As Twitter’s revenue growth slows and its share price continues to slide, it’s not surprising to find the company’s costs being scrutinised. Earlier this week Techcrunch looked at executive pay and the remuneration of CFO/COO Anthony Noto in particular.

Research and development expenditure is a key area for a technology company like Twitter. You would expect it to be a significant cost, and sure it enough it is. You would also expect to see a significant return from such investment in user growth and ultimately in revenue. Twitter’s lack of user growth has been widely discussed so I’ve focussed on the $s.

Twitter’s 2016 R & D Performance

Twitter spent $713m on research and development in 2016.

At the same time its net revenue (revenue minus the direct costs of earning that revenue) grew by $109m, a 7.3 per cent increase on 2015.

That represents $0.15 of additional net revenue for every $1 spent on R&D.

Facebook’s performance 10x better

To put that in context Facebook’s equivalent numbers were:

• R & D spend – $5,919m
• Net revenue growth $8,788m.
• $1.48 of additional net revenue for every $1 spent on R&D.

If Twitter had achieved the same level of net revenue growth for every $ spent on R&D its net revenue would have risen by over $1bn in 2016 ($1.48 x $713m = $1,055m).

This would have represented a 70.8 per cent increase in net revenue versus the actual growth achieved of 7.3 per cent.

Decline in Twitter R & D returns

As the graph below shows, 2015 and 2014 were considerably better by this metric.

Twitter achieved $0.66 of net revenue increase per $ in R&D spend in 2015 and $0.81 in 2014.

These figures were similar to those achieved by Alphabet (Google), though still only half (2014) and two thirds (2015) of Facebook’s.

These numbers show that matching Facebook’s 2016 performance of $1.48 revenue/$ R&D was probably too much of a tall order.

However even if Twitter’s 2016 performance had only been equal to its own 2015 figure the company would still have seen a rise in net revenue of $470m ($0.66 x $713m), $361m more than was actually achieved. This would have meant a growth rate of 31.6 per cent.

You can imagine the significant impact on the company’s share price that a stronger growth story like this would have.

So R&D is still being heavily funded, but revenue growth is fast disappearing. This begs the questions, where, in who and in what are these funds being invested?

Share based compensation

Another factor here is Twitter’s R & D staff are the single biggest recipients of share based compensation (SBC). Anyone unfamiliar with share based compensation please skip to the end of the post for a brief explanation of how it works before reading further.

In the last three years Twitter’s total share based compensation expense has totalled $1,929m and has exceeded the total adjusted EBITDA generated by the company ($1,610m).

Effectively all the underlying profit generated (and more) since its IPO has been invested in remunerating employees and officers.

Research and development SBC has totalled $1,099m over the last three years. This represents half of the $2.2bn invested in R&D and shows how crucial share based remuneration is to Twitter’s strategy for attracting and retaining product and engineering talent.

The problem is that this represents a transfer of value from shareholders to R&D staff of over $1bn since the start of 2014. Meanwhile any innovation and improvements made are having a declining impact on revenue growth, resulting in a reduction in Twitter’s value of $25bn over the period.

Not what you’d call a great deal for shareholders.

What does this mean?

I can see two key factors at work here:

1. Competition for talent – Twitter needs to attract the same quality of people who could work at Facebook, Google et al. It therefore needs to offer competitive remuneration packages. Share based compensation has apparently formed a key part of this to date.

2. Creative block – lack of successful new ideas, innovation and improvements to drive revenue growth.

Implying the following question:

Should Twitter be getting more out of its R&D resources or are even these talented individuals unable to add significant value to the platform?

If the former, then this is a management issue which needs addressing and fast.

If the latter then the company should be looking outside of its own teams for ideas and innovation and allocating funds accordingly.

Either way, if it continues to invest huge sums in R&D without significant improvement in net revenues, then the company’s declining value appears unlikely to turnaround.

Share based compensation expense – an explanation

In a company’s accounts share based compensation (SBC) expense represents value given to employees of the company in the form of shares or options.

It’s calculated in various ways, but in simple terms it’s equal to:

SBC expense = Market value (of shares/options provided) – Amount paid by employee

Share based compensation generally forms part of an employee’s remuneration package to attract new joiners, motivate them to create value and/or improve retention.

SBC is a non cash expense, however because it relates to the creation of new shares – either immediately or potentially in the future when options become exercisable – its effect is to dilute existing shareholders. Again in simple terms this dilution is equal to the value of the expense.

Shareholders therefore expect to see a return on this investment greater than the value that they have been diluted by.

Example:

Note this is a highly simplified example, but it should help to get the gist.

Today ABC Company has a market capitalisation (the value of its equity to its shareholders) of $1bn.

It gifts shares to a group of highly sort after new employees who it believes will be instrumental in creating a new product.

The SBC expense of these shares is $10m at the point they are gifted i.e. 1% of its equity value.

The new employees are prohibited from selling the shares for a period of one year.

All things being equal the existing shareholders now only own 99% or $990m of the company.

A year later the company is valued at $1.2bn, with the $200m increase in value being wholly attributed to the success of the new product created by the team.

The shareholders 99% is therefore worth $1.188bn representing a gain of $188m.

The new joiners can now sell their shares worth $12m ($10m value when they were granted plus 1% of the increase of $200m in the overall value of the company).

Everyone’s a winner. The company didn’t have to find any cash. The new employees have banked $12m (less associated taxes) and shareholders have seen the value of their shares rise by nearly 19%.

Note: I do not own any shares in Twitter, Facebook or Alphabet. All analysis is based on publicly available information from Annual Reports, SEC filings and Proxy Statements.

Lissted’s Tweetsdistilled experiment is coming to an end. The Twitter accounts created have been popular with all types of users.

They provide a blueprint for how Twitter could use such feeds to: increase ad impressions, encourage engagement, improve sign up conversion, potentially pop some filter bubbles and enhance its return from investments like Moments.

In its early days Twitter used to talk about being “the pulse of the planet”. Years later and Twitter’s still struggling to help users easily find its best content. And time is running out.

That’s the new main feed. Call it ‘Pulse’ or some such. The current main Twitter feed becomes ‘Live’ – for hardcore users/digging deeper.

— M.G. Siegler (@mgsiegler) 26 January 2017

These tweets from M G Siegler of Google Ventures a couple of weeks ago, and his related post, had me both cheering and beating my head against the wall in equal measure.

I could not agree more. I’ve been a broken record on the subject for a while now. And it’s not been based on just words.

Tweetsdistilled – finding Twitter’s pulse

Lissted’s Tweetsdistilled application was created in 2014. The objective? To design a system to identify the best of Twitter:

breaking news

We got kicked out of a @Delta airplane because I spoke Arabic to my mom on the phone and with my friend slim… WTFFFFFFFF please spread pic.twitter.com/P5dQCE0qos — Adam Saleh (@omgAdamSaleh) 21 December 2016

influential commentary

The worst defeat in our history. England beaten by a country with more volcanoes than professional footballers. Well played Iceland.

— Gary Lineker (@GaryLineker) 27 June 2016

comedy

merry Christmas here’s my cat getting busted for taking a cinnamon roll 2 seconds after we told him no pic.twitter.com/f0AAAx65O5 — cat mom (@_sophocles_) 25 December 2016

and the downright bizarre.

My uncle went to Japan and a man liked him so much he made him out of sushi pic.twitter.com/kAvDaR9icP — later (@odysseypeachran) 14 January 2017

In very simple terms Tweetsdistilled is sort of what you’d get if you crossed Twitter lists with the Highlights and While You Were Away features.

Except:

– you don’t have to find or create the Twitter lists (Lissted’s algorithms do that),

– we aren’t limited by list sizes (some feeds are distilling 10,000s of accounts in real time),

– and we reckon our tweet selection recipe is way better!

And unlike something like Nuzzel (which I think is great by the way), Tweetsdistilled is 100% focussed on identifying individual tweets, not external content that’s being shared.

Tweetsdistilled: a short history

In order to test the effectiveness of the app’s recipe, in September 2014 we created a Twitter account – @Tweetsdistilled – and a bot that auto retweeted the items identified by the system. We also tried creating a feed relating to a specific community – @PoliticsUKTD.

The initial results told us we were on the right track.

I leave you tonight with Twitter’s newest best kept secret: @tweetsdistilled by @lissted. And @PoliticsUKTD, a UK politics sister account. — Margarita Noriega (@margarita) 19 September 2014

Later on we split out US community tweets into their own feed – @USTweetsDistill – and made the @Tweetsdistilled feed based solely on UK Twitter community reaction.

Over the subsequent 2+ years we’ve experimented with a number of other feeds relating to different scenarios such as specific communities, geographies and topics.

These have included: @PoliticsUSTD, @HealthUKTD, @EducationUKTD, @AppleTwD, @WalesUKTD, @NUFCTD, @LogisticsTD, @EuropeTD and @PRUKDistilled. Last week we even added a Donald Trump specific feed – @TrumpDistilled!

The main feeds are broad in their focus and generally feature tweets from journalists, media outlets, celebrities, organisations, bloggers and other influencers. With a sample of good quality viral tweets from your average user thrown in!

Feeds focussed on a specific community will be a mixture of tweets that are about that area of interest and ones that could be about any topic, but which appear to be of greater interest to the group.

The end of the line for Tweetsdistilled

But all good things must come to end and as of 14^th February the Tweetsdistilled accounts will be mothballed. Basically the cost/benefit equation of running the feeds isn’t justified anymore.

There are a number of reasons for this:

Objective achieved

The experiment achieved its objective a while ago and we now have a great system for identifying what matters on Twitter. (Any social listening/MarTech companies out there interested in such a system drop me a line!  adam@lissted.com)

Data cost

We have to purchase a significant amount of data from Twitter to power the feeds. Ironically having refined it and published the insights publicly via the feeds the biggest group of followers is Twitter staff!

Limited organic follower growth

This is due to a combination of the nature of the accounts themselves i.e. they only retweet others and don’t have tweets of their own, and a lack of word of mouth – many followers are journalists who appear to want to keep the accounts as “secret” weapons!

But the biggest reason is:

Why isn’t something like this not part of Twitter anyway?

As M G Siegler points out

“The first tab on Twitter, the one currently labelled ‘Home’ should be replaced by a tab made up of the ‘While You Were Away’ / ‘Highlights’ content. But on steroids. Thousands of tweets. The “best of” Twitter.”

I’d go further and say you don’t even need two tabs. Using accounts like these you can have everything all in one feed. Simples.

Tweetsdistilled has been a proof of concept of the value of Twitter having a feature like this.

The accounts have been popular with every type of user: influential, power, occasional and new. Here are some examples of reactions to our announcement re closing the feeds down:

@tweetsdistilled very sorry to hear that. It’s been an a bracing blast into my filter bubble. Probably RT’d twice as often since following. — Nigel Sarbutts (@NigelSarbutts) 7 January 2017

@USTweetsDistill well i really appreciated what you did. thank you. — Helen Kennedy (@HelenKennedy) 7 January 2017

Tragic. https://t.co/n0rTKFoWay — Dorrine Mendoza (@dorrine) 11 January 2017

Some of the most disappointed are Twitter staff!

@tweetsdistilled ???? — Jérôme Tomasini ???? (@JeromeTomasini) 6 January 2017

@USTweetsDistill ???????????? please don’t! — Bridget Coyne ???????? (@bcoyne) 5 January 2017

Business case for “pulse” accounts like these

They inject the best of Twitter directly into your feed.

The approach would have a number of powerful benefits:

Advertising

Most important of all in a commercial context followers are still looking at a Twitter feed and so can be served ads in the usual way. This is not the case with the Trending Now and Moments elements of Explore. Also ads could be targeted effectively when a user was following a niche community or topic feed e.g. @EducationUKTD, @AppleTwD or @NUFCTD.

More out of Moments

There’s real skill in crafting a good Moment and they can be a great way of discovering new content. But they rarely appear in your feed organically. Mixing relevant Moments into particular TweetsDistilled type feeds would potentially increase engagement and improve ROI on their creation.

Efficient listening

A follower of one of these Tweetsdistilled feeds is effectively getting served some of the best tweets from a group without having to follow the thousands of accounts that are being distilled.

They also don’t have to switch into a different part of the app or use Tweetdeck like they would do to use a Twitter list. And finally they can still follow specific accounts as well if they want to see all of an individual’s tweets.

Ideal for a new user

No requirement for a new user to follow lots of accounts, they can follow one stream based on an interest area and then start to follow individual accounts over time if they want.

Optional for existing users

Instead of forcing an algorithmic approach onto someone’s feed, where they potentially miss content they wanted to see, these accounts simply augment the user’s native feed.

Filter bubble popper

It’s been well established that most users of social media only follow accounts that are similar in views to themselves. A system such as Tweetsdistilled allows you to see some of the most important and interesting tweets from a particular community without having to follow the specific members. This lack of direct relationship could encourage users to broaden their horizons.

So overall the accounts should:

– increase advertising impressions;

– get more out of existing investments like Moments;

– encourage engagement;

– improve sign up conversions; and

– potentially broaden the content users see.

Thanks and goodbye

Surely this is all a no brainer. If we can build Tweetsdistilled with our constrained resources then surely Twitter could? We’ve probably got the technological equivalent of duct tape and cardboard toilet rolls at our disposal compared to them!

No one will be more sad to see the feeds switched off than me. Leaving aside any vested interest, I’ll miss the fantastic content they gave me and their ability to keep me up to date with everything that was happening.

I’ll be following up this post with a few based on the data we’ve analysed.

In the meantime thanks to everyone who followed the feeds. We’ve really enjoyed helping you keep your finger on “the pulse”.

This system could assist Facebook in identifying articles that have a high risk of being fake. The methodology is based on a journalistic verification model.

Examples: the model would have rated as high risk:

-FINAL ELECTION 2016 NUMBERS: TRUMP WON BOTH POPULAR ( 62.9 M -62.2 M ) – about the election results. It was ranking top of Google for a search for “final election results” earlier this week and has had over 400,000 interactions on Facebook. It was identified as fake (obviously) by Buzzfeed.

–  ‘Pope Francis Shocks World, Endorses Donald Trump for President, Releases Statement‘. Shared  nearly 1 million times on Facebook. Now taken down, having been reported as fake by The New York Times

The rating system described below is subject to patent pending UK 1619460.7.

At the weekend Mark Zuckerberg described as “pretty crazy” the idea that sharing fake news on Facebook contributed to Donald Trump being elected President.

He went on to say in a Facebook post:

“Of all the content on Facebook, more than 99% of what people see is authentic. Only a very small amount is fake news and hoaxes. The hoaxes that do exist are not limited to one partisan view, or even to politics. Overall, this makes it extremely unlikely hoaxes changed the outcome of this election in one direction or the other.”

“That said, we don’t want any hoaxes on Facebook. Our goal is to show people the content they will find most meaningful, and people want accurate news. We have already launched work enabling our community to flag hoaxes and fake news, and there is more we can do here. We have made progress, and we will continue to work on this to improve further.”

Yesterday, Business Insider reported a group of students had hacked together a tool that might help.

I think part of the answer lies in another social network, Twitter.

An important aside

It’s important to note the topic of “fake” news is not black and white. For example, parody accounts and sites like The Onion are “fake news” that many people enjoy for the entertainment they provide.

There’s also the question of news that is biased, or only partially based in fact.

The idea proposed below is simply a model to identify content that is:

1. more likely to be fake; and

2. is generating a level of interaction on Facebook that increases the likelihood of it being influential.

Verification and subsequent action would be for a human editorial approach to decide.

Using Twitter data to identify potentially fake news

In its piece on Zuckerberg’s comments, The New York Times highlighted this article ‘Pope Francis Shocks World, Endorses Donald Trump for President, Releases Statement‘ (now removed) that had been shared nearly a million times on Facebook. It’s fake. This never happened.

If it had been true it would obviously have been a big story.

As such you’d expect influential Trump supporters, Republicans and other key right wing media, organisations and individuals to have been falling over themselves to highlight it.

They weren’t.

Lissted tracks the Twitter accounts of over 150,000 of the most influential people and organisations. This includes over 8,000 key influencers in relevant communities such as Republicans and US Politics, as well as potentially sympathetic ones such as UKIP and Vote Leave.

Of these 150,000+ accounts only 6 shared the article.

Extending the analysis

Lissted has indexed another 106 links from the same domain during the last 100 days.

The graph below shows analysis of these links based on how many unique influencer shares they received.

You can see that 74 of the 107 links (including the Pope story) were only shared by a single member of the 150,000 influencers we track. Only 5 have been shared by 6 or more and that includes the Pope story.

That’s just 196 influencer shares in total across the 107 links.

Yet, between them these URLs have been interacted with 12.1 million times on Facebook.

And of course these are the stories that have been shared by an influencer. There could be more that haven’t been shared at all by influential Twitter users.

Lissted’s data also tells us:

– 133 of the 150,000 influencers (less than 0.1%) have shared at least one of its articles; and

– the article published by the site that has proved most popular with influencers has received 10 shares.

How could this help identify high risk news?

You can’t identify fake news based simply on levels of reaction, nor based on analysing what they say. You need a journalistic filter. Twitter provides a potential basis for this because its data will tell you WHO shared something.

For example, Storyful, the Irish social media and content licensing agency, has used Twitter validation by specific sources as a way of identifying content that is more likely to be genuine.

I don’t know why very few of the influencers Lissted has been tracking shared the piece. But my suspicion would be that as influential members of their communities they’re:

– capable of spotting most fake news for what it is, and/or

– generally less likely to share it as even when it serves their purpose they know that they could be called out for it (they’re more visible and they’ve got more to lose); and /or

– less likely to be exposed to it in the first place.

Obviously, not all content will be shared on Twitter by these 150,000 accounts. But you can bet your bottom dollar that any vaguely significant news story will be. The temptation to want to highlight a genuine story is just too great.

Comparison to example of genuine content

To give the Pope story numbers some context, the table below shows a comparison to this piece on the Donald Trump website – Volunteer to be a Trump Election Observer (NB: post victory the URL now redirects to the home page).

Both URLs have similar Facebook engagement, but there’s a huge difference in the influencer metrics for the article and the domain.

This is just one example though. If we build a model based on this validation methodology does it provide a sound basis for rating content in general?

NB: the model that follows focuses on content from websites. A similar, approach could be applied to other content e.g. Facebook posts, YouTube videos etc.

Proof of concept

To test the methodology I built a rating model and applied it to three sets of data:

1. The 107 links identified from endingthefed.com – data here.

2. Links that Newswhip reported as having 250,000+ Facebook interactions in the period 15/9/16 – 14/11/16 – data here.

3. A random sample of over 3,000 links that were shared by influencers from the specific communities above in the period 15/10/16 -14/11/16 – data here.

The rating model gives links a score from 0 – 100. With 100 representing a links that has a very high risk of being fake and zero being a very low risk.

To rate as 100 a link would need to have:

– received 1,000,000 Facebook interactions; and
– be on a site that has never been shared by one of the 150,000 influencers, including the link itself.

The distribution of rating for the random sample is as follows:

Mark Zuckerberg’s commented that less than 1 per cent of content on Facebook is fake. If we look at the distribution we find that 1 per cent corresponds to a score of 30+.

The distribution also shows that no link in the sample scored more than 70.

Finally over 90 per cent of URLs rated at less than 10.

On this basis I’ve grouped links in the three data sets above into 4 risk bands:

Exceptional – 70+
High – 30 -70
Medium – 10 – 30
Low – 0-10

Applying these bands to the three sets gives:

Unsurprisingly a high proportion of the 250,000+ group are rated as Medium to Exceptional risk. This reflects the fact that there are so few of them – 182 – and the implicit risk of being influential due to their high engagement.

Verifying these would not be a huge drain on resources as that translates to just 2 or 3 links per day!

The graph also shows how high risk the endingthefed site is with over 95 per cent of its content rated as High or Medium.

HEALTH WARNINGS

1. Being ranked as medium – exceptional risk does NOT mean the content is fake. It is simply an indicator. Just because one article on a site is fake does not mean that all the risky content is.

Also an article could be genuine viral content that’s come out of the blue from a new source.

The value in the model is its ability to identify the content that needs verifying the most. Such verification should then be done by professional journalists.

2. The rankings only reflect the 150,000 individuals and organisations that Lissted currently tracks. There could be communities that aren’t sufficiently represented within this population.

This isn’t a flaw in the methodology however, just the implementation. It could be addressed by expanding the tracking data set.

Example findings

The top 10 ranked articles in the 250,000+ group are as follows:

1. Mike Pence: ‘If We Humble Ourselves And Pray, God Will Heal Our Land’ (506k Facebook interactions, 0 influencer shares)

2. Just Before the Election Thousands Take Over Times Square With the Power of God (416k Facebook interactions, 0 influencer shares)

3. TRUMP BREAKS RECORD in Pennsylvania “MASSIVE CROWD FOR TRUMP! (VIDEO) – National Insider Politics (207k Facebook interactions, 0 influencer shares)

4. SUSAN SARANDON: CLINTON IS THE DANGER, NOT TRUMP – National Insider Politics (273k Facebook interactions, 0 influencer shares)

5. FINGERS CROSSED: These 11 Celebrities Promised To Leave America If Trump Wins (455k Facebook interactions, 1 influencer share)

6. Trump: No Salary For Me As President USA Newsflash (539k Facebook interactions, 0 influencer shares)

7. I am. (454k Facebook interactions, 1 influencer share)

8. A Secret Has Been Uncovered: Cancer Is Not A Disease But Business! – NewsRescue.com (336k Facebook interactions, 0 influencer shares)

9. The BIGGEST Star Comes Out for TRUMP!! Matthew McConaughey VOTES Trump! (294k Facebook interactions, 1 influencer share)

10. Chicago Cubs Ben Zobrist Shares Christian Faith: We All Need Christ (548k Facebook interactions, 1 influencer share)

My own basic verification suggests some of these stories are true. For instance Donald Trump did indeed say that he would not draw his Presidential salary.

However the Matthew McConaughey story is false and by the article’s own admission the Pennslyvania rally image is from April not October, plus there are no details on what “records” have been broken.

From outside the top 10 this post, rated as high risk, FINAL ELECTION 2016 NUMBERS: TRUMP WON BOTH POPULAR ( 62.9 M -62.2 M ) about the election results was ranking top of Google for a search for “final election results” earlier this week. It was identified as fake by Buzzfeed.

It would be great if any journalists reading this would go through the full list of articles rated as high risk and see if they can identify any more.

Equally if anyone spots URLs rated as low risk that are fake please let me know.

Further development

This exercise, and the mathematical model behind it, were just a rudimentary proof of concept for the methodology. An actual system could:

– utilise machine learning to improve its hit rate;

– flag sites over time which had the highest inherent risk of fake content;

– include other metrics such as domain/page authority from a source such as Moz.

Challenge to Facebook

A system like this wouldn’t be difficult to setup. If someone (Newswhip, BuzzSumo etc) is willing to provide us with a feed of articles getting high shares on Facebook, we could do this analysis right now and flag the high risk articles publicly.

Snopes already does good work identifying fake stories. I wonder if they’re using algorithms such as this to help? If not then perhaps they could.

Either way, this is something Zuckerberg and Dorsey could probably setup in days, hours perhaps!

Happy & gloRIOus and most definitely #victoRIOus. Welcome home @TeamGB! #greattobeBAck #GBR https://t.co/iy6anYBtLe pic.twitter.com/VBLqxd1pwd

— British Airways (@British_Airways) August 23, 2016

So, the Olympics are over for another four years. Having gorged myself on the heroics of TeamGB, I’m personally suffering from withdrawl.

TeamGB’s social media team also did a sterling job over the two weeks, sharing content about our athletes’ magnificent performances.

Their two primary platforms based on fans and followers were Facebook and Twitter. Their Twitter account has an impressive 822,000 followers.

But there’s another relevant Twitter account with an even larger audience, and it’s dormant.

@London2012.

This account has 1.32 million followers. It’s tweeted seven times since the end of the Paralympics in 2012, the last in July 2013.

Since then, nothing.

Will this account and its audience of 1.3 million potentially sport mad followers just sit and fester forever?

And it isn’t just the number of followers that’s impressive, it’s the quality too.

Here are some examples of significant followers of @London2012 who don’t follow @TeamGB:

@coldplay, @WayneRooney, @idriselba, @astonmartin, @thetimes, @Harrods, @EvanHD, @cabinetofficeuk, @WomensRunning, @KathViner and @andyburnhammp.

The identity shouldn’t get in the way of using it. Behind every account is a unique TwitterId (it’s 19900778 in @London2012’s case if you’re interested). This means you can change your @username and still maintain your follower and following relationships. Here are the Twitter instructions to do this.

I don’t know who “owns” this asset, but surely whoever it is could think of a change of identity that would still be relevant to the majority of its followers. Perhaps it could have been used to support the Games’ legacy? @UK_Sport’s 91,400 followers rather pales in comparison.

And @London2012 isn’t the only account like this.

What are the BBC going to do with accounts relating to shows that are no more, like @BBCTheVoiceUK and its 521,000 followers, or the @ChrisMoylesShow with 518,000?

Nothing by the looks of it.

On a sombre note, there are accounts that become dormant because someone dies. Examples like @ebertchicago and @davidbowiereal demonstrate that even then there can be circumstances where it’s appropriate for the accounts to live on.

As of writing Lissted‘s data shows 28,401 accounts with 10,000+ followers who haven’t tweeted in the last 90 days.

Not all of these accounts will be dormant. Some like Ed Sheeran may be just “buggering off for a bit“. But many will.

Between them they have a combined untapped group of 1.5 trillion followers.

Now there’s a number worthy of a gold medal!

Last week venture capitalist Bill Gurley published a post called On the Road to Recap.

Would recommend that all of those in the Unicorn ecosystem, or those considering it, read this. Times are changing: https://t.co/VRKOCTB1UZ

— Bill Gurley (@bgurley) April 21, 2016

For anyone who doesn’t know, a Unicorn in this context is a startup company with a valuation in excess of $1bn.

The post analysed in depth the current investment situation in relation to Unicorns and concluded:

“The reason we are all in this mess is because of the excessive amounts of capital that have poured into the VC-backed startup market. This glut of capital has led to (1) record high burn rates, likely 5-10x those of the 1999 timeframe, (2) most companies operating far, far away from profitability, (3) excessively intense competition driven by access to said capital, (4) delayed or non-existent liquidity for employees and investors, and (5) the aforementioned solicitous fundraising practices. More money will not solve any of these problems — it will only contribute to them. The healthiest thing that could possibly happen is a dramatic increase in the real cost of capital and a return to an appreciation for sound business execution.”

The post lit a fire in the VC and startup communities.

In fact Lissted ranks the post as the most significant piece of content on any investment related topic in the VC community in the last two months. 

So I thought I’d see how it compares to other recent posts about Unicorns.

Comparison with other “Unicorn” content

I searched across the last month for posts with the most shares on LinkedIn (URLs listed at the end). If you search across all platforms you end up with very different types of unicorn!

Having found the Top 10 articles on this basis, I then looked at the number of distinct members of Lissted‘s VC community on Twitter who shared each of the articles. The community tracks the tweets of over 1,500 of the most influential people and organisations in relation to venture capital and angel investment.

Finally for completeness I also looked at the number of distinct Lissted influencers from any community who tweeted a link to the piece.

In the graph the engagement numbers have been rebased for comparison, with the top ranking article for each measure being set to 100.

The difference in reaction by the VC community and influential individuals in general is considerable.

15x more influential members of the VC community (169) shared ‘On the Road to Recap’ than the next highest article (11 -Topless dancers, champagne, and David Bowie: Inside the crash of London’s $2.7 billion unicorn Powa).

9x more influencers across all Lissted communities (419) shared the post (46 for the Powa piece).

VC Community reaction examples

Influential retweeters of Bill’s initial tweet above included Chris Sacca, Om Malik & Jessica Verrill.

Examples of key community influencers who tweeted their own views were:  

there is so much truth being told in this post. it is gold. https://t.co/HSQxAzddJd — Fred Wilson (@fredwilson) April 21, 2016

quotable @bgurley: “Being private does not mean you get a free pass on scrutiny.” On the Road to Recap: https://t.co/S4gElFmP08 @500startups

— Dave McClure (@davemcclure) April 21, 2016

Best recap of current private investment climate and unicorn ecosystem I’ve read to date. Must read https://t.co/vWucGRs9Ur via @bgurley — Jeff Weiner (@jeffweiner) April 21, 2016

And people are still sharing it days later:  

In case you haven’t read this post from @bgurley yet – lots of great insights. On the Road to Recap – https://t.co/0WkAETpxeI — Christoph Janz (@chrija) April 26, 2016

Mythical measurement

So, the next time you set out to find influential content, don’t get too carried away with big engagement numbers. Focus on understanding where and who that engagement came from.

That way your conclusions will be legendary, not mythical.

If you’d like to get a daily digest of the influential content in the Venture Capital community, sign up for a free Lissted account here, then visit the Venture Capital page.

Articles

1. Forget unicorns — Investors are looking for ‘cockroach’ startups now

2. What investors are really thinking when a unicorn startup implodes

3. On the Road to Recap: | Above the Crowd

4. Next Chapter: Cvent Acquired for $1.65 Billion

5. The fall of the unicorns brings a new dawn for water bears

6. Why Unicorns are struggling

7. Oracle just bought a 20-person company for $50 million

8. Silicon Valley startups are terrified by a new idea: profits

9. Topless dancers, champagne, and David Bowie: Inside the crash of London’s $2.7 billion unicorn Powa

10. 10 Startups That Could Beat a Possible Bubble Burst

Here are the three areas I think are crucial to address.

Note none of them relate to tweets or ads. True, changes to video, ability to edit tweets, tweet length, ad options etc. might improve things in the short term. But I’m convinced in the medium/long term they’re like moving the deckchairs on the Titanic.

Effective policing

Twitter’s public nature (protected accounts aside) is a major reason why it appeals to a minority of people. Those who accept, or are naïve about, the risk involved with such a platform.

Friday night saw an example of such naivety from a Twitter employee of all people in response to the #RIPTwitter hashtag:

Twitter engineer surprised at how Twitter works pic.twitter.com/DXYhiVp13H

— Ned Donovan (@Ned_Donovan) February 6, 2016

His experience was pretty mild though.

Frequent stories about people attacked by trolls, spammers and bullies can’t be helping user growth. Some investment has been made to address this, but it must be maintained.

Freedom of speech and expression is something to be valued. But just like society won’t tolerate all behaviour, nor should Twitter.

Update: While I’ve been drafting this post today, Twitter has announced the creation of a Trust and Safety Council.

Follow spam

Hands up who’s been followed multiple times by the same account? Here’s a screenshot of an account that followed our @Tweetsdistilled account ten times last month.

Each time it’s unfollowed and tried again because @Tweetsdistilled didn’t follow it back. Such automated follow spam is a joke. If these are the kind of users Twitter thinks it needs to be serving then it really doesn’t have a future.

At the moment anyone can follow up to 5,000 accounts. You are then limited to following only 10 per cent more accounts than the number that follow you. So to follow more than 5,000 accounts you currently need 4,545 followers.

I’d suggest changing this ratio to substantially less than 1.0x after 5,000 accounts. For example, if set at 0.25x then if you wanted to follow 6,000 (1,000 more) you would need to have 8,545 followers (4,000 more).

I’d also place stricter limits on the number of times you can follow the same account than appears to be the case at the moment. Twice in any 30 day period would be enough to allow for an accidental unfollow!

Combined, these changes would still allow people to grow their followers, but would mean they could only do so if they were interesting to an increasingly large group of users.

Why do I know these constraints shouldn’t be an issue?

Because of 2.57 million accounts that Lissted has identified as having any real influence potential on Twitter, 95 per cent of them (2.44 million) follow less than 5,000 accounts. Of the remaining 124,000 accounts, 24,000 would still be within the parameters I’ve suggested.

Here’s a table summarising the stats:

You can see the remaining 100,000 accounts have more follow relationships (2.619bn) than the other 2.47 million combined (2.449bn).

And these are just the accounts that Lissted has identified as having some degree of likelihood they are “genuine”. There are probably more that are pure spam that Lissted filters out.

So this tiny minority, less than 0.1 per cent of Twitter users is creating this huge amount of irrelevance.

Communities

A key strength of Twitter is the groups of experts you can find related to pretty much every industry, profession and topic you can think of.

In my opinion Twitter focuses too much on promoting “celebrities” and not enough on these niche communities.

Twitter needs to provide new and existing users with simple and effective ways to “plug into” them.

Inside Twitter

This could be done within the existing feed mechanism. Over the last 12 months our niche Tweetsdistilled accounts e.g. @PoliticsUKTD, @HealthUKTD and @EducationUKTD have been demonstrating this. They’re like a cross between Twitter lists and ‘While you were away’. Having chosen to subscribe to the feed it then posts interesting tweets from the community into your timeline and like Twitter lists you don’t need to be following the specific accounts concerned.

They appear to be doing something right, as they’re followed by many key members of these communities. Even accounts you might assume would have this covered anyway.

Outside Twitter

I’d love to know the engagement stats for the Popular in your Network emails. Does anyone actually look at them? For new users they seem to focus heavily on celebrity tweets. My suspicion is if you wanted to sign up for Stephen Fry’s or Kanye’s tweets you’d have done it by now.

Instead, why not allow users to subscribe to a summary of what communities have been talking about. The content they’ve shared and the tweets they’ve reacted to.

Lissted can now deliver daily and weekly digests of the most interesting content and tweets from an array of communities. Here’s Sunday’s US Business community weekly digest for example.

To produce these digests Lissted actually combines the response of a Twitter community with the wider social reaction across Facebook, LinkedIn and Google+. But it still demonstrates Twitter has the ability to be seen as a powerful intelligence tool for new and existing users with minimum investment on their part.

If you have 7 minutes to spare here’s a detailed story we produced last October about how this could also help Twitter in an onboarding context too.

Over to you Jack

Twitter’s next quarterly results announcement is tomorrow (10th February). I wonder if any of these areas will be addressed….

But recently I’ve noticed a move to start making absolute statements based on such analysis. I highlighted one such area earlier this year in relation to the UK general election. Some people even suggested Twitter could predict the outcome. They were wrong.

The thing is, as much as social data can be powerful and seem vast in scope, you still need to keep a sense of perspective.

It’s been estimated that every day people speak an average of around 16,000 words. With this in mind I thought I’d try and make a quick estimate of the proportion of people’s conversation in North America and the UK that social media monitoring data represents.

Answer? 0.16 per cent* 

And that’s before we get into issues like spam accounts, bias towards power users’ output, questions about whether tweets and posts are truly an authentic reflection of what people think and feel, demographic bias and the online disenfranchised.

I based my estimate on Twitter and Facebook, as they represent the majority of conversation that such tools access. We could add Reddit, blog posts, comments on online articles and YouTube videos, forums etc, and if anyone fancies doing so, be my guest! But I don’t expect you’ll get to a much bigger number.

Particularly as on the other side of the equation we could add to what people say other forms of conversation that aren’t accessible to social listening: emails, messaging apps and collaboration tools like Slack to name a few.

So does this make social listening as an insight tool a waste of time?

No, of course not. I’ve spent enough time buried deep in social data to know that it can provide hugely valuable insights. But to achieve this you need to be extremely focussed.

Ask good questions

Structure questions that take into account the limitations of the data. “Who does Twitter conversation suggest is going to win the UK general election?” does not fall into this category. Also ensure the answer doesn’t lead to a “so what” moment, but provides a genuine basis to take more action.

Say no to pretty noise

Pretty dashboards that pluck results out of the ether aren’t the answer. Make sure you understand exactly who you’re listening to – who is behind the data.You need this audience perspective to be confident what you’re seeing is real insight and to address what I call the four (f)laws of social listening.

Be sceptical

Sometimes social media analysis gives you an answer you didn’t expect, one that differs from your existing world view. It’s crucial you don’t dismiss such answers as they could be the most valuable insights you’ll ever get. Equally, don’t naively just accept them at face value. Challenge. Try and triangulate the answer from another source. Try asking the question in a different way and compare the answers. Sometimes you can be surprised.

* You can see my back of an envelope calc here. The estimated variables are editable in the “Try your own” sheet (highlighted in blue) so you can have a play to work out your own figures. In simple terms we’re comparing:

Talking: c. 422 million people across US, Canada and UK using 16,000 words per day = 6.75 trillion words.
Twitter: c. 137 million tweets (N. American and UK users assumed at 27.5 per cent of active users multiplied by 500 million tweets per day) assumed to contain an average of 25 words = 3.4 billion words
Facebook: c. 707 million Facebook posts per day (N. American and UK users assumed at 16.4 per cent of users multiplied by 4,320 million posts per day) assumed to contain an average of 50 words = 35 billion words. Only 20 per cent of these posts assumed to be accessible by social listening tools. I have no specific basis for the level of this last assumption, though clearly it is the case that social listening tools can’t access all Facebook data – though Datasift’s PYLON offering provides a potential solution to this privacy issue. However even if you assume all posts accessible the result only increases to 0.57 per cent.

Turnover is vanity, profit is sanity, but cash is reality*.

* another version replaces reality with “king”

The implications are pretty obvious. No matter how much turnover (or revenue if you prefer) you generate, if it doesn’t turn into profit you’ll only survive if someone keeps pumping in cash.

If you generate profit, but you don’t convert that profit to hard cash, then you’ll end up in the same boat.

A similar issue applies to social listening, analytics and measurement in general.

Vanity metrics and pretty noise

You can’t move for the number of tools and platforms that will give you graphs and metrics of social media data. The frequency of mentions of this, how many likes of that, the number of followers of the other. All wrapped up in a beautifully designed dashboard.

The thing is this “analysis” is often nothing more than pretty noise.  And the danger is it can be worse than meaningless, it can be misleading.

Really insightful

To find real insight we need to know the who, what and why of the data behind the numbers, how this relates to what we’re seeking to discover and most importantly of all, we need to know the right questions to ask.

The UK General Election social media coverage was a great example of how not to do this. All the attention was on counting stuff and comparing who had more of this and less of that.

Far too few asked questions like: who was active in these online conversations, why were they participating, and were they likely to be representative of what you were trying to understand?

It’s the outcome that really counts

Finally “actionable insight” is a phrase we hear all the time. But even when it’s an accurate description, the key element is “able”.

If we don’t possess the skills, resources or confidence to take the action required, then the whole exercise was pointless. So don’t bother asking a question unless you’re able to follow through on the answer.

Because it all comes down to this – what is the outcome of your action in the real world?

After all, just ask Ed Miliband whether his Twitter metrics were much consolation when it came to the result of the election.

Hat tip to Andrew Smith who inspired this post with his comment to me that with Lissted we’re seeking to focus on “sanity, not vanity”.

It could primarily be due to the highly effective broadcasting and amplification activities of small groups of partisan individuals, combined with the subsequent reporting by the UK media of simplistic volume based analysis.

The 2015 UK General Election is being called the “social media election”. Twitter’s importance has been compared to The Sun newspaper’s claimed impact on the 1992 result. In fact, this comparison was also drawn in 2010.

With this in mind you can’t move for social listening platforms and the media, talking about Twitter data and what it represents: graphs of mentions of leaders and parties abound.

Some have even suggested Twitter data might be able to predict the result.

The problem is, the analysis I’ve seen to date is so simplistic it risks being seriously misleading.

Demographics

There are multiple reasons why you have to be very careful when using Twitter data to look at something as complex as the Election. I tweeted the other day that demographics is one of them.

Demographics is just one reason why Twitter data needs treating VERY carefully if using to “predict” #ukelection 2015 pic.twitter.com/fAmKlY2WZq

— Adam Parker (@AdParker) March 23, 2015

Twitter is skewed towards younger people who are only a minority of those who will vote – and a significant number, 13 per cent, can’t vote at all.

This is valuable insight when it comes to targeting 18-34 year old potential young voters and trying to engage them politically e.g. for voter registration.

But it also shows that in a listening, or reaction context, Twitter’s user base is wholly unrepresentative of the UK voting population.

And there’s a potentially bigger issue with taking Twitter data at face value – vested interests.

#Leadersdebate

One of the first major examples of social media analysis that received widespread coverage was in relation to the seven way #leadersdebate. Many analytics vendors analysed the volume of mentions of leaders or parties, to try and provide insight into who “won”. What they didn’t do was question the motivations of those who participated in the Twitter conversation.

GB Political Twitterati

To investigate this I used Lissted to build communities for each of the seven parties represented in the debate – Conservatives, Labour, Liberal Democrats, SNP, Greens, UKIP and Plaid Cymru.

These communities comprise obvious users such as MPs and party accounts, as well as accounts that Lissted would predict are most likely to have a strong affiliation with that party based on their Twitter relationships and interactions.

They also include media, journalists and other commentators whose prominence suggests they are likely to be key UK political influencers, and a handful of celebrities were in there too.

We’ll call this group of accounts the “Political Twitterati”. 

The group contained 31,725 unique accounts[1] that appeared in at least one of the seven communities. This number represents only 0.2 per cent of the UK’s active Twitter users[2].

I then analysed 1.27 million of the tweets between 8pm and 11pm on the night of the debate that used the #leadersdebate hashtag, or mentioned some terms relating to the debate. 

Within this data I looked for tweets either by the Political Twitterati, or retweets of them by others.

Findings about the Political Twitterati

- 25x more likely to get involved in the conversation [3]

So we know they were motivated.

- Accounted for 50 per cent of the conversation [4]

So they were highly influential over the conversation as a whole.

- Included 69 per cent of the top 1,000 participants [5]

So the vast majority of the key voices could have been predicted in advance.

Analysis by Political Affiliation

I then broke the Twitterati into four groups.

– Journalists, media, celebrities and other key commentators who generally appeared in multiple communities

– Directly related to a party e.g. MPs, MSPs, MEPs or accounts run by the parties themselves

– Accounts with a strong apparent affiliation to one party because they only appeared in one of the communities

– Other accounts with mixed affiliation

Here’s a summary of their respective activity:

We can see that one in four tweets were generated by only 803 journalists, media, celebrities or other commentators.

The top ten of which were these:

We can also see that one in five tweets were generated by accounts that had a direct[6] or apparent political affiliation[7].

If we break these down by party we get this analysis of politically affiliated reaction: 

The numbers demonstrate how Labour and the SNP are able to shift the Twitter needle significantly through just a small number of participants.

The SNP’s performance is particularly impressive with only 801 accounts generating almost 5 per cent of the whole conversation.

An example of tactics

So how do they do this? Well here are some examples of how the SNP community amplifies positive remarks made by (I think) non affiliated Twitter users.

The following are all tweets by users with less than 40 followers, who rarely get more than the odd retweet, but who in these cases got 50 or more out the blue.   Can you guess why? 

I wish I could vote for Nicola Sturgeon! #leadersdebate — Chloe (@chlojojojo) April 2, 2015

So, after all that, I want to vote SNP; unfortunately they’re not fielding any candidates in England. #leadersdebate

— Chris Goddard (@MagpieSupernova) April 2, 2015

I’m off to Scotland so I can vote SNP #leadersdebate

— Mole (@OoberMole) April 2, 2015

I’d like to vote SNP/Plaid but I live in England! #leadersdebate — Garry Lucas (@GarryLucas1) April 2, 2015

Kinda wish I was Scottish. Would totally vote for Sturgeon. #leadersdebate — Mizukian (@mizukian) April 2, 2015

I live 600 miles from Scotland but want to vote for @NicolaSturgeon damn she knocked them all dead #leadersdebate — Gaynor Riley (@rilgay) April 2, 2015

What you find when you look at the retweets in each case is that many are coming from accounts that would appear to have a SNP affiliation.

In fact look closer and you find that a number of the 779 affiliated accounts[7] appear.

Unsurprisingly, given the reputation of the SNP community for being very active and organised online, they were looking out for positive tweets about their party or their leader, and then amplifying them.

Conclusion

Simplistic analysis of Twitter data around a topic like the General Election has the potential to be at the least flawed and at worst genuinely misleading.

Not only are the demographics unrepresentative of the voting population, but the actions of small groups of motivated individuals are capable of shifting the needle significantly where simple volume measures are concerned.

The resulting distorted view is then reported at face value by the media, creating a perception in the wider public’s mind that these views are widely held.

Of the seven parties it would appear that what they learned during the Scottish Referendum is standing the SNP community in good stead when it comes to competing for this share of apparent Twitter voice.

So Twitter may indeed end up being “wot won it”, but potentially not because of general public reaction, engagement and debate, but because of highly effective broadcasting and amplification by a relatively small, but motivated group of individuals, and the naive social media analysis that is then reported by the media.

Notes:

1. Lissted can decide how many accounts to include in a community list based on a threshold of the strength of someone’s relationships with a community. The lower the threshold, the weaker the ties, and arguably the weaker the affiliation.

2. Based on 15 million UK active Twitter users.

3. 6,008 of the Political Twitterati accounts appeared at least once. That’s around one in five (6,008 out of 31,725).

119,645 unique users appeared in the data sample as a whole. Based on 15 million active UK Twitter users that’s around 1 in 125.

Suggesting this group of relevant accounts was 25 times more likely to have participated in the conversation than your average Twitter user.

Even if we take the figures based on Kantar’s wider sample above of 282,000 unique users the resulting ratio of 1 in 53 gives a figure of 10x more likely.

4. These 6,008 accounts tweeted 50,461 times. These tweets were then retweeted 585,964 times meaning they accounted for 636,425 of the tweets or 50.1%.

5. Looking at the top accounts that generated the most tweets and retweets in the data gives the following:

The top 1,000 accounts generated over half of the tweets (50.6%) either directly or through retweets. 692 of these accounts appear in our Twitterati list.

6. Direct accounts

These are accounts directly affiliated with a party e.g. MPs, MSPs, MEPs or accounts run by the parties themselves.

Breaking these down across their political affiliations we get the following:

So this handful of 271 clearly biased individual accounts, were ultimately responsible for 10 per cent of the total tweets.

How likely do we think it is that people retweeting these party affiliated accounts were undecided voters?

7. Apparent affiliated accounts

At the other end of the scale there are the accounts that only appear in one of the communities.This suggests that these individuals have a very strong affiliation to one party and will equally be partisan.

Within the 6,008 Twitterati accounts that participated were 4,274 that only appear in one of the seven communities (and weren’t included in the media/celebrity group).

Between them these 4,274 users again accounted for 10 per cent of the total conversation.

The Labour party group comes out top with 3.2 per cent of the total tweets, but it’s the SNP group of 779 accounts, contributing 3.0 per cent, or one in thirty three of all tweets, that massively punches its weight in this group.

It’s important to understand the full context relating to social media statistics before you act on them.

The Stat

I came across this stat the other day:

91 per cent of mentions [on social media] come from people with fewer than 500 followers.

The implication in the source blog post and whitepaper was:

When it comes to your social media strategy, don’t discount the importance of brand mentions by Twitter users with low follower counts.

It’s complicated

Follower numbers shouldn’t be the be all and end all when it comes to defining your social media strategy. Agreed.

For a start, where influence is concerned, relevance, proximity, context and other factors are crucial. And followers is a very simplistic metric and depending on how they use social platforms, may have little in common with a person’s real potential for influence.

Also, even if the mention itself doesn’t influence anyone, simply the knowledge that an individual has shown an interest in your brand in some way is potentially of value.

But while sympathising with the inference drawn, I think the statistic and its underlying data would benefit from some numerical context to better understand their implications.

N.B. I’ve focussed on Twitter in this analysis as that’s where the majority of the data in the particular research apparently came from.

Analysis

Given the stat focuses on accounts with less than 500 followers, let’s split Twitter into two groups:

– Low Follower Group – Less than 500 followers.
– High Follower Group – 500 or more followers.

And then let’s look at two relevant areas – Impressions and Retweets.

Impressions

Who could have seen brand mentions by each of these groups and potentially been influenced by them?

To calculate this we need to know the following for each group:

– Average number of followers.
– Impression rate.

Average followers

I used this estimated distribution of follower numbers across Twitter users*, combined with Lissted‘s data on nearly 2 million of the most influential accounts, to calculate a weighted average of the number of followers each group is likely to have.

Results:

– Low Follower Group – 100
– High Follower Group – 8,400

Impression rate

Every time you tweet only a proportion of your followers will actually see it. For many users this proportion could be less than ten per cent. The “impression rate” represents the total number of impressions generated by your tweet, divided by your follower number.

It only includes impressions on specific Twitter platforms – web, iOS app and Android app. This means impressions in applications like Hootsuite and Tweetdeck don’t count.

The rate is also complicated by retweets. The rate calculated by Twitter Analytics includes impressions that were actually seen by followers of the retweeting account, who may not follow you.

I’ve tried to look at retweets separately below, so for the purpose of this analysis I’m looking for impression rates without the benefit of retweet amplification.

On this basis I’ve assumed an impression rate of ten per cent for the Low Follower Group and five per cent for the High Follower Group. These assumptions are based on various articles estimating impression rates in the range of 2-10%. For the sake of prudence I’ve used a lower rate for High Follower accounts on the assumption that they could have a higher proportion of inactive and spam followers.

We can now calculate the proportion of total impressions related to each group as shown in this table:

Finding: only 19 per cent of impressions relate to the Low Follower Group.

Quite simply the difference in reach of the High Follower accounts (84x higher – 8,400 v 100) more than offsets the difference in volume of mentions by the Low Follower Group (only 10x higher – 910 v 90).

For the Low Follower Group to even represent 50 per cent of the total impressions we’d need to assume an impressions rate for this group that is over 8x higher than for the High Follower Group e.g. 42% v 5%.

Though I suspect there may be a difference, is it really likely to be that much?

Retweets

Next we need to consider if any of the brand mentions were retweets. If so were the original tweets more likely to be by accounts with high or low followers?

A lot of retweets by volume are by accounts with low followers. That’s just common sense because the vast majority of Twitter users have low follower numbers. But when we’re exposed to a retweet it’s the original tweet that we’re exposed to. This is the very reason why Twitter includes the resulting impressions in the Impression rate (I’m assuming automatic retweets, not manual ones).

To understand this better I analysed a sample of over six million tweets tracked by Lissted over the last two months that were retweeted at least once. The sample included tweets by 1.27 million different accounts and collectively these tweets received over 200 million retweets in total.

Of these six million tweets, 0.6% of them (c.39,000) accounted for two thirds of the total retweets generated.

And 99 per cent of these “top tweets” were by users with 500+ followers.

Finding: a high proportion of retweets are of users with High Followers, even if many are by users with Low Followers.

Conclusion

Mentions relating to accounts with higher than 500 followers appear more likely to:

– represent the majority of initial impressions; and
– generate the majority of any resulting retweets.

In other words it’s high follower accounts that are more likely to be the source of the majority of the brand mentions that people are exposed to on Twitter.

Caveat

As I said at the start the purpose of this analysis is simply to give some proper context to an isolated statistic. Assessing the impact and actions you should take due to mentions of your brand requires consideration of a lot more factors than simply numerical exposure.

It could be the case that high follower tweets make up the vast majority of the mentions people are exposed to, but factors like trust, context, proximity and relevance could lead to mentions by low followers having more influence on business outcomes.

The key is to properly understand who is talking about you and why, and not base decisions on sweeping statistics.

*N.B the follower distribution analysis is from Dec 2013, but as Twitter hasn’t grown a huge amount in the last year, it seems reasonable to assume its validity. Happy to share my detailed workings with anyone who’s interested.