we see? What are the major drivers of Twitter take-up, in Australia and elsewhere? Do we connect around shared interests, shared location, or pre-existing offline relationships? And when, in the eleven-year history of the platform, did these structures form?

These are the questions that guided a new, long-term study of the Australian national Twittersphere that my colleagues and I have undertaken. Drawing on TrISMA, a major multi-institutional facility for social media analytics, we identified some 3.7 million Australian Twitter accounts in existence by early 2016, and captured the 167 million follower/followee connections they have amongst each other.

Twitter took off in Australia in 2009, some three years after its launch, and saw a fairly steady sign-up rate of 1,000-2,000 new accounts between 2010 and 2014. Growth has slowed since then, and this may indicate market saturation. There are a number of obvious spikes in new account sign-ups, too: the series of natural disasters in early 2011 attracts users to the platform who recognise its role in crisis communication, and the political turmoil of 2013 also seems to drive take-up.

A major spike in 2015 appears to coincide with the devastating Nepal earthquake, but we’ve yet to determine why that event would lead to new Twitter accounts being created in Australia.

To focus in on the core parts of the network, we further filtered this to accounts that have at least 1,000 connections in the global Twittersphere, which left us with the 255,000 best-connected accounts. We visualised their network using Gephi’s Force Atlas 2 algorithm, which places accounts close to each other if they share many connections, and further apart if they are only poorly connected.

The network map shows clear clustering tendencies: dense regions, where many accounts are closely connected, are separated from each other by lower-density spaces. We systematically examined these clusters, and labelled them based on the overarching themes that emerged from an analysis of the account profiles in each cluster. The result is a kind of birds-eye view of the Twitter landscape, from politics to popular culture and from education to sports.

Perhaps surprisingly, accounts connecting around teen culture make up the largest part of this network: 61,000 of our 255,000 accounts are located here. Other major clusters include aspirational accounts (these include self-declared social media gurus, self-improvement and life-coaching practitioners, and others who sought to use Twitter for professional betterment), at 26,000 accounts; sports, with 25,000 accounts (including distinct sub-clusters for cycling and horse racing); and netizens, technologists, and software developers (17,000 accounts).

Shared interests emerge from this as the central drivers of our connections on Twitter: for the most part, we follow others because of the topics they cover, not because they’re from the same city or state or because we already know them offline. An equivalent map for Facebook, where connections are much more strongly based on prior acquaintance, would likely look very different.

We further found that these accounts also arrived on Twitter at very different times: both netizen and aspirational accounts were created very early in the history of the platform. As expected, netizens constituted the vast majority of Australia’s early adopters, with aspirational accounts close behind; fully half of the population in both these clusters had arrived on Twitter by mid-2010. Sports took a year longer, and may well have been helped along by Twitter Australia itself as it reached out to key sporting codes to get their teams and players signed up.

By contrast, the teen culture accounts arrived a great deal later. It took until mid-2012 until half that cluster’s population had joined – the teen invasion of Twitter represents a secondary adoption event, following the first big influx of Australian users in 2009/10. Here, too, we suspect active encouragement from key bands like One Direction and Five Seconds of Summer as a major driver.

In spite of Twitter’s reputation as a space for political debate and agitation, politics attracts only some 13,000 accounts (including 1,500 that form a separate, staunchly right-wing cluster); there’s a great deal more to Twitter than political argument.

But if all you ever see on Twitter is partisan bickering, there may be a reason: per capita, the political accounts are some of the most active in the Australian Twittersphere. Over their lifetimes, they’ve posted an average of 7.2 tweets per day (and the accounts in the hard right cluster even manage 12.5 per day); in the turbulent first quarter of 2017, those averages are even higher. Most of the other major cluster communities have managed less than half that work rate; historically, only the teen culture accounts have been similarly active.

Twitter is what its users make it, and Australian users have made it a diverse and dynamic place, even if perhaps they’re less aware of each other than they should be. As users, we should step beyond our networks more often, to avoid becoming trapped in our own filter bubbles – and this goes doubly for politicians, journalists, and others who now treat their immediate Twitter networks as an instant source of popular opinion.

And as a company, Twitter too has much work to do to enable its users to experience the full variety of networked communication and culture that the platform has to offer. Changes to how it recommends new accounts to follow, and how it reveals trending topics outside of our existing networks, could help a great deal in combatting the threat of getting stuck in your own filter bubble.

It doesn’t stop there, of course. We can only speculate what the equivalent networks for Facebook, Instagram, or Snapchat would look like, and what they might tell us about how people are using these platforms.

(An edited version of this article was published in The Conversation on 3 May 2017.)