All is vanity

Depending on your point of view, Twitter is either a moral cesspit or a source of great inspiration. I can see both sides but as a free source of insight from some outstanding academics and journalists it is hard to beat (though sometimes you do have to wade through a lot of nonsense to find it). The news this week that Elon Musk’s $44 billion bid to buy Twitter has been accepted has raised more than a few eyebrows, generating concerns that the self-styled “free speech absolutist” will turn the platform into even more of a hell-hole than many people already believe it is.

Musk has not always been such a fan. Some years ago he was quoted as saying, “I don't have a Facebook page. I don't use my Twitter account. I am familiar with both, but I don't use them.” When he did finally venture onto Twitter in 2018, his Tweets suggesting that he was contemplating taking Tesla private earned Musk a $40 million securities fraud charge from the SEC. Undeterred by his past experience, the online payments guru turned car-maker cum space explorer appears to be following in the footsteps of 1970s entrepreneur Victor Kiam whose memorable marketing catchphrase for Remington shavers was “I liked it so much, I bought the company.”

Twitter's glory days may be behind it

The motivation for Musk’s involvement remains unclear. The social media segment is increasingly competitive and depending on how it is defined, Twitter does not even rank in the global top 15 most popular social networks. Growth in the number of active Twitter accounts has slowed sharply in recent years, having grown at single digit rates since 2015. Twitter’s preferred metric these days is Monetizable Daily Active Usage (mDAU) which is a measure of users who have logged into the platform and been exposed to adverts. After global mDAU gains of 21% and 27% in 2019 and 2020 respectively, this slowed to 13% in 2021 (chart). More worrying is that growth in the critical US market slowed to 2% last year versus 15% elsewhere. Twitter has been tight-lipped as to whether the slowdown in US activity is anything to do with the January 2021 ban imposed on former President Donald Trump. Whatever the reason, Twitter recorded a second consecutive annual loss last year, with cumulated losses of $1.36 billion over 2020 and 2021.

 

Financing the deal 

The financing arrangements of the buyout are also worthy of comment. Under the terms of his proposed deal, Musk will finance the buyout with $13 billion of debt, $12.5 billion secured against Tesla stock and $21 billion of his own equity. Musk is thus financing more than 70% of the deal from his own funds which runs contrary to standard LBO wisdom in which borrowing is mainly secured against the assets of the target company. There are suggestions that the lending banks are limiting their participation due to concerns that Twitter’s revenue stream has limited growth potential. Moreover, the company’s debt ratio, calculated relative to shareholder’s equity, has been creeping up since 2019, rising from 0.46 to 1.29 by Q1 2022. Even though the debt component of the deal is relatively limited, adding $13 billion of liabilities to the existing $4.2 billion of long-term debt would raise Twitter’s debt ratio to 3.5 which is significantly above the S&P500 average of 1.5 (chart below). Conducting a buyout in a rising interest rate environment will pose additional problems.

A highly indebted company with limited revenue growth potential does not look an attractive investment proposition. Moreover, the fact that the portion secured against Tesla stock takes the form of a margin loan means that if a margin call is triggered, Musk could be forced to sell Tesla stock to meet his commitments. This risks putting downward pressure on Tesla’s price. Roughly speaking, Musk would be on the hook if Tesla stock fell by 43% from the price prevailing on the filing date of 20 April. For the record the price is down 12% in a little over a week, and the trigger point is consistent with the price prevailing in November 2020. The plan to buy Twitter thus poses unnecessary risks to Tesla, which is now a very profitable business with one of the widest profit margins in the auto industry. But if Tesla is so successful why might we expect a price fall? For one thing the rally over the last couple of years has been remarkably strong, which is always a reason to be concerned about a pullback. Second, if Musk becomes distracted by running Twitter and takes his eye off Tesla’s operations there is a risk that any problems experienced by the carmaker are initially missed or become more difficult to fix.

Can Twitter be monetised?

Aside from concerns about the financing of the deal, the episode raises a lot of interesting questions about the valuation of digital content. For a platform such as Twitter, its value is embodied in its network. In theory, Metcalfe’s Law states that a network’s value is proportional to the square of the number of nodes in the network. Thus a network like Twitter with 300 million users has an inherent "node value" of 90 quadrillion. If these were dollars, Musk would be laughing all the way to the bank But monetising Twitter's reach will prove extremely difficult. Even a small subscription fee is likely to deter many users - demand is highly price elastic. Besides, imposing a fee is inconsistent with the vision of Twitter as a “digital town square” as former CEO Dick Costolo once called it. According to media reports, Musk told banks that agreed to help fund the takeover he would crack down on executive pay to slash costs, and would develop new ways to monetize tweets. Maybe Musk does have a plan to generate money from Tweets, but it is not immediately obvious to the many analysts who follow the company.

At this stage of proceedings the financials of Musk’s Twitter deal do not look compelling. Short of a radical overhaul of the business model it is difficult to see how the company can generate the returns which would justify paying $54.20 per share. The fact that the board is prepared to sell at a price 25% below last summer’s high may tell us something about how they view the future. If the deal does go ahead – although it is far from certain that it will – it may go down in history as a vanity project demonstrating the old adage “buy in haste, repent at leisure."

Beware the big data rush

Bank of England chief economist Andy Haldane today gave a speech entitled Will Big Data Keep Its Promise?  in which he assessed the contribution that big data can make to improving decision making in finance and macroeconomics. Whilst I agree that this is indeed a subject that offers significant potential, we do have to be mindful of the downsides associated with the data trails we leave as we live our lives in a digital world.

In 2005 there were around 1 billion global internet users; today there are estimated to be almost 3.5 billion. Just as important, there has been a significant switch from the one-way flow of traffic from suppliers to consumers, which characterised the early years of internet use, to a more interactive medium. Today, users send around 6000 tweets, make 40,000 Google searches and send 2 million emails every second. The capacity of text on the internet is estimated at 1.1 zettabytes, which is approximately 305.5 billion pages of A4 paper and which is projected to rise to 2 zettabytes by 2019 (more than 550 billion sheets). And that is without the pictures! To take another example, the Large Hadron Collider generates 15 petabytes of data each year, equivalent to around 15,000 years of digital music.

Where does all this data come from? Some of it is merely the transcription of existing data into an electronic form that makes it more accessible. Wikipedia, for example, has helped to democratise knowledge in a way that was previously impossible. But a lot of it has come into being as a result of technological developments which allow the capture of much greater volumes of information. This has been facilitated by the rise of cloud computing which allows users to store, manage and process vast amounts of information in a network of remote servers (ironically, this is a reversal of the trend of recent decades which saw a shift from centralised towards local data storage). Perhaps even more important, the rise of social media such as Twitter and Facebook has vastly increased the volume of information pumped out (not to mention the rise of microblogging sites in China such as Tencent or Sina Weibo).

Clearly, a lot of this information does not yield any valuable insight but given the vast amount of available information even a small fraction of it is still too much for humans to reasonably digest. Even if we only require 0.5% of the information stored online, we would still need 1.5 billion sheets of A4. The problem is compounded by the fact that we do not necessarily know what is useful information and what can easily be discarded so we have to scan far more than we require in order to stream out the good stuff. As a result, much progress has been made in recent years to devise methods of scanning large datasets in order to search for relevant information.

To the extent that knowledge is power, it stands to reason that those with the data in the digital age are those with the power. This raises a big question of how much control we should be prepared to give up, and there are legal issues about who owns the information that most of us have until now simply given away for free – something that the recent Facebook furore brought into the open.

But whilst social media platforms contain huge amounts of data that can be extracted at relatively little cost, and are often a useful barometer of public opinion, they are biased towards younger, urban-dwelling high income users. Relying on Tweets, for example, without accounting for this bias risks repeating the classic mistake made when trying to predict the US presidential results in 1936 and 1948, when the polling samples were skewed by the inclusion of those picked at random from the phonebook, at a time when telephone penetration was low.

Thus, whilst I agree with Haldane’s sentiment that “economics and finance needs to make an on-going investment in Big Data and data analytics” we need to beware of the headlong rush. As I wrote in a piece last year, “before too long, there will almost certainly be a spectacular miss which will bring out the critics in droves” and it could yet be that the Facebook problems will be a catalyst for a rethink. At the present time, much of society is only operating in the foothills of the big data revolution. The real trick, as former boss of Hewlett-Packard Carly Fiorina once said, will be to turn data into information, and information into insight. We are not quite there yet.

Whose data is it anyway?

To the extent that economics is concerned with the study of how resources are allocated, a system of property rights impacts on the way these resources can be used. For example, if a person owns a piece of land they can choose (within limits) what to do with it e.g. build a house or let it lie fallow. Other people have no right to determine how the land can be used. In a modern market economy, transactions between individuals involve the transfer of property rights and form the basis of the price determination process we see at work every day. These rights are backed up by a legal system designed to enforce the entitlement to a given bundle of goods (or services) and to record their transfer from one person to another.

However, in the digital age the distinction of property rights has become much more blurred. I was reminded of this recently by an article in The Economist which quoted Nikhil Pahwa, an Indian digital-rights activist, as saying “When they say, ‘Big data is the new oil,’ I answer, ‘But my data is not your resource.’” The context of his quote is India’s biometric ID scheme, Aadhaar, whose database is apparently rather leaky with the result that many people’s personal details find their way into the public domain. But it could equally be applied to the likes of Facebook, which owns the world’s largest personal dataset. At issue is whose data is it? 

Technically, of course, it belongs to the individual who posted it. But Facebook’s terms of service state quite explicitly that “you grant us a non-exclusive, transferable, sub-licensable, royalty-free, worldwide license to use any IP content that you post on or in connection with Facebook.” In other words,  although you own the content Facebook has carte blanche to do what they want with it. From the company’s perspective this is great because it has a huge database upon which it can let loose its AI algorithms to generate ever more sophisticated consumer profiles. One of the great concerns expressed by network campaigners is that such huge databases act as a barrier to entry to smaller companies attempting to break into a particular market, because the lack of access to data means that their consumer profiling will always be inferior.

And this takes us right back to Pahwa’s point: Is it right that the data which we own, and which we give away for free, should be used by a profit maximising organisation to enrich shareholders? In their defence, big data companies argue that they do not charge for their services – Google clicks do not cost the user, so in that sense we are getting something for nothing. Except that is not quite true because we pay for it by giving up some data about ourselves, which may be trivial in isolation but when combined with the billions of pieces from other users, goes to make up a huge mosaic which Google can use to target its adverts more effectively. 

In an interesting paper by Imanol Arrieta and co-authors, the argument is made that data providers should be paid for the information they yield in order that they are compensated for their contribution to the world of AI – information which might in due course be used to displace workers replaced by machines. As data hoarding by Big Data companies increasingly raises public interest concerns, it is likely to provoke the interest of regulators keen to cut down the monopoly power of Google, Facebook et al. It would not be the first time that regulators have taken an interest in tech-related issues: Twenty years ago, the US government opened antitrust proceedings against Microsoft, accusing it of establishing a monopoly position and engaging in anti-competitive practices. And if data really is the new oil, as many commentators contend, recall how in the early twentieth century the US government forced the breakup of Standard Oil, accusing it of being an illegal monopoly.

Big Data companies are already potentially feeling the heat from the US Federal Communications Commission, which voted in December to dismantle its existing net neutrality rules. These rules prevent broadband suppliers from treating different groups of consumers differently, and the likes of Google, Facebook et al are concerned that changes to the rules could impact upon their business models if they are discriminated against by internet service providers (ISPs). As an aside, there are many who argue that net neutrality impinges on the property rights of ISPs, but that is a subject for another day. 

In order to alleviate regulators concerns, it might be prudent for the Big Data outfits to take some pre-emptive actions which show that they are taking mounting social concerns more seriously. For example, there is a case for suggesting that at least part of the data they collect could be shared across a range of platforms thus creating an open-source database (after suitable efforts have been made to anonymise it). After all, it is a public resource – it is “our” information. Of course, this might mean an end to much of the apparently “free” content currently available online. However, both the tech industry and society as a whole are going to have to do some hard thinking about how to balance privacy issues against the cost of online services. If this does not happen, it is likely that government will take the decisions for us, which may not be to anyone’s liking.

How to spot a fake

They say that if you can fake sincerity you've got it made. These days it's fake news we worry about. But it is rare that anyone goes to such great lengths to highlight it as this fascinating Twitter thread I found the other day (also picked up by The Times). A data scientist with the Twitter handle Conspirador Norteño (CN) observed that bot and troll accounts on Twitter often have names that end with 8 random digits. He then took the time to trawl through a series of Twitter accounts, searching for those that referenced #unitetheright and #firemcmaster, both of which are trends followed by those on the Alt-right end of the political spectrum, and found 824 accounts with an 8 digit handle at the end of their user name. Searching their followers for similarly named accounts, and subsequently their followers' followers yielded 63099 accounts. It was (for CN) a simple task to trawl through the followers of these accounts in order to plot the node network. This research yielded the nugget that the largest node in the network belongs to a David Jones based in Southampton.

It then starts to get a little murky thereafter. CN observed that said account posted only between 8am to 8pm Moscow time "almost like it's his job or something". Breaking down the subject matter of the account reveals that this account posted a lot about Ukraine in 2014, then in 2016 moved on to the issues of Brexit and Trump (see chart). Some of the material on the Brexit topic was very inflammatory, particularly with regard to immigration. When it came to the US election, CN pointed out that the language was very similar to that used by adherents of the Alt-right, despite the fact the poster was supposed to be British. CN concludes that this account was "one of the more interesting troll accounts I've seen (and almost certainly human operated and not a bot)". Furthermore, the variety of topics was "aligned with the interests of the Kremlin at the time."

Now we may be maligning David Jones of Southampton unfairly and perhaps he really feels very strongly about the issues at stake. But as one commentator pointed out, if it were a UK based Tweeter their times would vary with the switch to daylight saving in the UK, which would change the time vis-a-vis Russia which has no DST. But they do not, hence accentuating CN's suspicions. This clearly highlights the ease with which it is possible to influence issues of the day by disseminating a particular view and creating a fake network of followers to provide "likes" and recommendations.

This is not to say that the likes of Russia are unduly influencing the democratic process in western economies – at least no more so than usual. Foreign powers have always used propaganda to influence beliefs in other countries. There is evidence to suggest its use as far back as the sixth century BC and it reached new heights during the Second World War, and the Cold War that followed. Even today, the TV channel RT and the BBC World Service provide a view of the world as seen from Moscow and London respectively.  Fake news is not new either: One of the more historically notable events was the publication by the Daily Mail of the infamous Zinoviev Letter in 1924 which purported to be a directive from the head of the international Communist movement, based in Moscow, to the British Communist Party encouraging it to engage in seditious activities. What appeared to be a direct attempt to influence British domestic policy turned out to be a forgery, but it cast a shadow over the Labour Party for decades thereafter, which (unfairly) blamed its heavy election defeat on the letter.

However, the rise of social media has changed the way in which propaganda can be disseminated. For one thing, it is easy to maintain online anonymity which means we can never be 100% sure of the source of the material. Moreover, social media operates on a decentralised basis so that it is straightforward to set up a series of apparently independent channels all feeding the same message. In this way, the message can be drip-fed rather than blasted out.

The impact of fake news on voting patterns is believed to be very small. In one study (Spenkuch and Toniatti 2016[1]) the authors suggest that exposing voters to one additional television campaign ad changes vote shares by approximately 0.02 percentage points. If exposure to one TV ad is as persuasive as one fake news article, each fake tweet influences voting patterns by mere hundredths of a percent. Preaching to the converted will not win more votes, so it does not matter how many times those convinced of a particular view are exposed to fake news because they only have one vote. But the cumulative effect of many thousands of such fake messages will start to mount up if they then influence other voters who otherwise might not be susceptible to such tactics. According to one source (Gottfried and Shearer 2016[2]), 62 percent of US adults get their news from social media and 18 percent do so often, with Facebook the most popular medium. Not everyone will believe the fake news of course, but the dissemination of fake news may have more of an effect than we often credit.

This appears to be a serious problem but I will leave it for others to debate the impact on voting patterns. As an economist, my concern is how such tactics could change the way politicians react to the groundswell of apparent public opinion. If social media is abuzz with reports of how health spending, for example, is scandalously low, do governments react by changing their priorities in order to win votes at the next election? And how would they do so: Do they change their defence budget? Given the low costs associated with fake news dissemination, it is easy to understand why foreign powers with a different world view might try to influence the policies of other governments. But the same applies to domestic interest groups which want to prioritize spending on one area over another. Companies might also apply the same tactics to make the case for a change in the law. If anything this highlights the extent to which all members with a vested interest in our society have a duty to do a little bit of due diligence rather than simply accepting the newsfeeds put before us.

Otherwise, as the often prescient and always quotable HL Mencken put it, "As democracy is perfected, the office of president represents, more and more closely, the inner soul of the people. On some great and glorious day the plain folks of the land will reach their heart's desire at last and the White House will be adorned by a downright moron."

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[1] Spenkuch, Jörg L., and David Toniatti. 2016 “Political Advertising and Election Outcomes.” CESifo Working Paper Series 5780 
[2] Gottfried, Jeffrey, and Elisa Shearer. 2016. “News Use across Social Media Platforms 2016.” Pew Research Center, May 26. http://www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016