Summary: Institutionalised forms of content regulation rest on the realistic assumption that all published content can been monitored and made to conform. If you can’t establish this expectation, this form of regulation becomes instantly redundant. That is why applying the old, publication-based regulatory model to Facebook et al is a distraction that only serves to make politicians feel good about themselves while actually increasing the dangers of online hate and fake news.
In the UK the phrase of ‘leaves on the line’ is firmly established within the national conversation as an example of an unacceptable corporate excuse. It is an excuse rail operators use around this time of year to explain delays to trains. The reason it is deemed unacceptable is that leaves fall off trees every year and have done so for quite some time and thus there is a realistic expectation that this is a problem rail operators should have cracked by now.
Which brings me to another problem where an expectation is building of a corporate fix: fake news and all forms of inappropriate online content/behaviour. This has clearly become something of big issue in recent times to the extent that governments are under considerable pressure to Do Something. And the Thing they are mostly looking to Do is to turn around and tell Facebook, Google, Twitter et al that they need to Do Something. In essence what governments are looking for them to do is assume a publishers responsibility for the content that appears on their platforms. The most recent example of this is the private members bill just introduced by the UK Member of Parliament, Lucy Powell (of which more in a moment).
You can see why this is a popular approach. In the first instance, it allows government to deflect responsibility away from itself or, at the very least, create an imagined space where established regulatory approaches can continue to have relevance. It is an approach which finds favour with the traditional media, which has to operate under conventional publication responsibilities and resents the fact that these new players are eating their advertising lunch while avoiding such constraints. To an extent, it even plays to the agenda of Facebook and Google themselves, because they know that in order to attract the advertising shilling, they need to present themselves as a form of media channel, if not a conventional form of publication. Facebook and Google also know that, despite all the regulatory huffing and puffing, governments will not be able to effectively deploy most of the things they are currently threatening to do – because the space they are trying to create is a fantasy space.
The trouble is – this approach will never work. Worse than that, it is dangerous. Continue reading →
Mark Zuckerberg’s appearance before Congress is a good example of the extent to which politicians and regulators have no idea, to quote The Donald, of “what on earth is going on”. It is not just them, this lack of understanding extends into the communities of thought and opinion framed by academia and journalism. This is a problem, because it means we have not yet identified the questions we need to be asking or the problems we need to be solving. If we think we are going to achieve anything by hauling Mark Zuckerberg over the coals, or telling Facebook to “act on data privacy or face regulation”, we have another think coming.
This is my attempt to provide that think.
The Google search and anonymity problem
Let’s start with Google Search. Imagine you sit down at a computer in a public library (i.e. a computer that has no data history associated with you) and type a question into Google. In this situation you are reasonably anonymous, especially if we imagine that the computer has a browser that isn’t tracking track search history. Despite this anonymity, Google can serve you up an answer that is incredibly specific to you and what it is you are looking for. Google knows almost nothing about you, yet is able to infer a very great deal about you – at least in relation to the very specific task associated with finding an answer to your question. It can do this because it (or its algorithms) ‘knows’ a very great deal about everyone or everything in relation to this specific search term.
So what? Most people sort of know this is how Google works and understand that Google uses data derived from how we all use Google, to make our individual experiences of Google better. But hidden within this seemingly benign and beneficial use of data is the same algorithmic process that could drive cyber warfare or mass surveillance. It therefore has incredibly important implications for how we think about privacy and regulation, not least because we have to find a way to outlaw the things we don’t like, while still allowing the things that we do (like search). You could call this the Google search problem or possibly the Google anonymity problem, because it demonstrates that in the world of the algorthm, anonymity has very little meaning and provides very little defence.
The Stasi problem
When you frame laws or regulations you need to start with defining what sort of problem you are trying to solve or avoid. To date the starting point for regulations on data and privacy (including the GDPR – the regulation to come) is what I call the STASI problem. The STASI was the East German Security Service and it was responsible for a mass surveillance operation that encouraged people to spy on each other and was thus able to amass detailed data files on a huge number of its citizens. The thinking behind this, and indeed the thinking applied to the usage of personal data everywhere in the age before big data, is that the only way to ‘know’ stuff about a person is to collect as much information about them as possible. The more information you have, the more complete the story and the better your understanding. At the heart of this approach is the concept that there exists in some form a data file on an individual which can be interrogated, read or owned.
The ability of a state or an organisation to compile such data files was seen as a bad thing and our approach to data regulation and privacy has therefore been based on trying to stop this from happening. This is why we have focused on things like anonymity, in the belief that a personal data file without a name attached to it becomes largely useless in terms of its impact on the individual to whom the data relates. Or we have established rights that allow us to see these data files, so that we can check that they don’t contain wrong information or give us the ability to edit, correct or withdraw information. Alternatively, regulation has sought to establish rights for us to determine how our the data in the file data is used or for us to have some sort of ownership or control over that data, wherever they may be held.
But think again about the Google search example. Our anonymity had no material bearing on what Google was able to do. It was able to infer a very great deal about us – in relation to a specific task – without actually knowing anything about us. It did this because it knew a lot about everything, which it had gained from gathering a very small amount of data from a huge number of people (i.e. everyone who had previously entered that same search term). It was analysing data laterally, not vertically. This is what I call Google anonymity, and it is a key part of Google’s privacy defence when it comes to things such as gmail. If you have a gmail account, Google ‘reads’ all your emails. If you have Google keyboard on your mobile, Google ‘knows’ everything that you enter into your mobile (including the passwords to your bank account) – but Google will say that it doesn’t really know this because algorithmic reading and knowledge is a different sort of thing. We can all swim in a sea of Google anonymity right up until the moment a data fisherman (such as a Google search query) gets us on the hook.
The reason this defence (sort of) stacks-up is that Google can only really know your bank account password is if it analyses your data vertically. The personal data file is a vertical form of data analysis. It requires that you mine downwards and digest all the data to then derive any range of conclusions about the person to whom that data corresponds. It has its limitations, as the Stasi found out, in that if you collect too much data you suffer from data overload. The bigger each file becomes the more cumbersome it is to read or digest the information that lies within it. It is a small data approach. Anyone who talks about data overload or data noise is a small data person.
Now while it might have been possible to get the Stasi to supply all the information it has on you, the idea that you place the same requirement on Google is ridiculous. If I think about all the Google services I use and the vast amount of data this generates, there is no way this data could be assembled into a single file and even if it could, it would have no meaning, because the way it would be structured has no relevance to the way in which Google uses this data. Google already has vastly more data on me than the biggest data file the STASI ever had on a single individual. But this doesn’t mean that Google actually knows anything about me as an individual. I still have a form of anonymity, but this anoymity is largely useless because it has no bearing on the outcomes that derive from the usage of my data.
The KitKat Problem
Algorithms don’t suffer from data overload, not just because of the speed at which they can process information but because they are designed to create shortcuts through the process of correlations and pattern recognition. One of the most revealing nuggets of information within Carole Cadwalladr’s expose of the Facebook / Cambridge Analytica ‘scandal’ was the fact that a data agency of the like of Cambridge Analytica working for a state intelligence service had discovered a correlation between people who self-confess to hating Israel and a tendency to like Nike trainers and KitKats. This exercise, in fact, became known as Operation KitKat. To put it another way, with an algorithm it is possible to infer something very consequential about someone (that they hate Israel) not by a detailed analysis of their data file, but by looking at consumption of chocolate bars. This is an issue I first flagged back in 2012.
I think this is possibly the most important revelation of the whole saga, because, as with the Google search example it cuts right to the heart of the issue and exposes the extent to which our current definition of the problem is so misplaced. We shouldn’t be worrying about the STASI problem, we should be worried about the KitKat problem. Operation KitKat demonstrates two of the fundamental characteristics of algorithmic analysis (or algorithmic surveillance). First, not only can you derive something quite significant about a person based on data that has nothing whatsoever to do with what it is you are looking for. Second, algorithms can tell you what to do (discover haters of Israel by looking at chocolate and trainers) without the need to understand why this works. An algorithm cannot tell you why there is a link between haters of Israel and KitKats. There may not even be reason that makes any sort of sense. Algorithms cannot explain themselves, they leave no audit trail – they are the classic black box.
The reason this is so important is that it drives a cart and horse through any form of regulation that tries to establish a link between any one piece of data and the use to which that data is then put. How could one create a piece of legislation that requires manufacturers or retailers of KitKats to anticipate (and otherwise encourage or prevent) data about their product being used to identify haters of Israel? It also scuppers the idea that any form protection can be provided through the act of data ownership. You cannot make the consumption of a chocolate bar or the wearing of trainers a private act, the data on which is ‘owned’ by the people concerned.
KitKats and trainers bring us neatly to the Internet of Things. Up until now we have been able to assume that most data is created by, or about, people. This is about to change as the amount of data produced by people is dwarfed by the amount of data produced by things. How do we establish rules about data produced by things especially when it is data about other things? If your fridge is talking to your lighting about your heating thermostat, who owns that conversation? There is a form of Facebook emerging for objects and it is going to be much bigger than the Facebook for people.
Within this world the concept of personal data as a discrete category will melt away and instead we will see the emergence of vaste new swathes of data, most of which is entirely unregulatable or even unownable.
The digital caste problem
A recent blog post by Doc Searls has made the point that what Facebook has been doing is simply the tip of an iceberg, in that all online publishers or any owners of digital platforms are doing the same thing to create targeted digital advertising opportunities. However, targeted digital advertising is itself the tip of a much bigger iceberg. One of Edward Snowden’s Wikileaks exposures concerned something known as Operation Prism. This was (probably still is) a programme run by the NSA in the US that involves the abilty to hoover-up huge swathes of data from all of the world’s biggest internet companies. Snowden also revealed that the UK’s GCHQ is copying huge chunks of the internet by accessing the data cables that carry internet traffic. This expropriation of data is essentially the same as Cambridge Analytica’s usage of the ‘breach’ of Facebook data, except on a vastly greater scale. Cambridge Analytica used their slice of Facebook to create a targeting algorithm to analyse political behaviour or intentions, whereas GCHQ or the NSA can use their slice of the internet to create algorithms that analyse the behaviour or intentions of all of us about pretty much anything. Apparently GCHQ only holds the data it copies for a maximum of 30 days, but once you have built your algorithms and are engaged in a process of real-time sifting, the data that you used to build the agorithm in the first place, or the data that you then sift through it, is of no real value anymore. Retention of data is only an issue if you are still thinking about personal data files and the STASI problem.
This is all quite concerning on a number of levels, but when it comes to thinking about data regulation it highlights the fact that, provided we wish to maintain the idea that we live in a democracy where governments can’t operate above the law, any form of regulation you might decide to apply to Facebook and any current or future Cambridge Analyticas also has to apply to GCHQ and the NSA. The NSA deserves to be put in front of Congress just as much as Mark Zuckerberg.
Furthermore, it highlights the extent to which this is so much bigger than digital advertising. We are moving towards a society structured along lines defined by a form of digital caste system. We will all be assigned membership of a digital caste. This won’t be fixed but will be related to specific tasks in the same way that Google search’s understanding of us is related to the specific task of answering a particular search query. These tasks could be as varied as providing us with search results, to deciding whether to loan us money, or whether we are a potential terrorist. For some things we may be desirable digital Brahmins, for others we may be digital untouchables and it will be algorithms that will determine our status. And the data the algorithms use to do this could come from KitKats and fridges – not through any detailed analysis of our personal data files. In this world the reality of our lives becomes little more than personal opinion: we are what the algorithm says we are, and the algorithm can’t or won’t tell us why it thinks that. In a strange way, creating a big personal data file and making this available is the only way to provide protection in this world so that we can ‘prove’ our identity (cue reference to a Blockchain solution which I could devise if I knew more about Blockchains), rather than have an algorithmic identity (or caste) assigned to us. Or to put it another way, the problem we are seeking to avoid could actually be a solution to the real problem we need to solve.
The digital caste problem is the one we really need to be focused on.
So – the challenge is how do we prevent or manage the emergence of a digital caste system. And how do we do this in a way which still allows Google search to operate, doesn’t require that we make consumption of chocolate bars a private act or regulate conversations between household objects (and all the things on the Internet of Things) and can apply both to the operations of Facebook and the NSA. I don’t see any evidence thus far the the great and the good have any clue that this is what they need to be thinking about. If there is any clue as to the direction of travel it is that the focus needs to be on the algorithms, not the data they feed on.
We live in a world of a rising tide of data, and trying to control the tides is a futile exercise, as Canute the Great demonstrated in the 11th century. The only difference between then and now is that Canute understood this, and his exercise in placing his seat by the ocean was designed to demostrate the limits of kingly power. The GDPR is currently dragging its regulatory throne to the waters edge anticipating an entirely different outcome.
For the last year Carole Cadwalladr at the Observer has been doing a very important job exposing the activities of Cambridge Analytica and its role in creating targeted political advertising using data sourced, primarily, from Facebook. It is only now, with the latest revelations published in this week’s Observer, that her work is starting to gain political traction.
This is an exercise in shining a light where a light needs to be shone. The problem however is in illuminating something that is actually illegal. Currently the focus is on the way in which CA obtained the data it then used to create its targeting algorithm and whether this happened with either the consent or knowledge of Facebook or the individuals concerned. But this misses the real issue. The problem with algorithms is not the data that they feed on. The problem is that an algorithm, by its very nature, drives a horse and cart through all of the regulatory frameworks we have in place, mostly because these regulations have data as their starting point. This is one of the reasons why the new set of European data regulations – the GDPR – which come in to force in a couple of months, are unlikely to be much use in preventing US billionaires from influencing elections.
If we look at what CA appear to have been doing, laying aside the data acquisition issue, it is hard to see what is actually illegal. Facebook has been criticised for not being sufficiently rigourous in how it policed the usage of its data but, I would speculate, the reason for this is that CA was not doing anything particularly different from what Facebook itself does with its own algorithms within the privacy of its own algorithmic workshops. The only difference is that Facebook does this to target brand messages (because that is where the money is), whereas CA does it to target political messages. Since the output of the CA activity was still Facebook ads (and thus Facebook revenue), from Facebook’s perspective their work appeared to be little more that a form of outsourcing and thus didn’t initially set-off any major alarm bells.
This is the problem if you make ownership or control of the data the issue – it makes it very difficult to discriminate between what we have come to accept as ‘normal’ brand activities and cyber warfare. Data is data: the issue is not who owns it, it is what you do with it.
We are creating a datafied society, whether we like it or not. Data is becoming ubiquitous and seeking to control data will soon be recognised as a futile exercise. Algorithms are the genes of a datafied society: the billons of pieces of code that at one level all have their specific, isolated, purpose but which, collectively, can come to shape the operation of the entire organism (that organism being society itself). Currently, the only people with the resources or incentive to write society’s algorithmic code are large corporations or very wealthy individuals and they are doing this, to a large extent, outside of the view, scope or interest of either governments or the public. This is the problem. The regulatory starting point therefore needs to be the algorithms, not the data, and creating both transparency and control over their ownership and purpose.
Carol’s work is so important because it brings this activity into view. We should not be distracted or deterred by the desire to detect illegality because this ultimately plays to the agenda of the billionaires. What is happening is very dangerous and that danger cannot be contained by the current legal cage, but it can be constrained by transparency.
As well as the bubble angle, Carole also focused on the disrupt angle implicit in the event’s title and noted the extent to which the D word is inserted into all the pitches. The logic here appears to be: 1) look at the businesses that have become successful and that we wish to emulate 2) identify a common characteristic of all of these success stories, i.e. that they were all disruptive 3) reach the conclusion that disruption is therefore the key to success.
Here is a report on research from Brandwatch that I think neatly encapsulates where many brands have got to in terms of understanding and using social media. OK, so it doesn’t actually say that swimmers are failing at mountain climbing, it says are that retailers are ‘failing’ on Facebook and Twitter because they are failing to listen and respond to their audiences. But it may as well talk about swimmers and mountains because while it has identified the failure bit, it has reached the wrong conclusion about why the failure is occuring or what to do about it.
The failure is one of defining the challenge, it is not a failure of insufficient activity. The challenge in the social media space is defined by behaviour identifiction and response (i.e. it is all about swimming). The challenge in the traditional media / marketing space was all about channel and message, reach and frequency (i.e. mountain climbing). So while Brandwatch correctly identified the problem i.e. failure to listen and respond it didn’t realise this was happening because it was positioned against the wrong challenge. In effect the article is saying “you are failing to swim up this mountain, but if you flayed your arms about more frequently and thrashed your legs more vigourously you would be more successful .” True enough, but you wouldn’t be that much more successful. What the article should really say is “you are failing at swimming, but that is because you are trying to climb a mountain rather than cross a lake”. Right activity, wrong context. If swimming is what it is all about, look for a lake, don’t look for a mountain.
In reality, the reason most brands are not responding effectively is because this is not what they got into social media to do. Listening and response is seen as the rather awkward consequence of being in the social media space. It is seen as a cost that needs to be paid in order to fulfill the ambition of spreading their content far and wide and maximising ‘engagement’ with their audience. And like all costs, it should therefore be kept to a minimum.
Social media is entirely a behaviour identification and response challenge. It is not a channel and message / reach and frequency challenge. Listening and response is not a cost, it is the source of value creation. Most brands haven’t really grasped this yet – but at least they are starting to realise that what they are doing has a problem (sort of).
Let’s be honest. Using a brand Facebook page to reach your consumer audience was always a stupid idea. Organic reach, as it is now called, has always been a waste of time. It hasn’t been Facebook and its actions over time which has made it so. By finally making Facebook a pay-to-pay venue, Facebook has simply done the decent thing and killed something that should never have been brought to life in the first place.
Now Facebook’s motives for doing this can be questioned. They say it is to improve the user experience, I say it is to improve the shareholder experience – but that is a subject for a whole new (old) post.
Facebook has form in this respect. When it introduced Timeline a few years back it killed off the idea that the objective for brands was to try and make your Facebook page look like your website. Do you remember that time? We even had respected digital consultancies lauding those brands that had overcome the ‘static format’ of Facebook in order to create ‘brilliant’ Facebook pages. I guess the logic went like this: here is this thing called Facebook which we don’t really understand. Here is this thing called a website which we do understand. Make our Facebook page look like our website and ‘hey prestos’ we can therefore understand Facebook. Stupid, but none-the-less a lot of digital agencies made a lot of money helping brands do this – so not so stupid from someone’s perspective I guess.
Again, Facebook said it did this to allow users to tell and record their life stories within Facebook. I say they did that to encourage users to input more data into Facebook – but that is the subject for a whole new (old) post.
Social media is not a space where audiences naturally exist. Creating audiences in social media is always going to be a fruitless task. Engagement rates with brand Facebook pages have always been miniscule, expressed as a percentage of your total ‘target’ audience (this dates back way before Facebook started making it difficult to create ‘engagement’ with ‘organic’ posts). Social media is not a medium of distribution, it is a medium of connection. The name of the game is not channel and message / reach and frequency – it is about behaviour identification and response. The world of social media is a world of the individual, not a world of the audience. Traditional media is a high reach but low engagement space, social media is a low reach but high engagement space. You can only use social media effectively to deal with very small numbers of people at any one time, but the value you can (must) create from these contacts therefore has to an order of magnitude greater than that when all you were doing was pushing content at them. This has been the subject for a whole old blog.
So don’t blame Facebook for finally killing your organic Facebook engagement content brandfill nonsense strategy. Facebook is simply doing you a favour.
Twitter is basically comprised of an idea, some geeks and some server space. The last of these are not precious or scarce resources and the idea is basically now a sunk cost. Not just for Twitter, but for anything that aspires to be Twitter like.
It therefore doesn’t cost much to be Twitter (or Twitterlike). Logically speaking therefore, the revenue opportunities for Twitter, long-term, are likely to be similarly low. The problem for Twitter (and Facebook) is not generating sufficient revenue to cover its costs, certainly not the costs of delivering the service its users want. Its problem is generating sufficient revenue to justify its share price.
In the chase for this revenue, an Ipola sufferer turns away from its users and focuses on marketing directors. It tries to turn itself into a media platform or a data mine, because that is the only way it can seduce the marketing dollar. And in the process it basically destroys what it was that made it successful in the first place. Its vital organs start to fail.
Twitter is basically a conversation. Take the chronology out of conversation and it stops working.
Twitter is never going to be some sort of content lillypad – which has always been its problem. It has no real estate on which advertising dollars can settle.
Which would all be fine, if it didn’t have try and keep the boys on Wall Street happy.
Ipola is not going to kill Twitter just yet, although it is going to run a sweat. What will kill Twitter is when the market gets infected by a competitor – and users realise how easy it is to swap, because the size of your accumulated Twitter following means nothing (because they are not actually an audience), you don’t follow handles anymore, you follow or search hashtags (in real-time), and if you do want to follow someone (or have them follow you) they are still only a click away. Doing a factory reset on your Twitter following is basically a good thing because it means you only get the ones back which were worth anything in the first place.
The only course of treatment for Twitter (and Facebook and LinkedIn) is to recognise your stock is going to become a devalued currency when Wall Street finally realises you are never going to hit the long-term revenue expectations, so use it while it is trading at such a ridiculous premium to buy other companies that can then become the lifeboats for when the business model sinks.
Wednesday saw an important announcement from the New York Attorney General. He announced that Barclays Bank is to be prosecuted concerning their operation of a ‘dark pool’. A dark pool is basically a private trading area which a bank can operate on behalf of its clients, or anyone else to whom the bank grants access. It is dark because it doesn’t operate to the same level of transparency as conventional exchanges. The accusation is that Barclays allowed high frequency traders into their dark pool and allowed these traders to prey on the trading activity of the other investors within the pool, including Barclays’ own clients.
This is an astonishingly important announcement for two reasons: First, it is important for Wall Street but it also important for Facebook, Google, Big Data, data protection, the Internet of Things and thus, quite possibly therefore the future of humanity itself.
First Wall Street: What is happening within Barclays’ dark pool is almost certainly similar to what is happening in the dark pools operated by almost all the major banks. It is also pretty similar to what is happening in the ‘light pools’ that constitute the official Wall Street stock exchanges (just read Michael Lewis’s ‘Flash Boys’, published a few weeks ago if you want validation of this). This will therefore be a test case and rather than go after one of the Big Beasts, the Attorney General has sensibly chosen to pick off an already wounded juvenile. Barclays is a foreign bank, it is a peripheral player (albeit one with a very large dark pool) and it is already discredited by it actions in rigging inter-bank lending rates. It is therefore easy prey, but bringing it down will provide the ammunition necessary to tackle, or at least discipline, the major players. You can bet that there are a lot of people on Wall Street right now really focused on how this case plays out, even if the mainstream media has yet to really wake-up to its significance.
But this isn’t about just about Wall Street. What is playing out here are the first attempts to understand and regulate the world of the algorithm. High frequency trading is driven by algorithms and exploits one of an algorithm’s principle characteristics, which is its speed in processing large amounts of data. High frequency trading illustrates the power of algorithms and also their potential for abuse. High frequency trading is not illegal (yet), but it is abusive. It is only not illegal because the law makers don’t really understand how algorithms work and no-one has worked out a way to stop people who do understand them from using them in an abusive way. Interestingly the Attorney General has not tried to establish that high frequency trading is illegal, rather that Barclays misrepresented its dark pool as offering protection from the abusive behaviour of high frequency traders.
Algorithms colonised Wall Street for two reasons: first Big Data was already there in the form of the vast amount of information flowing through the financial markets and; second, Wall Street could afford to pay top-dollar for the relatively small group of geeks who actually understand algorithms. But this is about to change. The pool of geeks is expanding and pools of data, large enough for complex algorithms to operate within, are now developing in many other places, driven by the growth of Big Data and the Internet of Things.
Which brings us to Facebook. In many ways Facebook is a dark pool, except the data within it isn’t data about financial trading, it is data about human behaviour. Now I don’t want to suggest that Facebook is trading this information or necessarily inviting access to this data for organisations who are going to behave in an abusive or predatory way. In a somewhat ironic sense of role reversal, the PRISM affair has revealed that the regulators (i.e. the NSA and the UK’s GCHQ) are the equivalent of the high frequency traders. They are the people who want to get into Facebook’s dark pool of data so they can feed it through their algorithms and Facebook has been doing what (little) it can to resist their entry. But of course there is nothing at the moment to really stop Facebook (or for that matter Google or Twitter) from allowing algorithms into their data pools. In fact, we know they are already in there. While there may not be abusive activity taking place at the moment there is nothing to stop abusive behaviour from taking place, other than the rules of integrity and behaviour that Facebook and Google set for themselves or those that might be set by the people Facebook or Google allow into their pools. Remember also that Facebook needs to generate sufficient revenue to justify a valuation north of $80 billion – and it is not going to do that simply through selling advertising, it is going to do that by selling access to its pool of data. And, of course, the growth of Big Data and the Internet of Things is creating vast data pools that exist in far more shadowy and less obvious places that Google and Facebook. This is a recipe for abusive and predatory behaviour, unless the law-makers and regulators are able to get there first and set-out the rules.
Which brings us back to New York versus Barclays. It is not just Wall Street and financial regulators who need to focus on this: this could prove to be the opening skirmish in a battle that will come to define how society will operate in the world we are now entering – the world of the algorithm. I can’t lay claim to understanding how this may play out, or how we are going to regulate the world of algorithms. The only thing I do know is that the abusive use of algorithms flourishes in the dark and the daylight of transparency is their enemy. Trying to drive a regulatory stake through the heart of every abusive algorithm is a near self-defeating exercise – far better is to create an environment where they don’t have a competitive advantage.
Every month I receive an email from measurement / metrics company SocialBakers alerting me to the latest league table of performance for UK Facebook pages. I usually avoid opening this email because it depresses me, perpetuating as it does, the view that Facebook activity and social media in general is a numbers game that is all about creating the maximum number of fans and this thing called engagement. However, this month I took a look, just to see if things were changing. They were not. The part of the report that always depresses me the most, remained depressing. I have shown it below. Continue reading →
(This is a page, but it was meant to be a post. Here is the proper post. I have kept this page alive because people have RT’d the link, but I have hidden it from navigation)
At the end of last year I was teaching a session on social media in a masters of communication course at the London campus of the European Communication School. In total I was lecturing to 30 students in two groups. Most of the students were French or Belgian and in their early twenties. At the start of the course I conducted an exercise designed to define how people actually use Facebook, based on the students’ own experiences. What this exercise revealed was that everyone used Facebook to keep in contact with their friends and that this activity constituted the vast majority of the time spent on Facebook. No real surprise there.
I then looked at engagement with brands. Of the 30, only three confessed that they used Facebook to have any sort of contact with brands and of these three, two only did this in response to some form of incentive – getting freebies, entering competitions etc. And only one person said that they used Facebook to follow brands in any proactive way – albeit time spent doing this was very small, compared to time spent keeping in contact with friends.