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!

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….

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.

A survey by Cision has found that time spent using social media for work by the UK journalists who responded has fallen. The focus this finding has received is unfortunate as this reduction may simply be due to increased productivity. Meanwhile, for the first time the same survey found that a majority of UK journalists now think social media use for work is both necessary and beneficial.

Cision have produced their annual survey of how journalists are using social media. The top finding is a fall in the proportion of UK journalists using social media for work for four hours or more per day. The level has reduced from 24 per cent in 2012 to 13 per cent in 2014. The inference drawn is that we’ve reached a point of “saturation”, or even decline, in the use of social media by UK journalists.

The thing is, time spent is only relevant if you can relate it to a set objective. In this case the reduction in time seems most likely to me to be due to improved productivity in the use of social media by journalists.

Here are a few potential reasons for this:

– According to the survey, Twitter is the No.1 tool used by UK journalists (75 per cent). Our analysis of when UK journalists joined Twitter suggests they have had between three and six years to become proficient at it.

– Productivity tools are likely to be widely used by now, particularly by those who use social media the most. An example of this is in the survey where it highlights 25% of respondents saying they use Hootsuite.

– Knowledge from earlier adopters will have been shared with colleagues who joined later. Journalism.co.uk’s excellent newsrewired conferences are an example (the latest of which was yesterday).

Meanwhile the same survey also tells us:

– 54% of journalists who responded couldn’t carry out their work without social media (up from 43% in 2013 and 28% in 2012).

– 58% say social media has improved their productivity (up from 54% in 2013 and 39% in 2012).

If the survey is representative, this means a majority of UK journalists now think that the use of social media for work is both necessary and beneficial.

Isn’t this the real story?

In the words of Giles Palmer, Brandwatch’s CEO, it was because

“As we (Giles and Azeem, Peer Index’s CEO) talked, I became acutely aware that PeerIndex were years ahead of us in their understanding and technology for influencer analytics and mapping.”

But why the need for a social media monitoring company to invest so heavily** to address influencer analytics and community mapping?

The answer lies in the exponential rate at which online activity has been growing, and the challenge this has created to find the people, content and conversations that really matter to PR and Marketing objectives.

*I’m the founder and architect of Lissted for anyone who doesn’t already know me.
**£10m looks to represent around 10-15% of Brandwatch’s value based on filings relating to its most recent finance raising in May 2014.

A world of ‘pretty noise’

The key social media monitoring platforms (including Brandwatch) were conceived and designed in the mid-to-late Noughties when we had a fraction of the online conversations we have today. Even by the summer of 2008, Facebook had only reached 100 million users (versus 1.35bn now), Twitter had a measly 10 million (versus 284 million now) and Instagram didn’t even exist.

In this relatively quiet online world the platforms didn’t need to do much to address what I think of as the ‘laws of social listening‘. A few simple metrics – like number of Twitter followers – and keywords were often enough to find who, and what, mattered most.

As the scale of online conversation has grown in the last few years these platforms have invested in an arms race of engineering to try and keep pace with the demands of multiple sources, processing and storage. Meanwhile at their heart they mostly still treat ‘listening’ as a purely data driven exercise, continuing to use similar metrics of now questionable worth, combined with increasingly complex Boolean keyword strings, to desperately try and filter it.

This has resulted in what I call “pretty noise”. Beautifully designed front end applications with graphs, charts and word clouds that look wonderful, but often tell you very little of real value, or worse can be genuinely misleading.

Because real listening, and the insight that comes with it, requires an understanding of people and communities, not simply data mining.

Noise doesn’t equal influence

At the same time as social listening platforms have been struggling in a Canute-like fashion with this vast wave of conversation, we’ve also seen the rise of the ‘influencer’ – someone who is judged to have the potential to exert higher levels of influence over others.

Such people have always existed of course, but social media and the wider online world has increased the ways in which this potential can be earned, observed and utilised.

Again, a plethora of tools and platforms have been created to try and help users identify these influencers in relation to their brand, product or industry. The majority of them start with who produces online content around your chosen keywords, and then look at the reaction they generate before deciding who ranks highest.

Unfortunately, these tools generally suffer from the same weakness as the social listening platforms. The scale of conversation and online activity is so great that they often equate noise to influence.

And even when these tools are successful in identifying truly influential and relevant content creators, they’re still of limited use, as creating content isn’t the only way to be influential.

They may help me identify candidates for outreach purposes, but it certainly doesn’t follow that their answers will be relevant to other key Marketing and PR activities such as:

• ranking higher in search;
• organising an event with industry leading figures;
• understanding how my competitors are behaving online;
• reaching more relevant people with my own content; or
• identifying who I should target with my advertising.

True, there may be some active content creators in a particular field who will indeed be relevant no matter what your objective. For instance – if you’re looking at a list of UK PR influencers that doesn’t have Stephen Waddington near (or at) the top (as I was the other day) then I would seriously question whatever approach they’re using.

But mostly, this combination of noise driven methodologies and varying objectives has created a situation where users are asking questions of these tools, and it’s often just dumb luck if they get back the answer they really need.

It’s all about Community

So how do we address these two problems?

• Effectively listen to social media sources to find true insight in a real time world.

• Identify the right people and organisations depending on our objective.

The solution lies with understanding communities and the contextual relevance they provide.

It’s about identifying, observing and listening to enough of the key members of a community, particularly the ones who are authoritative and knowledgeable.

Lissted approaches this challenge in a very different way to Peer Index, but we do agree that if you can understand the makeup of communities relevant to you, everything else starts to fall into place. This is because the very people, content and conversations they are paying attention to are the ones that are likely to matter most.

We call this “Superhuman” social listening. A host of people who really know their stuff helping you to filter the online world and discover who, and what, really matters to your PR and Marketing objectives:

Reputation management: they’ll highlight important stories and conversations about your brand before most people get to hear about them.

Outreach: they’ll tell you who the content creators are that drive influential conversations, not just noisy ones.

Amplifying your content: they’ll tell you who the curators are that identify and share influential conversations.

Improving your search ranking: they’ll tell you the domains that they trust, which are therefore likely to be the very ones that Google will trust too.

Targeting your advertising: they’ll help you identify the people most like them and who share their interests.

Event organising: they’ll tell you who are the most recognised people in their field.

Real time marketing: they’ll help you identify what’s really getting relevant people engaged.

And so on……

The future

The critical element is the ability to identify these communities accurately. To find the right people to listen to. This is why we’ve spent the last two years developing Lissted’s real world approach to identifying relevant communities and why I believe Brandwatch have invested heavily with this acquisition to try and achieve this too.

I expect we’ll see a lot more activity around this challenge in 2015 and beyond as others in the social listening industry recognise the need to address the elephant of noise in the room.

Beta invites

We’re currently running a private beta of Lissted’s latest community analysis tool. If you’d like to get involved then drop me an email, adam@lissted.com.

Source: Moviepilot.com

Genesis

Two things collided to create this post.

First, I’m a huge ‘The Big Bang Theory’ fan. For anyone who doesn’t watch the show its central characters are two roommates – Sheldon Cooper and Leonard Hofstadter – both physicists at CalTech, and Penny, an aspiring actress and “temporary” waitress who moves in across the hall from them.

Penny lacks the guys intellect, often struggling to understand (or care) what they’re talking about, but she knows a lot more about life and relating to people than the two geeky scientists.

Second, I showed a very clever real life physicist, Stephen Baldwin* a graph of retweet activity over time for one of the tweets identified by our Tweets Distilled experiment. Tweets Distilled seeks to identify interesting tweets early in their lifecycle.

Stephen suggested there could be parallels between what makes content successful and the physical properties of heat capacity and phase transition.

*Stephen specialises in acoustics and sound processing and is currently looking for a new challenge.

Our Theory

Here’s a summary of what we produced:

Content heat capacity

Content phase transition 

Sheldon Cooper will explain the physics behind these properties later in the post. First here’s the story of the tweet that got me and Stephen talking….

Giving it 110 per cent

On the day of the Scottish Independence Referendum, CNN’s graphic department had clearly been listening to too many footballers’ post match analysis as they put this graphic up on screen.

A Twitter user called Brady, who goes by the screenname of @BurningGoats picked up on the error and tweeted:

At least no matter what Scotland decides, they are giving it 110%. #ScotlandDecides #CNN pic.twitter.com/fl9dzQlaYL

— Brady (@burninggoats) September 18, 2014

This was just after 8pm. The graph below shows the average retweets per minute in the 8 hours after the tweet was posted. You can see that in the first hour and a half there was a limited amount of activity. and by 21:31 the tweet had been retweeted 62 times. This is exceptional for Brady as none of his tweets in the subsequent 2 months have had more than 2 retweets, but it’s nothing compared to what happens in the next few minutes. At 21:31 the tweet is retweeted by @BefairSports, an account that doesn’t follow @BurningGoats. @BetFairSports has over 80,000 followers, one of whom is the ex Liverpool and Germany footballer Dietmar Hamann who has over 600,000 followers. He also retweets the story at 21:33 and immediately following this combination we see a massive spike in retweet activity. From then on the tweet never looks back maintaining a rate of 40-80 retweets per minute for the next couple of hours.

Finally at 02.26 the story is picked up by the mainstream media and appears on the Daily Mail, whose article generates nearly 1,500 comments on Facebook. The Science

So how can physics help to explain what happened? Over to the awesome Sheldon Cooper to explain.

Source: bigbangtheory.wikia.com/

Sheldon: Two physical properties are relevant here. Heat capacity and phase transition.

Heat capacity

Heat capacity is the amount of energy that is required to increase the temperature of a material. It can be expressed as an equation, thus: So the higher the heat capacity, the more energy it’s going to need to get hot. Different materials have different heat capacities.

Leonard explanation for Penny: It’s why they don’t make hair straightener plates out of rubber!

Phase transition

Sheldon: As the temperature of a material rises it will change state. These changes are called phase transitions and the most well-known are from solid to liquid and liquid to gas. There is also a fourth state that matter can take. When a gas is heated sufficiently it will ionise and form plasma (the most abundant material in the universe). Different materials go through phase changes at different temperatures.

Applying physics theory to Content Marketing

Content heat capacity

Sheldon: If we relate:

– heat capacity as a measure of the quality and likely potential interest a piece of content possesses within an online community (where high quality content is equivalent to material with a low heat capacity);

– energy as the tweets, retweets, favorites, likes, shares and other forms of engagement with the content it receives; and

– temperature as the level of interest it’s achieving within the relevant online community;

we get: This predicts that low quality content will need high engagement to raise the level of interest being shown in it.

Leonard to Penny:  “You might read an article on toilet brushes, but only if Gerard Butler tweeted it!”

Content phase transition

Sheldon:  If we see the different states content can exist in as: Solid phase = low quality content that even your followers find uninteresting or decent content that receives very little engagement.

Liquid phase = great content that’s got some engagement and started reaching followers of your followers, or not so great content that’s lucky enough to get engagement from some influential sources.

Gas phase = content that’s reached the wider community, either because a) it’s awesome and quickly got the attention of people dotted throughout that community, b) it’s pretty great content that’s getting lots of engagement generally or c) decent content that’s had the full star power treatment to force it out into the wider community.

Ionised = Amazing content that’s so “hot” you’d actually talk to someone about it in the real world and/or it’s appearing in media outside of the online community concerned.

The key is that the more widespread the appeal of the content among the community the lower the temperature (interest level) it will have to reach to change its state.

So awesome content will take a lot less energy to go through these phases and start reaching the wider community .

Leonard to Penny: “It’s why the biggest secrets make the best gossip.”

Source: fanpop.com

How the theory fits the CNN Story

Sheldon: The CNN graphic is great material. It’s funny, has immediate visual impact and it’s got numbers, and everyone loves numbers. It therefore has an inherently low heat capacity i.e. very high content quality rating.

The tweet by @burninggoats improves on this by bringing in the “giving it 110 per cent” phrase that is often used by sports people. This meant the tweet itself had an even lower heat capacity and so raised its inherent quality further.

Implication: The tweet only needed a relatively small amount of energy (engagement) to raise its temperature (interest level). 

At the same time the content also had the potential for widespread appeal. Whether you were interested in the Scots referendum, appreciated the sporting reference or simply wanted to have a laugh at CNN’s expense, many people were likely to find this content interesting.

Implication: The tweet only needed a relatively low increase in temperature to change state and reach the wider Twitter community.

Combine these two and you had content that only needed a relatively small amount of energy (engagement) and it was going to reach far and wide. This is what happened when it received the retweets from @BetfairSports and @DidiHamann. And the content subsequently “ionised” when it was published by the Daily Mail.

Practical application of the Theory

To be successful you need to recognise three key implications:

1. Content creation and design is crucial

If your content isn’t all the things you know it should be – well designed, eye catching, exciting, thought-provoking, surprising, timely, appropriate format etc – then its going to need a lot of energy from the community to raise the interest level.

If you don’t possess this yourself e.g. a brand with a huge organic following like Apple, or you can’t buy it (celebrity endorsement for example), then content like this is going to really struggle to reach beyond a small proportion of those who are closest to you.

2. Listen to understand what will have widespread appeal

If you want to reach the parts of a community you don’t already know then you need to understand what is likely to engage those people, as well as those close to you, and design your content accordingly.

This will mean that your content has the potential to “change state” at much lower levels of interest. Again this means the energy (engagement) requirements to achieve this are lower.

3. Influencer engagement (and potentially paid promotion) will often still be necessary for success

In almost every case the quality and appeal of the content will only get you so far. They will reduce the energy requirements, but they won’t eliminate them.

As we saw with the CNN example, the innate quality and widespread appeal of the tweet meant it turned into a liquid (reached the followers of Brady’s followers) quite quickly. Even still it took the input of energy from Betfair and Didi Hamann’s engagement to make it change state to a gas and start reaching the wider community.

This demonstrates one of the potential benefits of an influencer strategy. Though be wary. It’s important that the influencers, really do possess the potential for influence. Don’t get fooled by simple reach numbers. Make sure they are highly relevant so that the wider community they help you reach is the one you were looking to target.

Finally in a corporate situation the use of paid promotion should be considered as an alternative to provide this additional energy when it doesn’t appear organically.

Sheldon Cooper signing off. 

Social listening is big business. Having access to a platform that can interrogate social media data has become a must have for many, if not most, organisations.

The problem is the vast majority of these platforms suffer from major flaws which can often lead to them producing pretty graphs and analysis that at best tell you very little and at worst are hugely misleading.

For social listening to be effective in providing genuine insight, it needs to fully address the following: refine, contextualise and weight the data, and be wary of sentiment analysis.

1. Refine

This is where it starts. Rubbish in, rubbish out. Within any social media the volume of spam (or robot created) accounts, conversation and content is vast. Any social listening exercise or platform must be effective at filtering this crud out, otherwise the resulting analysis is going to be seriously flawed.

Think of it like refining oil. You could try running your car on raw crude, but I wouldn’t recommend it.

Separating the wheat from the chaff is not a simple exercise. Just saying “let’s look at data from accounts with more than X followers” or “at least X shares” or similar sliders that many vendors provide doesn’t cut it. In fact, it can often result in missing really important conversations involving key individuals who just happen to be less active or noisy.

You need filters that are sophisticated enough to eliminate the noise, whilst retaining the signal, something Ray Dolby knew a thing or two about.

2. Contextualise

The keyword. That wonderful item, beloved by social listening platform users the world over. Selecting the right keywords has almost become an art for some people. They produce complex Boolean searches in a desire to find the conversations they hope are contextually relevant.

The thing is, the more complex your keyword search criteria, the more likely you’re missing something you needed to hear. I want to identify conversations about Apple the brand not the fruit, so I search for a string of “Apple” AND “XXXX”, or “Apple” NOT “XXXX”.

More sophisticated solutions use pattern recognition and topic clustering to identify the context and save you thinking of every AND XXXX or NOT XXXX. But what about conversations that are relevant, but in a less direct way? What about competitors like Samsung or Microsoft? When I ask for Apple data do we include these conversations or ignore them? Is it only when they mention Apple? Where do we draw the line?

The other major weakness in these approaches is that they rely on semantic content in the first place. If keywords aren’t present in the social media post then the system can’t identify them. Makes for a pretty major weakness when you consider the huge increase in visual based social media like minimally tagged Instagram posts, or tweets where it’s the picture that tells the story.

The bottom line is neither of these approaches is a human way of listening. We don’t rely on keywords to tell us if something is insightful or of interest; we have the capacity to recognise it when it is. Social listening solutions need to be designed with this in mind.

After all we are listening to people, not simply crunching data.

3. Weight

When Barack Obama gets up to the White House podium, the world listens. When @abc45xxx (not real) with 200 spam followers auto posts a link to an article, no one does.

I’m not talking about influence measurement here – though it’s a related topic – I’m merely pointing out that social media may have democratised conversation, but that doesn’t mean that every social media post should be given equal weight. It also doesn’t mean that only posts from “influencers” are important either.

Social listening solutions must take account of the relative importance of what is said, by whom and how people reacted to it and then weight their analysis accordingly.

4. Be wary of sentiment

This is simple. Automated sentiment analysis is quite simply not much better than a coin toss in many cases.

If you’re happy to make business decisions on this basis then fine, otherwise don’t believe those seductive dashboard graphs and accept that you’re going to have to take a more human approach to such analysis and that means you probably can’t do it for every last tweet, YouTube comment, blog post etc. 

Consequences

Compliance with all the first three requirements is an absolute necessity if a social listening exercise is to have any potential for producing insight.

If you don’t filter out the noise your analysis is flawed from the start.

Filter the noise, but don’t ensure you have the right context and you’ll be listening to the wrong conversations, even if they will be crystal clear.

And fail to weight what was said and you risk missing the key voices that were driving the conversation.

Social listening solutions need to start addressing these (f)laws properly and fast, before users realise how irrelevant and misleading a lot of what they are telling them actually is.