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.

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.)

Full details are below – please pay particularly close attention to the Eligibility Requirements.

The Project

We are seeking a highly motivated candidate to participate in an Australian Research Council Future Fellowship project which draws on several ‘big data’ sources on Australian online public communication.

This PhD project provides an unprecedented opportunity to investigate the flow of information across the Australian online public sphere at large scale and in close to real time, within a world-class research environment. With an ERA ranking of 5 (well above world standing), Creative Industries at QUT is the leading institution for Media and Communication research in Australia, and ARC Future Fellow Professor Axel Bruns is an international research leader in the area of Internet studies.

The PhD researcher will be supervised by the ARC Future Fellow. This position will be located on the QUT Kelvin Grove campus, Brisbane, and will commence in mid-2015.

The researcher will carry out a range of tasks associated with project activities, including:

• using data collection and analysis methods and instruments developed for the project for a variety of purposes, including:
  • post hoc research into user activity patterns and information flows in the Australian online public sphere across a wide range of cases;
  • speedy and agile analysis of online activities in issue publics related to current events, and publication of initial analysis;
  • input into further development of online media tracking and analysis methods and instruments developed by the project.
• contributing to the development of new models of communication processes in the Australian online public sphere by:
  • tracing the trajectories of intermedia information flows across the diverse datasets available to the project;
  • developing and testing a range of preliminary models for the conceptualisation of issue publics and other formations of public discourse in online environments;
  • contributing to the integration of these models into a more comprehensive framework for understanding processes of communication across the contemporary media ecology.
• contributing to the dissemination of research findings from the project by:
  • publishing preliminary analyses and findings in relevant outlets (The Conversation, project website and other publications, etc.);
  • presenting project findings at relevant national and international conferences in media and communication and related fields;
  • publishing research outcomes from the project in sole- and collaboratively authored articles and chapters in high-profile journals and books.

This PhD project supports an ARC Future Fellowship research project investigating intermedia information flows in the Australian online public sphere. The emergence of new media forms has led to a profound transformation of the Australian media environment: mainstream, niche, and social media intersect in many ways, online and offline. Increased access to large-scale data on public communication online enables an observation of how the nation responds to the news of the day, how themes and topics unfold, and how interest publics develop and decline over time.

Eligibility Requirements

You must have:

• first class honours (H1), or equivalent, in media and communication or a closely related area;
• demonstrated expertise in research on the contemporary public sphere and on information flows in online and social media;
• demonstrated knowledge of, and entry-level experience with, qualitative and quantitative research that uses innovative methods drawing on ‘big social data’ from social media and other relevant online sources;
• demonstrated understanding of current themes and issues in Australian public debate, and of the contemporary Australian media environment;
• effective written, interpersonal and computer-mediated communication skills;
• demonstrated computing skills, including familiarity with digital research management and social media research tools.

How to Apply

You’ll need to submit:

• your CV;
• the names of two referees;
• a detailed statement addressing the eligibility criteria.

Send your application to Professor Axel Bruns (a.bruns@qut.edu.au) by the closing date.

What Happens Next

We’ll award the scholarship based on academic merit, research experience and potential.

If your profile meets the eligibility requirements you’ll be asked to submit a formal application for admission to the PhD.

Further details are available on the QUT Website.

A couple of presentations considered social media from a systemic perspective – here are Ben Light and Elija Cassidy on the practices of disconnection using social media:

And here’s our ‘very big data’ perspective on Twitter as a global social network:

Wilfred Wang explored Weibo, and highlighted the fact that even in centralised China regional differences still matter immensely in social media:

Jacinta Buchbach explored the implications of social media use for employers and employees:

Jean Burgess, Elija Cassidy, and Ben Light discussed the social media components of the Movember phenomenon:

While Ben Light tackled the elephant – er, the cat – in the room:

Finally, in a panel of the uses of social media for second-screen engagement with television, Darryl Woodford et al. introduced Telemetrics as a new set of methodologies and metrics for evaluating audience engagement:

And continued the discussion by considering the impact of existing follower/followee structures in the Australian Twittersphere for such engagement with TV content:

So much for 2014 – see you next year in Phoenix for IR16 (the call for papers has been released already)!

Given this popularity, and a significant overlap between the target market for Big Brother viewers and the social media platforms, it will be interesting to observe the extent to which social media activity (and perhaps, eventually, sentiment) acts as a predictor for votes on the show. In this blog, following the first round of nominations, first eviction and the first round of single nominations, we are going to look to the data from the last 2.5 weeks to try to test whether social media activity acts as a predictor of public votes.

So far, at least, it has been a mixed bag, but let’s start with the positive; the public vote for the ‘Perfect Pair’ dance competition, in which the winners were awarded $30,000, was held between the final two pairs – Lawson and Aisha & Dion and Jason. The public then voted for the pair with the best dance through JumpIn, but did they actually just vote for their favourite pair? If we use social media activity as a barometer, it seems that could be the case. Our data showed a tight race, which Lawson & Aisha just pipped, and indeed the public vote came back 51.8% in favour of Lawson & Aisha. Perhaps, if they had been up against, say, Travis and Cat – who were hardly mentioned this week – they would have won by even more:

Lawson also tells an interesting story in the overall polling; as seen in the chart below which highlights the running total for all housemates; largely anonymous until the dance-off and his decision to give Aisha the lions share of the prize money ($20,000) was rewarded in the social media volume.

Below is a running total of Twitter mentions for the pairs since launch night, however we will focus on the last week’s long-winded and highly debated eviction process for the time being. Nominees made up 5 of the six most talked about housemates on the night before the eviction process began, and the ones not being talked about were being carried by their partner based on the pairs table:

We can of course ask some other interesting questions from these charts: where were Skye and Lisa when they were ‘saved’? Were Jake and Gemma losers in the public vote due to anonymity, or hatred? What caused David and Sandra to be saved, when they were virtually anonymous through the first week, and only talked about subsequently in regard to David’s chauvinistic comments. Was it better for David to be hated, rather than not talked about at all? Related to this, there is the question of screen time and popularity inside the house, allowing us to address what went wrong for Gemma this week, given her achieved intent to secure airtime?

Up for eviction this week were Skye & Lisa, Jake & Gemma, Travis & Cat and David & Sandra. Ever since the Katie & Priya first week fiasco, Skye & Lisa have been by far the most talked about pair of the season and consequently were saved on Monday night as per our prediction based on the previous graph, with Skye & Lisa the most popular pair on the 22nd September. Interesting here, however, is that Gemma & Jake were the pair with the second most social media activity, and the most popular during the nomination period, indicating that the sentiment will also be a significant factor in creating further predictions.

While we have our own tool monitoring Big Brother discussion (http://bigbrother.thehypometer.com), Channel 9 (Mi9/JumpIn) have also launched a counter, the “Big Brother Radar”, which captures tweets and Facebook statuses by those who seek, deliberately, to be noticed by the radar using official C9 hashtags (e.g. #BBAUGemma). Our tool, by contrast, attempts to measure the underlying volume of discussion (and, by possible inference, interest) in the competitors as a whole, on social media.

Going forward, we hypothesise that those housemates who the public have no interest in will be those who struggle in a ‘vote to save’ format. That said, it’s probably not advisable to bet based on this information. It may be that the Radar format serves as a better prediction of those likely to be evicted (i.e. the effort to post with the correct hashtag is correlated to the effort to vote), it may be that sentiment proves highly significant, or indeed it may be that social media is not a good barometer of the BB voting public. Whichever of these proves to be the case however, the data is sure to be interesting.

Finally, it is worth noting that one of the problems of a lack of live feed – which we have ranted about previously – and indeed this year any live updates at all is that it allows producers to largely control the message; hence, social media reaction largely follows the amount of airtime given to contestants and the plot lines developed, much like a soap. By contrast in the USA, with 4 live camera views running 24 hours a day, users are able to create and share their own storylines about the housemates — generating ‘hype’ for the show which we do not see here. In Australian Big Brother we are told what to think, and we’ll leave it as an exercise for the reader how that reflects on wider society. Finally, we’ll leave you with a running total of the housemates mentions to date, where Skye continues to lead the way:

With the purpose of trying out some new tools and ideas, I collected tweets about Toronto’s WorldPride festival, which took place this past June. It was a huge shindig and while I wasn’t able to capture every relevant tweet, the 6 hashtags that I tracked* (#WP14TO, #WorldPride, #PrideToronto, #TorontoPride, #PrideTO, #WPTO14) turned up a pretty good dataset totalling 68,231 tweets. This dataset showed some cool trends relating to participation, especially people’s awesome selfies and photo documentation of the WorldPride parade (check out the National Post’s photos if you’re lacking rainbows in your life). I hope to eventually share some of these broader analyses but today I just wanted to look at a little bump that showed up after the festival, circled in Figure 1.

Figure 1. Total WorldPride tweets over time

This little spike of nearly 1000 tweets happened when Toronto’s Mayor, Rob Ford – fresh out of rehab, as all the latest news stories note – refused to join in the standing ovation at a city council meeting to thank WorldPride’s coordinators. That’s right, everyone else stood up and clapped but Ford, with his history of avoiding Toronto Pride and opposing visible support for LGBTQ people throughout the city, remained seated. Apparently, to add insult to injury, all of this came alongside Ford casting the only vote against launching a study to determine if more homeless shelter space for LGBTQ youth is needed in Toronto.

So what did Torontonians do? Well, when the incident first happened, some of the city councillors tweeted about it. This is reflected in the first bump in Figure 2, when many people retweeted these preliminary expressions of disappointment with Ford’s behaviour. Figure 2 shows the volume of tweets over time for the bump that was circled in Figure 1 but here I’ve also plugged a bit of code into Tableau to show the different types of tweets. You can see that this whole Twitter event was characterized by people retweeting, often using the popular #TOpoli (Toronto politics) alongside the WorldPride hashtags.

Figure 2. Rob Ford incident over time, sorted by tweet type

The mainstream press caught wind of the story and a bit later in the day, CBC News tweeted about it, adding a photo of Rob Ford sitting during the applause. However, the real kicker in terms of momentum happened when media personality Jian Ghomeshi (broadcaster, musician, host of Q) made a tweet that resonated with a bunch of people:

It’s not the drugs/alcohol, it’s this… RT @CBCNews Toronto Mayor Rob Ford doesn’t stand for #WorldPride ovation. pic.twitter.com/Z33MFpYmhx

— jian ghomeshi (@jianghomeshi) July 9, 2014

Okay, so Ghomeshi’s tweet wasn’t an original, he simply added his own opinion to the CBC’s previous tweet. But the combination of celebrity critique with the compelling visual made this the most popular retweet of the whole debacle, raking in nearly 300 retweets in my dataset and gaining even a few more that weren’t captured during my data collection.

What does it mean that retweets dominated the dialogue throughout this whole spectacle? Does it show that mainstream media still has the loudest voice even on social media platforms, which are often lauded as being participatory and democratizing? Perhaps. Does it mean that Torontonians are lazy and would rather just press the ‘retweet’ button than weigh in with their own opinions? I think not.

Retweeting IS a form of participation (boyd, Golder & Lotan, 2010). It serves multiple purposes: it gets the word out by making a conversation more visible, it engages a wider network of participants in the dialogue, and it shows support for a particular viewpoint. Ghomeshi’s tweet hits the important points – it expresses a negative sentiment for Ford’s actions and drives it home with visual evidence of his non-participation. People who retweeted likely felt that this tweet represented their feelings accurately. It’s also likely that a broader range of people feel comfortable retweeting something fairly political when it’s led by a media personality because they may not be ready to make such strong statements independently.

A couple of the participants in my MSc research who weren’t out to their families talked about this. They explained that they wanted to show support for LGBTQ people and did so through political tweets that didn’t reflect their identity as much as personal statements. It seems that retweeting might be a way for a lot of people to get involved and stand in solidarity with a certain viewpoint without their actions implicating them beyond their capacity. Our personal situations may not always allow all of us to be highly vocal activists, but retweeting could add power to those who do speak up so that they speak on behalf of a collective – a collective of Twitter users, at least.

Personally, I might also guess that users mostly retweeted during this incident because, well, is there really anything left to say about Rob Ford?

————

Notes:

• I’ve added Tableau to my blog’s “Assorted tools” page in case you’d like to have a closer look at it. Their website allows a free trial along with some great video tutorials.
• A good resource for what/why/how to work with Twitter data is the book “Twitter and Society” edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt and Cornelius Puschmann.
• You may have noticed that I’ve been talking about ‘big data’ without heaps of numbers and statistics. While this speaks to my tendency toward qualitative research, it’s also a technique from the digital humanities methods that I’ve been learning about. It’s possible to take large sets of data and do a ‘distant reading’ (Moretti, 2007) of them in their entirety (like Figure 1) and then to drill down into more qualitative types of content analysis. I turned to Richard Rogers’ book “Digital Methods” as inspiration for this.
• Disclaimer: This was just an exercise (with a relatively small number of tweets!) that I’ve presented for discussion – there are of course lots of limitations to ‘big data’ analysis and the use of Twitter data. While I don’t address these here, other people have – start with boyd and Crawford’s “Critical Questions for Big Data” to get a handle on the issues.
• Opinions are my own, as this was cross-posted from stefanieduguay.com

*All of this was done with the gracious help of QUT’s Social Media Research Group, especially with Jean Burgess’ ninja Twitter data collection skills and Darryl Woodford’s crash course on Tableau analysis for Twitter data.

In text references:

boyd, d., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. Proceedings of the 43rd Hawaii International Conference on System Sciences, IEEE. doi:10.1109/HICSS.2010.412

Moretti, F. (2007). Graphs, maps, trees: Abstract models for a literary history. London: Verso.

First, a look at the week that was.  Overall, the generic hashtags are being used consistently and in true Big Brother fashion, more hashtags are being used everyday, often introduced by CBS. Just this week I had to add, #BBTracker as well as a few running hashtags, #ZackAttack (a nickname for HG Zach), #Zankie (showmance between Zach and Frankie), #ZankieFallOut (the potential end of Zach and Frankie), and #EvictionPrediction. It’s a wonder why CBS continues to add hashtags that could possibly be steering people away from using their generic ones. What’s wrong with a simple #bb16?

Being elimination night, Thursday seems to be the peak show of the week with close to double the amount of tweets of the other two days. We will look at whether the ‘big night’ remains consistent in coming weeks in the interest of finding out whether context or type of show are more important for tweet numbers.

Total number of tweets containing the generic BB hashags for the week 6 – 12 July.

Taking a closer look at the Thursday show – conversation remained fairly consistent with no major spikes, just a lot of volume.

Total tweets by minute for the Thursday show.

HG Twitter Accounts

The graph below shows us which housemates’ twitters are getting the most mentions, and unsurprisingly at the top, is Joey, the first eliminated contestant voted out in a unanimous 13/13 vote. Two down the list is the most recently voted out contestant Paola, which we are guessing will probably be the most talked about by this time next week if there is a pattern. We saw this last year when HG Kaitlin was voted out early yet continued to be one of the most talked about HGs for the remainder of the series, largely thanks to her involvement in the racial-slur-scandal and somewhat thanks to her social media presence.

Number of times contestant Twitter accounts have been mentioned; 6 – 12 July.

Something new: Users by Timezone

Something we became interested in during last year’s BB broadcast was the difference in tweeters from one side of the US coast to the other, this year also considering the top 10 timezones joining the conversation with the generic hashtags:

Top 10 timezones using the generic BB hashtags on Twitter; 6 – 12 July.

Eastern time tweet by far the most of any of the timezones which fits with the documented distribution of the US population (47% live in Eastern Time). However, people in Mountain Time are tweeting more about Big Brother than expected with their distribution being only 5.4%, but publishing 9.45% of the total tweets regarding Big Brother. We’ve established that the Quito (Ecuador) timezone aligns with Chicago / Central time, so those users who say they’re in Quito, likely aren’t.

Until next time…

Essentially, an infographic combines key text, imagery and design to tell a story. It is visual shorthand with the ability to communicate in-depth information. Infographics allow information to be viewed and understood by a wide audience, not just by academics. The most crucial and interesting information is singled out from a pool of data, making it more relatable and compelling to the general public.

When it comes to mapping the Twittersphere, the amount of information is overwhelming. Infographics will play a crucial role in the success of this project within the media and general public. The main goal of the infographics I have created is to compare the US and Australian data, highlighting the difference in relationships between Twitter and reality television. My approach to designing infographics for this project has revolved around picking the most crucial data and displaying it in a simplified and uniform manner. The font and colour choice creates a contemporary aesthetic that appeals to a wide audience. Currently, the end goal for this series of infographics is to create a completed publication for both print and online viewing.

My approach to the design process of an infographic

Design Iteration 1

The most important step is to select the story you want the infographic to tell. For this particular infographic, I wanted to compare the number of viewers to the number of tweets to show that there is a dramatic difference between US and AU tweet habits in regards to reality television. The first obstacle is the raw data as it compares oranges to apples. It has to be transformed to be comparable and relevant to one another. The conversion of reams of data into a single number needs to be systematic and fair to be a true representation.

I originally wanted to show the number of tweeters to the number of viewers as a percentage, however the numbers just weren’t there. Percentages below 1 aren’t powerful, so a ratio was used in their place. The final infographic read 1 viewer tweets for every 57 who watch The X Factor (US). The viewer numbers are averaged from the season, excluding the premiere and finale episodes. The premiere and finale episodes had to be excluded as they were skewing the averages for both the viewers and tweets.

Initially the tweet numbers used in design iteration 1 were all-inclusive, altering the story of the infographic. The gap between the US and AU tweet habits had been marginalised and subsequently the point was lost. In design iteration 2 the tweet numbers were substituted with unique authors, realigning the infographic with the initial story. Once the data is communicating the correct story, the primary aim is to assist the communication by presenting it as clearly and engagingly as possible.

In my first semester of university I was introduced to a term called ‘Chartjunk.’ It refers to the presence of any element that is not essential to the overall design. Anything that does not have a purpose or assist in the communication of the message needs to be removed. Design iteration 3 is all about refining and the eradication of said Chartjunk. The information is complex and it takes time to digest, so the design must be as streamlined as visually possible.

Design Iteration 2

Design Iteration 3

From Design iteration 2 to Design iteration 3 these are the significant changes that occurred:

• The amount of white space was increased so the eye is able to separate the information more easily and create a pathway for viewing.
• The number of font sizes was reduced and spacing between was altered to distinguish the headings from the body and in turn communicating more clearly.
• The individual numbers from each bar were removed and replaced by a simplistic scale. By adding a scale the viewer instinctively processes the information visually without the need of text.
• The blue column not only guides the eye through the infographic, but it also clearly shows that there is a relationship between the numbers contained within it. The key became obsolete with the addition of the blue column and was removed.

The design for this research project needs to be intelligent, simple and appealing. Through the iterative design process, the final solution for this infographic is the most powerful and successfully meets the design needs for this project. The infographics as part of this research project are an invaluable method of communication. With new data being added everyday to the growing Twittersphere, these infographics are just the beginning. I look forward to sharing the series of infographics I’ve been busy working on with you in the near future.

‘Big Data’

• Jean Burgess: A Decade of ‘Social Media’ and What to Do about It: Methodological Challenges of Historicising the Proprietary Web, presented at the Association of Internet Researchers Conference pre-conference on Internet Histories, Denver, 23-26 Oct. 2013.
• Axel Bruns and Jean Burgess: Easy Data, Hard Data? Twitter Research and the Politics of Data Access, presented at the Compromised Data symposium, Toronto, 28 Oct. 2013.
• Darryl Woodford: Slicing Big Data: Gambling, Twitter & Time Sensitive Information, presented at the Association of Internet Researchers Conference, Denver, 23-26 Oct. 2013.
• Darryl Woodford: Lightning Talk: Social Media Methods & Ethics, presented at the Association of Internet Researchers Conference pre-conference, Denver, 23-26 Oct. 2013.
• Sky Croeser and Tim Highfield: Mapping Movements: Social Movement Research and Big Data: Critiques and Alternatives, presented at the Compromised Data symposium, Toronto, 29 Oct. 2013.

Crisis Communication

• Jean Burgess, Axel Bruns and Avijit Paul: Cumulative and Comparative Social Media Analytics for Crisis Communication, presented at the Association of Internet Researchers Conference, Denver, 23-26 Oct. 2013.
• Axel Bruns: Social Media Issue Publics in Australia, presented at the IBM Research Colloquium 2013, Melbourne, 12 Nov. 2013.
• Avijit Paul: Extracting Important Information from Social Network Stream during Crisis, presented at the Association of Internet Researchers Conference, Denver, 23-26 Oct. 2013.
• Axel Bruns: Social Media in Times of Crisis: The Australian Perspective, presented at a Project EPIC symposium, Boulder, 23 Oct. 2013.

News and Politics

• Axel Bruns, Tim Highfield and Theresa Sauter: #ausvotes Mark Two: Twitter in the 2013 Australian Federal Election, presented at the Association of Internet Researchers conference, Denver, 24 Oct. 2013.
• Theresa Sauter and Axel Bruns: Exploring Emotions on #auspol: Polarity and Public Performance in the Twitter Debate on Australian Politics, presented at the Association of Internet Researchers conference, Denver, 26 Oct. 2013.
• Tim Highfield: Appropriating Breaking News? The Evolving Twitter Coverage of the Lance Armstrong Doping Scandal, presented at the Association of Internet Researchers Conference, Denver, 23-26 Oct. 2013.
• Axel Bruns: Twitter and the 2013 Australian Election, presented at Political Imperatives: Media and the 2013 Election Campaign, Brisbane, 27 Sep. 2013.
• Wilfred Wang: Defending Cantonese Weibo and Geo-Identity Politics in Guangzhou China, presented at ‘The Impact of War on Modern Chinese Society’, University of Queensland, Brisbane, 18-19 Oct. 2013.
• Axel Bruns and Theresa Sauter: Anatomie eines Trending Topics: Retweet-Ketten als Verbreitungsmechanismus für aktuelle Ereignisse, presented at the DGPuk Innovative Methods for the Study of Public Communication workshop, Vienna, 8 Nov. 2013.

Popular Culture

• Ana Vimieiro and Renato Vimieiro: Oscar Pistorius and the Death of Reeva Steenkamp: Mapping Public Opinions Using Frame Analysis and Data Mining, presented at the Association of Internet Researchers conference, Denver, 23-26 Oct. 2013.
• Katrin Weller and Axel Bruns: Friends or Followers: German Soccer Clubs and Their Fans on Twitter, presented at the Association of Internet Researchers conference, Denver, 25 Oct. 2013.
• Tim Highfield: News via Voldemort: The Role of Parody and Satire in Topical Discussions on Twitter, presented at the Association of Internet Researchers Conference, Denver, 23-26 Oct. 2013.
• Theresa Sauter and Axel Bruns: #Pontiff-Ex: The Twitter Community’s Reaction to the Papal Resignation, presented at the Association of Internet Researchers conference, Denver, 25 Oct. 2013.

Platforms

• Kim Osman: Wikipedia as a New Media Institution: Issues of Diversity, Regulation and Sustainability in an Open Encyclopaedia, poster presented at WikiSym & OpenSym 2013, Hong Kong, 5-7 Aug. 2013 (paper at QUT ePrints).
• Wilfred Wang: Identity, Locality and Social Media in Guangzhou China, presented at the Fifth Postgraduate Symposium: Identities, Ideas, Ideologies, Griffith University, Brisbane, 1 Nov. 2013.

That should be enough for 2013! See you next year at a conference somewhere…

However, I still get the question “what is data science?”, so here is an overview of a recent presentation I did for a Digital Marketing event where I talked about the value of data science and how data can be an asset for businesses through service multiple levels of the organisation.

As a data scientist I work with data to build tools that support us in making better decisions about how we live our lives.  My data science work has brought me to a large variety of environments in which I’ve enjoyed being a data scientist. I’ve worked with epidemiologists in building artificial intelligence tools to help them identify causes of cancer, I’ve helped construction company managers build reporting tools to identify real-time job costs and charges, and most recently I’ve been working with interdisciplinary researchers from QUT in the collection, analysis and visualisation of social media data to help them theorise about the forces from which humankind construct their daily lives.

In the next two years we will create the same amount of digital data that has been created since the beginning of time up until now. The world’s data doesn’t just hold the pool of human knowledge that we call facts; it also holds information about our feelings, our growth, our relationships and interactions with each other and the world.

With all this data, it is becoming an increasing challenge to filter out the information that we need to know from the information that we don’t need to know, which is to say – to filter out the signal from the noise. Due to the volume, velocity and variety of these signals it is getting increasingly difficult to derive meaning from them in a timely manner. The speed at which they are being created can easily be greater than the speed at which we can notice them. Without the right equipment it can be like trying to count the spokes on a spinning wheel.

However, those who can do it are building empires on the value that the data brings. Companies like Google, Wikipedia, Twitter and Facebook cashing in on the value of connecting data with data and sharing that with people.

Jimmy Wales’s vision of making the sum of all human knowledge freely accessible to every single person is well on its way. And whereas Wikipedia serves us with the common facts, online social media is serving us with the personal facts… and in real time. Twitter’s mission is to instantly connect people everywhere to what’s most important to them. Facebook’s mission is to give people the power to share and make the world more open and connected. Google’s mission is to organise the world’s information and make it universally accessible and useful. The high level of success that these companies have achieved is a showcase for what can be achieved with a company mission focussed on organising and delivering data. It is working well for them and there is no reason why it can’t work for us too.

Data is simply data. The commonality of data can level the playing fields, remove boundaries and provide a language that can bring about a common understanding. And the data is flowing free like the water flows down our rivers. All you need to do is get to the water and start processing it in a way that is right for you.

Data could be both the biggest opportunity and the biggest challenge of our modern age.

Imagine being able to know everything that you want to know. Want to know the current temperature in Hawaii, want to know the stock price of Apple Inc., want to know the average house price in New Zealand? And if the information is not there, then what you most often find in its place are some instructions on how to go get it or create it for yourself.

Information is being made available in large quantities, and the future predicts more information to come.

Modern technology is capturing the data and modern culture is sharing it more openly. Together our data and our share-alike culture are rapidly growing the world’s data ecosystem, and this is simultaneously revolutionising the way we interact with each other and the world. It is revolutionising the way we get our news, the way we get our education, the way we are entertained, and the way we do business.

Looking at Twitter alone, we see how social media can impact journalism by turning twitter users into 24-hour citizen journalists. Twitter opens a marketing channel to 600 million people. It enables you to build ongoing customer relationships and insights about what your customers want. Recent social media and big data analysis research performed at the CCI found that the rate of new Twitter accounts is 833 every minute – more on this in a future blog post.

And if it is happening in the external world, it is very likely that it is happening internally within your organisation. Your organisation’s data ecosystem is made up of a number of key areas that are required for you to maintain a successful business. Using the Business Model Canvas we can take a holistic view of these key areas. The Business Model Canvas is a strategic management and entrepreneurial tool that helps you to describe your business model and is a great way to bring forth awareness of the data assets within it.

In your organisation you have information about your customers, your service or product, your delivery of that service or product, your internal and external interactions, your tools and resources, your activities, your partners, your costs and your revenue. These data assets – once identified, mapped and tracked over time – enable deep insights to be discovered about what is important for growth and wellbeing of your organisation. It can show surprising information about the dependent relationship that each data asset has on the other. If you have an average ecosystem, you will produce average results.

Data ecosystems can look like neatly pastured farm fields or they can look like chaotic overgrown rainforests. In either case it is important to maintain the high-value data assets within. Maintaining your high-value data assets is quite easy, all you need to do is regularly ask questions of them. Look at a high level diagram of your data ecosystem and start listing some quality questions you could regularly ask of it. For example, what would happen to your 12 month revenue if you doubled your sales staff? What would it take to double your productivity? What is the cause or your major frustration right now? If the answers are not there then you need take action to ensure there are there. Behind your high quality questions are the high value data assets. And just as it is important to read between the lines, when it comes to data you need to look beneath the numbers.

Overall, then, data is available to your organisation from both internal and external sources. With the volume, velocity and variety of data available via the Internet, it seems that we are all now in the business of big data. You have the tools and data available to you to gain insights. Your insights will help you to move more efficiently towards achieving your business goals. All you need is a plan that brings them together. By establishing a data ecosystem diagram your data assets become more real and enable you to look beneath the numbers. Start now by identifying what the number one question is that you could ask of your data.

One more thing.

Here are the results of the data science experiment that I performed in the presentation, where I asked the 100+ business professionals that attended “what was there number one frustration right now?” Click on the graph for an interactive visualisation, and explore…