OUR DIGITAL MASKS - Socint

OUR DIGITAL MASKS: How Britain behaves online, 2020

OUR DIGITAL MASKS: How Britain behaves online, 2020 -


FOREWORD: Notes on online behaviour at the start of a new decade

Over the last few years social media has evolved from an online community industry into the world’s largest advertising platform and a weapon used in hybrid warfare offensives. The human side of this, however, is often forgotten in the race to create algorithms and artificial intelligence to extract the data of civilisation and turn it into graphs and charts and money and elections.

There is an elderly saying, about what happens to people when you give them masks, which is as current in the borderless world as it ever was. The internet, and social media in particular, provide a digital mask behind which the people logged in lose many of the inhibitions and controls which keep humanity in check. Language standards fall away, prejudices are openly displayed, and very often predatory pack activity replaces the reasoned behaviour and compliance with the law which every day life requires. What we used to see in riots, we now see in our daily digital lives and, increasingly, we are experiencing the real-world consequences of virality.

This has brought with it increasing levels of abuse, constant stuttering by authorities in the response to what would be considered criminal on the street, and an increasing need for regulation and legislation hampered by a lack of understanding of the problem at a policy level.

Much of the conversation lags drastically behind developments in understanding. People still talk about bots when they mean accounts managed by troll farm operators or, closer to home, genuinely unpleasant people in our own towns and villages who misbehave simply because they can. Into the upper levels of understanding, there is still a basic failure to understand the key relationships between user numbers, engagement levels, and advertising revenues which make these platforms the ideal place to amplify messages through the basic psychology of human behaviour.

Lip service has been paid, of course, to enhancing user experiences and improving online reporting options. The reality is, however, that almost nothing has changed with one exception: people are getting used to life online and are starting to conform the new norms it has created.

This report is deliberately written in plain language to provide a recognisable landscape. The purpose is to drive the conversation forward in a way which is not reliant on the often bamboozling techno-babblings of the platforms themselves or the industry pundits who have carved out niches around them.

Very often, in the big data and the excitement of AI, a simple truth is forgotten: People are people and they behave in response to the constraints of their environment. The same was true in the Wild West.

James Patrick

Director

SOCINT: Social Intelligence


Summary of Findings:

1. Abuse directed at political figures appears to come disproportionately from accounts which obfuscate their real-world identity.

2. Lower level abuse and name-calling is almost evenly distributed across identifiable and anonymous accounts and comes from both sides of the Brexit divide.

3. The most commonly used offensive terms are widely deployed by a broad range of users in common parlance but use of such terms in a deliberately offensive way is more likely to occur in the case of anonymous users. (There is a disinhibition impact surrounding anonymity.)

4. There was a moderate level of automated and suspicious Twitter account activity identified during the 2019 General Election. Of the party leaders, Boris Johnson appears to have had largest network attraction to such accounts.

Summary of Recommendations:

1. The introduction of Digital Identification, providing accountability while facilitating the preservation of outward anonymity.

2. Expansion of current regulatory frameworks to capture the official accounts of journalists under the duties which occur for them and their publishers under other circumstances.

3. An update to Public Order legislation, formalising the recognition of the digital world as a public place.


CONTENTS:

6, Methodology and Terms………………………………………………………. 7-14, “Die” And Other British Greetings………………………………………………. 15-17, Brexit, The Great Indivision……………………………………………………. 18-23, Ladies What Lead…………………………………………………………... 24-31, Brits Or Bots? ………………………………………………............................ 32-45, Happy F@#$%*g New Year………………………………………………... 46-47, Conclusion………………………………………………………………….


METHOD:

Each chapter of this report is preceded by a “Notes on Sample” section which describes the size of the data sample, the source, and how sub-samples and dip-samples were drawn from the data.

The core data, the original source, is accessed in several different ways, each of which are compliant with platform usage.

For rapid analysis, commercial services including Trendsmap were used to provide key term searches and datasets including timelines of posts, hashtag usage, geographical origin, and so forth. More in depth analysis relies upon the use of an in-house system which streams and captures millions of tweets across the globe each day. This system was built using Kibana/Elastic Search and sits on secure servers but further information on how the system was developed are subject to commercial sensitivities.

The analytical work is conducted within a sterile corridor. The sterile corridor is a component of the Social Intelligence Model, specifically designed for work in this field and influenced by the National Intelligence Model used in UK policing and intelligence operations.

Analysis of accounts for signs of automation, suspicious behaviour, and anonymity is carried out using a three-system computerised check, a manual examination of account activity – including language usage, keyboard punctuation style, sleep-patterns and timezones – and a visual investigation, seeking signs of human behaviour manifested in, for example, personal content or geographically relevant images.

Terms:

Bot – An automated social media account programmed to post or share certain content.

Troll – A human managing a social media account to deliberately harass and abuse.

Troll Farm – A commercial entity which is paid to provide trolling services to clients.

Cyborg – An account which is human-managed but which uses partial automation to share content

Signal Amplification – An increase in the visibility of online content achieved, often achieved through algorithm gaming by inflating the number of shares or replies to content using bots and trolls.

Inauthentic Behaviour – The creation of online profiles and content which are false or misleading, or indicate off-platform co-ordination around digital campaigning on topics or in elections.

Suspicious Accounts – Profiles or accounts which exhibit indicators of behaviour outside of human norms. Such accounts may have names featuring lots of numbers of letters, mismatched user and profile names, photographs which appear elsewhere, be posting in a language other than that relevant to their stated location.

Anonymous Accounts – Accounts which do not feature an identifiable human name, a profile photograph other than that of a person, a bio which obfuscates everything about them, and which do not feature personal tweets or media or references to world events which would identify them as a person existing in a specific place.

Identifiable – A real person who can be identified through examination of their online profile.


“DIE” AND OTHER BRITISH GREETINGS: The use of language and public discourse with and around MPs in the digital world

Notes On The Sample:

Several years ago a bespoke software system was created to stream, capture, and archive millions of tweets across the globe. For the purposes of this analysis, a dip sample of UK tweets around MPs was created using the sixty-day date range November 13 2019 to January 12 2020.

The whole sample contains 10,162,934 tweets and dip sample sets were created from this.

From the main sample, all tweets from accounts with between 1,500 and 4,000 followers were isolated, creating a sub-sample of 1,283,444 tweets. This was filtered for tweets containing the words scum, die, bitch, and rape, generating a dip sample of 1,672 tweets for analysis.

From the main sample, all tweets containing the words scum, die, bitch, and rape were isolated, generating a dip sample of 15,603 tweets for analysis.

From the main sample, all tweets containing the usernames of Nicky Morgan, Laura Kuennsberg, Jess Phillips, and Allison Pearson were filtered, creating sub-sample of 243,627 tweets. This sample was isolated to the day within the sample period in which the most traffic was generated, December 1 2019, generating a dip sample of 22,687 tweets for analysis.

From the main sample, all tweets containing the words black, retard, and nigger alongside the username of Diane Abbott were isolated, creating a dip sample of 191 tweets.

From the main sample, all tweets containing the words black, retard, paki, and nigger alongside the username of Priti Patel were isolated, creating a dip sample of 48 tweets.

While the use of deliberately offensive behaviour accounts for a low percentage of all content posted on Twitter, it is very often targeted at higher profile figures, increasing the visibility of the material itself. This, in turn, acts to embolden others.


Analysis:

The main sample set of 10.1million tweets between November 13 2019 and January 12 2020 was filtered to isolate posts from Twitter users with between 1,500 and 4,000 followers to establish a content range with an average reach and public visibility.

This generated a sub-sample of 1.2million tweets so a further filter was applied, isolating tweets which contained any of the words: scum, die, rape, and bitch. This reduced the sample size to 1,672 posts.

The dip sample was extracted for analysis and scanned for repeated tweets by the same user. This showed that 1,429 unique users were responsible for the 1,672 tweets mentioning the filter words. The unique users have a combined audience of 3,462,477 followers.

Of the total number of user accounts in the dip sample, 160 users had posted more than once using the filter words. A visual analysis of the keywords this group used in posts showed repeated use of the filter terms clearly:

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Examining the most repeated words in the posts by the repeat user set, looking specifically at more than 15 repetitions, the most used words were scum, die, and rape, with the fourth filter word – bitch – falling some way behind the party leader, Jess Phillips, and the link to a Herald Scotland article with the headline: Boris Johnson: I'll never debate Sturgeon and I'll never agree to a second Scottish independence referendum.

A random dip sample of the tweets featuring the word “scum” revealed a pattern of abuse towards MPs across political divides:

1: “#BrexitJohnson #BrexitShambles #Brexitdeal No you are not you lying scum bag, and you never will”

2: "@jeremycorbyn @johnmcdonnellMP I DIDN'T KNOW IGNORANT ANIMALS THAT SUPPORT MURDERING TERRORIST SCUM COULD WRITE ! #Corbyncanwrite"

3: “@BorisJohnson Are the bullingdon scum stopping burning 50 notes in homeless peoples faces that's great news. Vomit.”

4: "@wesstreeting Yes Wes, you are Last night Jews were being called Fascists and Scum at a corbyn rally, but because you were worried about your job you kept quiet, and made one more push to get the man they worshiped elected!”

5: "@johnmcdonnellMP Marxist scum - you are one sinister man You will ruin the UK Nobody falls for your friendly uncle bullsh1t Investment will flood out of our country if you are in government - mass unemployment will ensue. #GE2019 #NeverCorbyn”

6: “@Jacob_Rees_Mogg you make me sick you pious scum bucket”

7: “@NickyMorgan01 @Conservatives So what stopped you Tory scum doing ANY of this stuff in the last 9 years you've been in power? Too busy cutting local council budgets & throwing orphans out into the storm in rags? You are a monster. #ToryScum #ToriesOutDecember12 #TacticalVoting

8: “@MattHancock #NHS was built by migrants and is maintained by migrants. The racist #Tory scum hate the #NHS because it's about public service not private greed.”

9: “@SKinnock @Couchy174 obvs that Corbyn and McDonald are both terrorist fanboy scum who actively despise their own Country ( ie the voters )”

10: “@JamesCleverly Just like the Tory UKIP scum have become with an undemocratic, advisory referendum! Fuck I hate you shit stirring scum! Pick a ditch!”

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jeremycorbyn

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MattHancock

victims

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NHS

https://twitter.com/heraldscotland/status/1196557861452693505

https://twitter.com/heraldscotland/status/1196557861452693505


The word “rape” was largely confined to discussions on the Cyprus incident and news stories relating to “rape gangs”, while the word “die” was used in two contexts: the first in repeating “die in a ditch,” mostly used as an insult to Boris Johnson, and the second in the context of hospital and poverty deaths.

The terms bitch was more broadly used, with several repeating themes picked up in a random dip sample.

1: “@jessphillips @EveningStandard LEADER???? I think not! NASTY BITCH jobs yours!”

2: “@michaelgove Come back when you're a leader not Johnson's bitch”

3: “@HackneyAbbott God, give it up, you retarded bitch!”

4: “@Siobhain_Mc @daneacross @jeremycorbyn This is YOUR fault remaining bitch . You remainer blairites smeared him relentlessly”

5: “@EmilyThornberry Go away you terrorist loving bitch, fuck off to Iran, Syria or ROI, you will be much happier there”

Taking a representative sample (16 users or 10%) from the repeat user group, they were visually analysed and tested with bot detection products. The latter result was unsurprising, with all of the users showing no signs of being inauthentic. The visual analysis focused on the public facing Twitter profiles of the users in order to ascertain whether the account owners were anonymous or identified themselves. Only 25% of the accounts were identifiable while 75% of the accounts were anonymous, with no given real name or photograph or media to identify them.

Expanding the sample back to follower count, there were three categories of users within the main dip sample.

There were 324 users with over 3,000 followers and a combined following of 1.1million and 695 users with over 1500 but less than 2000 followers and a combined following of 857,024. The largest sub-group was those accounts with a following of between 2,000 and 3,000, and accounted for a combined audience of 1.4million.

The usage of the four filter words was heavily weighted towards “die” due to a large number of retweets and quoted tweets relating to the “die in a ditch” phrase, with bitch and scum used as deliberate insults. In total, across users with all levels of following, the filter words appeared 15,603 times.

Bitch6%

Scum17%

Die50%

Rape27%


The larger sample of 22,687 tweets, taken from the sub-sample of 243,627 mentioning the four female public figures yielded a slightly different result.

With the date range confined to December 1 2019, retweets were eliminated, leaving 5,465 original content posts by Twitter users. The majority of the eliminated retweets (over 17,200) belonged to Jess Phillips who had achieved a great deal of engagement success that day.

Repeated posts were then removed, leaving a dip sample set of 4,425 tweets.

Within this set, the vast majority of tweets mentioned Jess Phillips (2875 tweets), with Laura Kuennsberg being mentioned 22 times, Allison Pearson being mentioned 6 times, and Nicky Morgan only receiving 1 mention.

While the content mentioning Allison Pearson and Nicky Morgan in this sample was inoffensive, the limited content mentioning Laura Kuennsberg continued snippets of a long-term pattern of abuse:

1: “@patel4witham @AndrewMarr9 You've just had anal sex with him, eh? Bet @bbclaurak is raging.”

2: “@BorisJohnson I take it Dom plagiarised these tweets from @BarristerSecret for you? Or was it @bbclaurak ?”

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Total Sample RTs Removed Duplicates Removed

Jess Phillips, 2875

Nicky Morgan, 1

Allison Pearson, 6

Laura Kuenssberg, 22

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Jess Phillips

Nicky Morgan

Allison Pearson

Laura Kuenssberg


With tweets mentioning Jess Phillips making up the bulk of the sample set, a visual analysis shows a fairly unconcerning distribution of language:

However, a close inspection shows a more mixed bag of online behaviour:

1: “@jessphillips Is that after knocking on thousands of doors too love? I'd be more concerned with your leader”

2: “@jessphillips Yeah, yeah, yeah. Naturally we believe you. You're getting as bad as Johnson.”

3: “@jessphillips BULL-SHIT!”

4: “@jessphillips I think u might find that Jess is the liar . The one who is a lying twat is u Jess”

5: “@jessphillips I would rather not hear you comment on Rape Jess, as you have totally ignored the industrial Rape”

6: “@jessphillips How many trees you planted today ? #Stealing a wage”

7: “@jessphillips And i suppose your gang of scrotes led by Corbyn & the socialist workers party will keep us safe”

8: “@jessphillips Two votes here for you #yardleysaynototories”

9: “@jessphillips Jess, you're my secret crush, you're a spicy one, but that's bollocks.”

10: “@jessphillips #CorbynMeansCommunism”

11: “@jessphillips I love your integrity and tenacity but I'm afraid that an alarmingly growing group of people don't”

12: “@jessphillips Ahh that wonderful woman called Jess she’s just obnoxious #GE2019”

13: “@jessphillips Jess is going to work for free to show us all how it's done.”

14: “@jessphillips I'm sick of all you bastards. You're all the same. You import trouble. You don't ask the electorate"

15: “@jessphillips What's the Lie then Me,me,me,me? Thought not."


Visual dip sampling on the whole subset of 4,425 tweets tended to confirm that the 75% anonymity rate is fairly consistent across the two differing sample sets.

In the Diane Abbott sub-sample of the main sample, of the 191 tweets in the filter group 12 featured the term retard and all followed a very similar trend of insults:

1: “@HackneyAbbott F_ck of Abbott u retard. You would let the whole lot out like you would IRA members”

2: “@HackneyAbbott @alantravis40 It won't keep us safe if they are locked up? Are you a retard?”

3: “@HackneyAbbott Retard alert..!!!!”

4: “@HackneyAbbott how are your shoes looking now, you fat fucking retard”

5: “@HackneyAbbott By your maths I wont take the risk! #Retard”

Within this subgroup, the anonymity ratio dropped to around 60%.

The majority of the tweets in this subset featured the filter word “black” and were mostly on discussions surrounding the community, led by community figures. However, approximately 7% of the tweets contained assertions of racism on the part of Diane Abbott, following comments she allegedly made about motherhood.

1: “@HackneyAbbott @junesarpong This is racism. Black people always support each other but never support/praise white people.”

2: “@HackneyAbbott @alantravis40 Can’t we lock you up for saying black women are better mothers than white ya #racist”

3: “@HackneyAbbott Abbott only seems to have one argument and that is that being a bigot is fine cuz she is black init”

4: “@HackneyAbbott Look at you trying to stir up trouble between white and black people you need stopping.”

The term “nigger,” while searched for having been used previously in highly offensive content aimed at the MP, did not appear in the subset.

In the Priti Patel subsample, of the 48 tweets in the filter group one contained the word “nigger”:

1: “@patel4witham Peepee, I've worked with young people for twenty three years. Heard a lot of language. Nigger, fatty…”

The word “retard” also featured in one tweet:

1: “@BorisJohnson @patel4witham Retard. You wont get brexit done in 3 days. #GetBorisGone”

The word “black” featured in most of the tweets in the Priti Patel subset, however most of the use referred to issues being black and white and mentions of an absence of black pudding in response to a tweet of the MP’s about what she was eating for breakfast. However, a small number of clearly racist tweets were present, following well-established far-right tropes:

1: “@BorisJohnson @patel4witham @SadiqKhan black gangs murdering people on our streets every day with impunity.”

2: “@patel4witham Stopping more blacks from coming in to the UK would make the biggest difference.”

3: “@patel4witham Arrest and deport all those black and muslim gangs and their so call communities.”

4: “@patel4witham Enforce the law with black and Asian gangs and half the job would be done. Too much tip toeing around”

The term “paki,” while searched for, did not appear in the subset.

The anonymity ratio in the Priti Patel subset was closer to 50:50.

It is apparent that openly racist language is still used, though perhaps in smaller volumes than previously noted given the size of the main sample.


Summary:

Repeat users employing abusive terms deliberately in offensive manner are human, largely anonymous, and are directing abuse at MPs in varying volumes with racist abuse forming a lower volume of content and more general abuse forming a higher volume of what is posted.

Abuse is evenly spread across MPs, with no core difference surrounding party divides or Brexit lines.

Anonymity ranges from 50% to 75% and is tightly linked to the issue of online abuse.

If the anonymity range were to be applied to the whole 60-day sample of 10,162,934 tweets, without formulating a secondary duplicate user estimate or abuse ratio, anonymous users would be responsible for between 5,081,467 and 7,622,201 of the tweets.

It is clear, however, that where the anonymity level is lower, there is less abuse and offensive behaviour and more conformity with the expected societal norms.

There remain a number of tactical and strategic options open to dealing with these issues:

1) Wait for the platforms to introduce new features which limit replies to tweets from public figures. 2) Introduce regulation or legislation which outright prohibits online anonymity. 3) Introduce focused legislation which creates a state-led approach to online accountability while

preserving facilities for accounts to be outwardly anonymous.

There are a number of pros and cons to each of these options, with 1) being the least desirable of all given that the platforms have not addressed a number of core issues and appear to be showing only limited signs of taking state interventions seriously – though Germany has had some success with new hate laws. The introduction of legislation which effectively bans online anonymity is likely to face challenge at every stage from a number of actors and is not a desirable outcome as it does not weigh core freedoms.

The third option provides an opportunity for the Government to work with platforms in shaping a digital ID system which has additional applications and ensures that, while people can preserve their identity outwardly – necessary for a number of reasons for many people – they could be held officially accountable for their online actions. This would also ease the administrative burdens on services such as policing and on the platforms themselves.

It is clear, however, that something needs to be done to make extensive existing law more operationally effective in the online environment in order to eliminate online abuse. Accountability is key to this.

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5081467

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Total Sample Anonymity (Lower - 50%) Anonymity (Upper - 75%)


BREXIT, THE GREAT INDIVISION: Insults and abuse from both sides of the Brexit divide.


For the purposes of this analysis, a dip sample of UK tweets to MPs was created using the sixty-day date range November 22 2019 to January 21 2020.

The whole sample contains 8,949,901 tweets and a dip sample set was created from this.

From the main sample, all tweets featuring the well-known hashtag FBPE in the text were isolated, creating a sub-sample of 426 tweets. This was filtered to remove retweets of MPs, leaving a sample of 217 tweets.

A random sample of 25 usernames was selected to be sampled for identifiability and anonymity and the content of the 217 tweets was examined for content and behavioural themes.


Analysis:

The dip sampled accounts were subjected to an investigative analysis, manually checking each to ascertain whether a genuine name was used, a genuine photograph, what kind of content the account posted, whether they posted personal pictures or real-life details, and so forth.

Just over half of the accounts were anonymous, carrying no features which would render a known person identifiable as being accountable for the management of the profile. The average follower account across the sample group was 3,719 meaning these accounts are relatively healthy in terms of their reach.

A small number of MPs were repeatedly mentioned more than others within the sample group, with Boris Johnson being the most used account name for inclusion.

Beneath the level of party leaders, Liz Truss and Matt Hancock received the most mentions.

Terms most used co-concurrently with FBPE included BorisTheLiar, LibDems, GTTO, and remain.

It is clear this sample exists within the Brexit echo chamber of Twitter.

Anonymous, 52%

Identifiable, 48%


Content directed at Boris Johnson tended to feature more repetition by single users and included insults such as “@BorisJohnson looks more and more like Frankenstein' s creature. A monster of his own making. #FBPE,” but also featured a number of supportive tweets, critical of the FBPE subset of the remain activists: “@BorisJohnson Waiting for the #FBPE crazies to start shouting and screaming. Please reserve my seat while I fetch some popcorn . Ta x”

This theme was repeated in the case of Liz Truss, with insults including “Stop smiling like a goon. You are not impressing anyone . #FBPE,” and more supportive replies including “@trussliz The level of XENOPHOBIA from anti Brexit #FBPE cultist nut-jobs is immeasurable.”

This pattern repeated across the sample, with lower level insults being matched almost evenly by supportive insults.

The clear theme identifiable was of pro-Brexit supporters being defensive of MPs on the receiving end of abuse from those overtly identifying as FBPE Remainers.

There was little variation in the levels of anonymity on either side of the Brexit divide.

Due to the levels of anonymity, how much of the content is masked issue amplification – which is referred to by some as ‘astro-turfing’ – cannot be ascertained.

Of the accounts sampled, none showed signs of automation when tested.

Summary:

The proportion of anonymous accounts is slightly higher than those identifiable as real people who could be held readily accountable for their online behaviour.

While there is little difference in the level of abuse or quality of language used, those purporting to support the remain side of the Brexit debate are more vitriolic towards MPs, while those supportive of Brexit are more likely to defend MPs with derogatory insults to remain supporters.

The sample is generally reflective of the low quality of public discourse on display across the country in the digital environment.

For the sake of comparison, the word “cunt” appeared in 6,808 tweets from the main sample and was used repeatedly as a direct insult to a number of MPs, including those already mentioned and others from across part lines (for example Jacob Rees-Mogg and Wes Streeting).

This term was most commonly used in variations of the phrase “fuck off you cunt” and the level of anonymity rose to 74% in this subsample.

This works to confirm the emboldening or disinhibiting affect of anonymity on interactions with public officials and tends to show that the more offensive the abusive behaviour, the less likely people are to deploy it where the chances of being held accountable are greater. This is no different to real-world scenarios and indicates that a closer or, more accurately, clearer regulatory alignment with offline legislation will have the affect of reducing online harms.


LADIES WHAT LEAD: The online trends and behaviour patterns surrounding senior British figures.


This sample is designed to give a snapshot in time of the social media, trends, and traffic surrounding four key figures: Allison Pearson, Jess Phillips, Nicky Morgan, and Laura Kuennsberg, all of whom have faced a variety of online abuse connected to viral amplification and co-ordinated behaviour.

This sample draws down an account analysis, to provide a summary and highlight any red flags in surrounding behaviour, and further datasets which include co-concurrent hashtag usage, data sources, geography, and influential or widely shared tweets.

The sample for each figure is built on two distinct search term sets: their name as a search term and their username as a search term. The date range is the four weeks ending on January 7 2020.

Public Figures:

Allison Pearson

Jess Phillips

Nicky Morgan

Laura Kuenssberg


Analysis:

To give some initial context, prior to searching and extracting the data pertaining to the four public figures headline data was obtained for two popular footballers who have been subject to high profile abuse. This topic has garnered a significant amount of media attention.

In the four week period, 66,900 tweets were generated featuring the name “Troy Deeney” and 89,600 tweets were generated featuring the name “Rudiger”. The maximum tweets per minute peaked at 690 and one of the most popular posts achieved 3,000 reactions, 1,200 comments, and 443 shares on Facebook via Match Of The Day. In the case of both footballers, the given genders of those tweeting about them was 80% male and 20% female.

Over the same period, mentions of the public figures names generated comparable amounts of traffic with a much lower average in terms of the maximum tweets per minute (68) and a slightly lower male domination of the stated gender of the users involved (35% female, 65% male).

This picture changes again when using the username as the search term:

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https://www.facebook.com/8585725981/posts/10158406848410982


With the exception of Nicky Morgan, where there exists a greater propensity to discuss the former MP rather than discuss with her, the data shows significant levels of direct engagement with these public figures. The tweets per minute rate more than doubles to 146 on average and the male proportion of interaction increases to 67% while female reduces to 33%.

Viewed as a whole, these four public figures generate and were engaged by significant volumes of digital conversation in the four-week period, with the average maximum tweets per minute peaking at 211 and male generated traffic standing at 66% versus 34% female.

With an average of tweets per public figure of 226,500 this would have generated around 5 notifications every minute, twenty-four hours a day for the whole four weeks. A constant bombardment. However, activity is never so evenly distributed. It come in peaks and troughs, hence the addictive nature of social media.

To visualise this over the four weeks in the case of the public figure with the highest volume of activity, Jess Phillips, her timeline manifested in several huge peaks:

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Tweets To Tweets About Total


Dry data, however, is not necessarily the best way to view what conversation is taking place around public figures. While it does provide useful context, understanding what the bombardment they face looks like is often more useful. A random selection of five tweets per each of the public figures follows.

Tweets About Nicky Morgan:

“Congratulations to Nicky Morgan on her life peerage for services to lying on behalf of an utter charlatan to whom you wouldn't have given the time of day six months earlier.”

“Lady 30 pieces is welcomed to the Lords.”

“So there it is, you can't even vote the fuckers out any more. They do what they want, and we handed it to them.”

“What a fucking scandal - abandon your principles, lie for Johnson and your rewards is a cabinet post without even having to bother trying to get elected. @NickyMorgan01 what a disgrace”

“There are many words to describe Nicky Morgan, but none are polite.”

Tweets About Allison Pearson:

“Allison Pearson is an incessant reminder of how the British press lavishly reward professional stupidity.”

“Oh my fucking god.”

“Don’t normally tweet things like this, but: the Telegraph should absolutely sack Allison Pearson for bringing the paper into disrepute - she is either too stupid or too dishonest to do her job and this is far from an isolated incident.”

“Can someone please tell Allison Pearson to stop passing off totally fabricated, politically-motivated crap as journalism? That's supposed to be my job!”

“Allison Pearson is a complete gobshite.Pass it on.@Telegraph #ToryLies #ToryLiars #ToriesOut #SaveOurNHS”


Tweets About Jess Phillips:

“Jess Phillips does not have a political bone in her body (how often have you heard her talk about economic policy, for example?). She is a self-publicist who treats parliament as if it were the set of a reality TV show. If she is the answer, Labour is asking the wrong question.”

“Jess Phillips (Birmingham's 2nd most expensive MP in 2018) threatens to sue someone for bringing up her expenses and the fact that she employed her husband. Good start to the year, Jess. #WestMidlands #Birmingham #ToryTao”

“Interesting aside, since I've championed Jess Phillips I've lost 100 followers - I think this shows you.”

“New from the same people who brought you: Calling Jeremy Corbyn an antisemite is a reprehensible smear Comes: “Jess Phillips Hates Minorities” Coming soon to a CLP near you.”

“Jess Phillips jumps on the bullshit train again.”

Tweets About Laura Kuenssberg:

“Recorded footage of me, Laura Kuenssberg, apparently committing a serious crime live on TV for which there has been no police investigation & no disciplinary action from the BBC.Didn't you know Tories are above the law?”

“I suggest that if you find 38% of votes via the post unexpectedly high you keep sharing this film clip of Laura knowing rather too much...”

“Just received an email from a very prominent member of the Remain community asking me whether if the election goes the wrong way for us" it could be voided on the basis of Laura Kuenssberg's remarks".”

“So BBC Director-General Tony Hall calls for Twitter and Facebook to block people from pointing out the Tory bias of Laura Kuenssberg.Blocking people from countering state propaganda is a fascist response.”

“Voters warned not to take selfies in polling booth as it could break the law.Laura Kuenssberg: Hold my beer.”


Summary:

In the first segment of this report, the focus was on offensive language, deliberately offensive use of words, and the role anonymity plays in encouraging that behaviour. However, pack behaviour and bombardment also play a significant role in online abuse.

The majority of the tweets in this data sample relating to the public figures are from identified and identifiable people with very little anonymity at play. While the language may vary, the meaning does not change a great deal: these are insults and deliberately deployed as such – often times by people with very large audiences and the added amplification which comes with a blue tick. Hundreds of thousands of people see and share messages like these every day with little though, if any at all, given to the person they refer to. In addition, volume is significant. Hundreds of thousands of tweets and retweets relay this content directly to the subject.

It is widely accepted away from life online that abusive behaviour can constitute a slow drip of poison just as it can be sharp infliction of pain, yet this knowledge is being selectively disapplied.

While anonymity itself is relatively simple to deal with as outlined above - alongside more straight forward instances of the use of language which is clearly abusive - there has been little discussion around barrage and this more insidious form of passive-aggressive platform misuse.

In some cases there are relatively simple steps which could be taken, from an expansion of mandatory social media rules for journalists by including their content in regulated frameworks relating to publications, to the compulsory introduction of term limiting by platforms where a person reports they are receiving abuse.

In other cases, however, matters become more complex – in particular where a pattern of harassment is reported to police but where multiple people are posting and reposting content.

There is a need for a more rounded and, frankly, adult discussion about what is acceptable in society now that much of what was once public order related offending takes place in a world which didn’t exist when the legislation was written.

A simple and perhaps overdue amendment would at least start the process of acknowledging the change, by using the Online Harms Bill to amend the definition of public place in the Public Order Act to explicitly include the internet and social media platforms.

Twitter has shown time and again that it is capable of influencing employment, mental health, and elections among other things. Allowing that to continue without some form of state-led intervention is no longer a sensible or responsible proposition.


BRITS OR BOTS?: The following of party leaders during the 2019 general election


This analysis was carried out in the last days of the 2019 General Election campaign.

The captured data covers the three main party leaders with broad audiences across the nations of the United Kingdom.

Analytical processes in bot identification have developed substantially since 2017, as has the nature of inauthentic behaviour itself. Estimates are provided on a basis of confidence, in line with intelligence framework best practice. The method is described within the text.

Leaders:

Jeremy Corbyn

Jo Swinson

Boris Johnson


Jeremy Corbyn:

We carried out some standard searches using Twitter Audit (a reliable first port of call which gives a general assessment of a Twitter account) and Truthnest.

Twitter Audit identified 9% of the @jeremycorbyn following as being suspicious, meaning there are signs of automation and inauthenticity, while Truthnest identified 6.8% of the following as inactive and 0.8% of the following as extremely active, both core indicators of inauthenticity.

You can view the full Truthnest report here.

We pulled dip sampling sets from both the inactive and extreme account groups and ran them through a tried and tested, stable process using Botometer (University of Indiana Social Media Observatory), Bot or Not (an AI tool by shinyapps), and Bot Sentinel to test and verify results. (Failure and accuracy rates for each were established during a 2018 study of Scottish Twitter commissioned by an MEP).

In the sampling process, we identified that the inactive followers contained a distinct segment of fully automated accounts and a much larger segment of accounts which exhibit suspicious behaviour, meaning they are inauthentic. None of the inactive Corbyn followers could be verified as genuine.

https://smiu.live/wp-content/uploads/2019/12/JeremyCorbyn.pdf


Within the extremely active accounts, the number of accounts verified as genuine was much higher, though automated and suspicious accounts were both identified, accounting for just over 20% of the total within this segment.

With the verification process of the dip sample complete, we can provide an estimate of fully automated (bot) and suspicious (human-managed) accounts which form part of the following of the @jeremycorbyn account.

The result sits within the normal ranges of over-identification by fully automated applications and sits within the lower bracket of the unhealthy range of suspicious activity around popular politician accounts across Europe, Australia, and North America.

We have not studied individual content posts or dip sampled non-following accounts for signs of suspicious activity.


Jo Swinson:

Following our analysis of Jeremy Corbyn’s Twitter account, we turned to Jo Swinson and carried out the standard searches using Twitter Audit and Truthnest.

Twitter Audit identified 6% of the @joswinson following as being suspicious, meaning there are signs of automation and inauthenticity, while Truthnest identified 7.9% of the following as inactive and 1% of the following as extremely active, both core indicators of inauthenticity.


We pulled dip sampling sets from both the inactive and extreme account groups and ran them through testing.

In the sampling process, we identified that the inactive followers contained a distinct segment of fully automated accounts and an even larger segment of accounts which exhibit suspicious behaviour, meaning they are inauthentic. None of the inactive Swinson followers could be verified as genuine.

https://smiu.live/wp-content/uploads/2019/12/JoSwinson.pdf


Within the extremely active accounts, the number of accounts verified as genuine was much higher. Suspicious accounts were also identified though the number of automated accounts was significantly higher within this segment.

With the verification process of the dip sample complete, we can provide an estimate of fully automated (bot) and suspicious (human-managed) accounts which form part of the following of the @joswinson account.

The result sits within the normal ranges of over-identification by fully automated applications and sits within the lower bracket of the unhealthy range of suspicious activity around popular politician accounts across Europe, Australia, and North America.



Boris Johnson:

Following our analysis of Jeremy Corbyn and Jo Swinson’s Twitter accounts, we turned to Boris Johnson and carried out the same standard searches using Twitter Audit and Truthnest.

Twitter Audit identified 7% of the @borisjohnson following as being suspicious, meaning there are signs of automation and inauthenticity, while Truthnest identified 14.10% of the following as inactive and 1.40% of the following as extremely active, both core indicators of inauthenticity.


We pulled dip sampling sets from both the inactive and extreme account groups and ran them through testing.

In the sampling process, we identified that the inactive followers contained a large segment of fully automated accounts and an even larger segment (over 50%) of accounts which exhibit suspicious behaviour, meaning they are inauthentic. None of the inactive Johnson followers could be verified as genuine.

https://smiu.live/wp-content/uploads/2019/12/BorisJohnson.pdf


Within the extremely active accounts, the number of accounts verified as genuine was just over 10%. Automated and Suspicious accounts were identified within this segment, accounting for almost 90% of the accounts and evenly split.

With the verification process of the dip sample complete, we can provide an estimate of fully automated (bot) and suspicious (human-managed) accounts which form part of the following of the @borisjohnson account.

The result sits within the normal ranges of over-identification by fully automated apps and sits within the higher bracket of the unhealthy range of suspicious activity around popular politician accounts across Europe, Australia, and North America.



Summary:

All of the party leaders examined are followed by fully automated and suspicious accounts.

All three party leaders fall within the unhealthy range of suspicious activity around their accounts.

The current Prime Minister has a noticeably greater suspicious following than the other two leaders.

While progress has been made by Twitter, suspicious activity around key public figures persists.

It is clear that Twitter as a platform still has work to do dealing with automated accounts, suspicious accounts, and their interaction or presence in the digital vicinity of key public figures during elections.

There is still no process of identity verification in place which addresses the anonymity/accountability issue at the centre of most problems with social media.

In the case of politicians and public figures, in particular during elections where public discourse has been steered by malicious actors using false accounts, it would be prudent to restrict the visibility and capabilities of suspicious and automated accounts.


HAPPY F@#$%*G NEW YEAR: A snapshot of Twitter in the first days of 2020


This snapshot analysis was restricted to the data segment of the 10 days ending on January 9 2020, geographically attributed to United Kingdom users of Twitter. The terms were deliberately restricted to four keywords or phrases which have previously been significant in studies of language used in response to female MPs.

For the avoidance of doubt, these terms offensive.

With the prevalence of more general swear-words and a vast array of colloquialisms and slang in general usage, it is no longer sensible to arrange “Top Ten” lists of terms and this would very often distort understanding. Rather, our analytical process identifies frequency and type of use, peak usage rate, and user gender demographic as a headline.

Each term is then analysed as a timeline, in order to assess whether the usage patterns are human or automated, what topics (where applicable) are co-concurrent with the term and where the term is used geographically.

We then extract the most retweeted and most quoted tweets where the term has featured to give an indication of how the term features in social media discourse.

This snapshot is designed to give an overview of term usage and not assess individual trolling. Twitter has made a number of advances in the way it responds to abuse and targeted harassment since 2017 and the way that online abuse is conducted by perpetrators has also shifted.

Terms:

Term 1: “Retarded”

Term 2: “Rape Her”

Term 3: “Bitch”

Term 4: “Fuck”


Term 1:

In the 10 Day period, the term “retarded” was used in 5,500 tweets attributed to UK Twitter users, of which 33% (1,800) were retweets and 42% (2,300) were replies. This split is different from normal terms of discussion which would feature a much heavier weighting towards retweets and a smaller proportion of replies, indicating that the usage of the term remains a vitriolic response rather than a normalised leader trend. In terms of human behaviour, the lower number of retweets is a recognition signature that the term is offensive and inappropriate.

At peak, in the late evening of January 7, the term reached a maximum usage of 21 tweets per minute, indicating that the usage is by human operators and shows no signs of automation.

The declared gender of those using the term is heavily weighted towards males (78%) versus a much lower proportion of females (22%).

The term is geographically contained to fairly tight areas across the country, mostly urban centres according to given geocodes.

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Total Tweets Retweets Replies

Westminster Manchester Liverpool

Glasgow Birmingham Leeds

Edinburgh Oxfordshire West Sussex

Nottingham Cardiff Islington

Newcastle upon Tyne


Co-concurrent hashtag use (those written alongside the term) is fairly constrained, with the additional signifiers only appearing in small portion of the tweets. The absence of hashtag usage indicates that there is no leader trend or concerted effort to amplify this term.

The term appeared alongside World War 3, Bitcoin, and an apparent misspelling of the Oscars tag.

Co-concurrent word analysis (words used alongside the term) is also fairly constrained, with additional signifiers repeating in a small proportion of tweets.

From this we can identify a usage pattern which clearly shows the term is knowingly used in a derogatory fashion in combination with the terms “mentally”, “Trump”, and “fashionistas.” In general, the usage of the term indicates a lack of restraint on behalf of the users.

0.00% 0.10% 0.20% 0.30% 0.40% 0.50% 0.60% 0.70%

#wwiii

#wwlll

#goldengiobes

#bitcoin


Generally, the timeline of tweets shows human rather than automated usage of this term, with peaks and troughs following human waking and sleeping patterns. The peak use of the term came in the evening of January 7.

When viewing specific content, we focus on what is achieving social amplification – namely, what is being shared. On Twitter this is broken into two sharing options: quoted and retweeted.

The most repeated text in the tweets appears to be an auto-generated card share of a Daily Mail online article which produces the headline as the text: “Iran's president accuses Trump of being 'mentally retarded'.” Many of the responses to the tweets (and the tweets themselves) appear to be people who are anti-Trump agreeing with the sentiment the headline relays.

Much of the rest of the content is childish and puerile, however one of the retweeted posts from journalist Louise Milligan highlights an incident in which an Australian politician provoked a misogynistic Facebook pile-on against a UK television presenter.

Usage: The term does not appear as frequently as others but is used deliberately and is deployed by humans.

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Term 2:

In the 10 Day period, the term “rape her” was used in 38,500 tweets attributed to UK Twitter users, of which 92% (35,500) were retweets and 4% (1,500) were replies. This split is in line with normal terms of discussion which feature a much heavier weighting towards retweets and a smaller proportion of replies, indicating the term forms part of an ongoing event discussion. In terms of human behaviour, the higher number of retweets could be indicative of automation without other contributing explanation.

At peak, in the afternoon of December 30, the term reached a maximum usage of 97 tweets per minute, indicating that the usage is by human operators discussing a live news topic.

The declared gender of those using the term is weighted towards females (60%) versus a lower proportion of males (40%). The term is in geographically widespread use, with a majority of traffic geocoded to London.

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Westminster Manchester

Birmingham Glasgow

Liverpool Islington

Oxfordshire Bristol

Portsmouth North Lanarkshire

Nottingham Newcastle upon Tyne

Enfield


Co-concurrent hashtag use (those written alongside the term) is barely existent, with less than half a percent of tweets featuring the term combined with the World War Three tag.

Co-concurrent word analysis (words used alongside the term) is much broader, with additional signifiers repeating in a proportion of tweets and identifying the topic under discussion being the British teenager convicted of making a false allegation of rape in Cyprus. With this context, we can comfortably assess the topic is human-led news response rather than automated amplification.

The timeline of tweets shows clear human rather than automated usage of this term, with peaks and troughs following news events and distinct sleeping patterns.

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Almost all of the quoted and shared content specifically relates to the Cyprus case and is in support of the victim.

There is very little outlier content. However, one influencer picked up on the term as it began to trend and used a deeply disturbing tweet thread as an advert for services as a brand influencer:

Usage: The term occurred with some frequency in response to news events and has mostly been used in what should be deemed an acceptable context, with a limited number of exceptions.


Term 3:

In the 10 Day period, the term “bitch” was used in 234,000 tweets attributed to UK Twitter users, of which 70% (164,500) were retweets and 13% (29,300) were replies. This split is in line with normal terms of discussion which feature a normal weighting towards retweets and a smaller proportion of replies, indicating the term forms part of general conversation. In terms of human behaviour, these proportions are in the normal human range with no indication of automation.

At peak, in the late evening of January 6, the term reached a maximum usage of 91 tweets per minute, indicating that the usage is by human operators and shows no signs of automation.

The declared gender of those using the term is weighted towards females (54%) versus a slightly lower proportion of females (46%).

The term is in geographically widespread use with increased repetition in mostly urban areas, according to given geocodes.

234000

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29300

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Birmingham Glasgow

Liverpool Leeds

Newcastle upon Tyne Bromley

Edinburgh Nottingham

Bristol Oxfordshire


Co-concurrent hashtag use (those written alongside the term) is fairly constrained, with the additional signifiers only appearing in small portion of the tweets. The pronounced hashtags are identifiable as related to the sex industry.

Co-concurrent word analysis (words used alongside the term) is also fairly constrained, with additional signifiers repeating in a small proportion of tweets and once again relating to sex industry topics.

From this we can identify a usage pattern which clearly shows that the term is knowingly used in general language as an expletive or, in some cases, in a specialist sex industry context.

It is also used during arguments and when referring to people identified as racist.

0.00% 0.10% 0.20% 0.30% 0.40% 0.50% 0.60% 0.70%

#findom

#femdom

#mustwatch

#paypig

#find


Generally, the timeline of tweets shows human rather than automated usage of this term, with peaks and troughs following human waking and sleeping patterns. The peak use of the term came on January 2. As with most sex industry-linked topics, there is notably no fall to zero during the night due to when many of these conversations take place.


The use of the term is mostly random and in context, although the content is largely low quality.

Usage: The term appears frequently in daily use, is used deliberately, and is deployed by humans.

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Term 4:

In the 10 Day period, the term “fuck” was used in 1,071,200 tweets attributed to UK Twitter users, of which 54% (578,300) were retweets and 21% (222,500) were replies. This split is in line with normal terms of reply-centric discussion with a lower than normal weighting towards retweets and a larger than normal proportion of replies, indicating the term is a general use word. In terms of human behaviour, these proportions are in the normal range.

At peak, at lunchtime of January 1, the term reached a maximum usage of 480 tweets per minute, indicating that the usage is human but in the frequent word use category.

The declared gender of those using the term is heavily weighted towards males (69%) versus a much lower proportion of females (31%).

The term is in geographically viral, with a higher density of repetition geocoded to London.

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Glasgow Liverpool

Birmingham Leeds

Newcastle upon Tyne Edinburgh

Bristol Sheffield

Nottingham West Sussex


Co-concurrent hashtag use (those written alongside the term) is much broader than any of the other terms, with a broad spread of appearances across multiple trending topics.

Co-concurrent word analysis (words used alongside the term) is fairly constrained, with no real signifiers repeating. This indicates the term is simply used as any other word.

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#goldenglobes

#celebrityrefs

#afterlife2

#supernature

#wwiii

#australiaburning

#sex

#horny

#gay

#soutot

#bareback

#findom

#mufc


Generally, the timeline of tweets shows human rather than automated usage of this term, with peaks and troughs following human waking and sleeping patterns. The peak use of the term came on January 1. As with most common use words, the appearance of the term never falls away entirely.


The use of the term is mostly random, as you would expect from a general use word. There are however some notable tweets, including this from an influencer who decided to raise money for the Australian disaster relief projects by sending nude pictures of herself to donors.

Usage: The term is used as an everyday word.

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Summary:

For the purposes of this analysis we created a data segment of 1.34 million tweets over a ten day period, of which 58% (780,100) were retweets and 19% (255,600) were replies.

The more offensive the term and the more deliberately offensive the deployment of the term, the more constrained the statistical repetition and the geographical spread of the users becomes.

In the case of all of the terms analysed there is little evidence of automation and the usage is human-led.

There are signs that the use of terms has shifted significantly from the landscape position of 2017, however this would need to monitored over the medium to long term to establish if permanent change exists.

It is clear that Twitter as a platform still has work to do dealing with the automatic prevention of certain terms being posted. However, with articles using those terms in headlines used to create to pre-written tweets, there is a clear need for guidance or regulation from external sources. There is also the need to consider freedom of expression within this course of action.

Little has otherwise changed: there is still no process of identity verification in place which addresses the anonymity/accountability issue at the centre of most problems with social media.

As daily language continues to shift, with certain words becoming more and more acceptable and less offensive per se, the use of genuinely offensive terms - and online behaviour itself - actually gets easier to identify, codify, and regulate by consensus. Subsequently, the start of 2020 presents a good opportunity to do this and clear patterns or indicators in the usage of terms make the task much easier than it would have been even twelve months ago.

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CONCLUSION: Securing a better future online

Offensive language and its deliberate use is still a visible problem on social media and, notably, is still the reserve of real people – often hiding behind the digital mask online anonymity provides. Much general academic evidence of the role anonymity plays in disinhibition already exists and this report looked at a snapshot of UK twitter, seeking to test whether these views were supported by evidence of behaviour online. Our view is that the research is supported by what we found.

Our public figures, from politicians to journalists and beyond, are prime targets for online abuse, whether it be overt or insidious, and the gravity well of sharing continues to amplify the problem in the absence of state-led intervention following a failure by the platforms to effectively curb excesses. The effect of this is detrimental to society as a whole, altering public perception of what behaviour is acceptable and reinforcing the imaginary dividing line between online action and real-world consequence.

Findings:

It is clear that platforms have made strides in dealing with some of the issues which have been under the microscope for the last few years. Twitter, for example, has made progress in the identification and removal of bots, while also introducing new layers to its abuse reporting functions. Facebook has also recently announced that it is recruiting another 1,000 UK-based staff to reduce online harms. However, there is still a significant over-reliance on technology being the answer to technology’s problem and despite best efforts to date, it is still possible to create fake accounts - whether automated or human-managed – and content limitation or filtering has not solved the abuse issue.

It is clear that self-moderation is closely tied to accountability which, in turn, comes from identifiability.

1. Abuse directed at political figures appears to come disproportionately from accounts which obfuscate their real-world identity.

2. Lower level abuse and name-calling is almost evenly distributed across identifiable and anonymous accounts and comes from both sides of the Brexit divide, though some is defensive aggression.

3. The most commonly used offensive terms are widely deployed by a broad range of users in common parlance, but use of such terms in a deliberately offensive way is more likely to occur in the case of anonymous users.

4. There was a moderate level of automated and suspicious Twitter account activity identified during the 2019 General Election. Of the party leaders, Boris Johnson appears to have had largest network attraction to such accounts.

In addition, we believe that some abusive behaviour – while not anonymous – can be attributed to the reality detachment effect social media is having on society at large. To date the reconciliation of our online selves and our physical selves has not fully cemented and, subsequently, the boundaries of what is acceptable behaviour have diverged and may continue to grow further apart without intervention.


Recommendations:

There are a number of options available, some of which are complex and others which are more simplistic and easier to bring into place.

This report makes three key recommendations which should ideally be dealt with during the passage of the Online Harms Bill.

1. Parliament should pave the way for Digital Identification, providing accountability while facilitating the preservation of outward anonymity. This need be no more complex that the provision of a Gateway style ID which can only be obtained through the provision of identification and which facilitates a “UK Verified” status on social media platforms. The platforms can easily support this and, in addition, should provide a quality filter which allows non-verified users to effectively be subject to a damp down in the conversation space. This would not only reduce the visibility and normalisation of all types of abusive behaviour, but would render more perpetrators more readily traceable by authorities. In addition, this would add further protective benefit around key events which are open to influence, such as elections. There would be little or no impact of freedoms by way of this approach, in particular given that social media platforms already provide conditional access in any case.

2. Parliament should expand the current regulatory frameworks to capture the official accounts of journalists under the duties which occur for them and their publishers under other circumstances. Official capacity rules are already well-embedded in public services where social media is concerned and the BBC has already bolstered its own guidelines. The distinction is an important one for the public who have the right to expect that its writers and broadcasters are providing a high quality of service, impartially, at all times.

3. Parliament should use the Online Harms Bill to refresh Public Order legislation, formalising the recognition of the digital world as a public place. This simple measure will produce an immediate (and currently absent) reality attachment and help clarify the personal responsibilities each person has to moderate their behaviour in line with the expected norms of society.

These measures are purposefully designed to address specific problems and provide a rapid route to

securing a better online future.

Ends.


OUR DIGITAL MASKS: How Britain behaves online, 2020

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OUR DIGITAL MASKS - Socint

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