Truth in an Age of Information

Alan Dix

1,2

Professorial Fellow, Cardiff Metropolitan University, Wales U.K.

Director of the Computational Foundry, Swansea University, Wales, U.K.

Keywords: Misinformation, Argumentation, Provenance, Qualitative–quantitaive Reasoning, Fake News, Echo Chambers.

Abstract: Many of the issues in the modern world are complex and multifaceted: migration, banking, not to mention

climate change and Covid. Furthermore, social-media, which at first seemed to offer more reliable 'on the

ground' citizen journalism, has instead become a seedbed of dis-information. Trust in media has plummeted,

just when it has become essential. This is a problem, but also an opportunity for research in HCI that can

make a real difference in the world. The majority of work in this area, from various disciplines including data-

science, AI and HCI, is focused on combatting misinformation – fighting back against bad actors. However,

we should also think about doing better – helping good actors to curate, disseminate and comprehend

information better. There is exciting work in this area, but much still to do.

1 INTRODUCTION

Falsehood flies, and truth comes limping after it.

Jonathan Swift, The Examiner No. 14,

Thursday, 9th November 1710

Politicians have always been ‘economical with the

truth’ and newspapers have toed an editorial line.

However, never in recent times does it seem that

confidence in our media has been lower. From the

Brexit battle bus in the UK to suspected Russian

meddling in US elections, fake news to alternative

facts – it seems impossible for the general public to

make sense of the contradictory arguments and

suspect evidence presented both in social media and

traditional channels. Even seasoned journalists and

editors seem unable to keep up with the pace and

complexity of news.

These problems were highlighted during Covid

when understanding of complex epidemiological data

was essential for effective government policy and

individual responses. As well as the difficulty of

media (and often government) in understanding and

communicating the complexity of the situation,

various forms of misinformation caused confusion.

There are obvious health impacts of this

misinformation due to taking dangerous ‘cures’

(Nelson, 2020) and vaccination hesitancy (Lee,

2022a), as well as its role in encouraging violence

against health workers (Mahase, 2022). In addition, a

meta-review of many studies of Covid

misinformation identified mental health impacts as

also significant (Rocha, 2021).

If democracy is to survive and nations coordinate

to address global crises, we desperately need tools

and methods to help ordinary people make sense of

the extraordinary events around them: to sift fact from

surmise, lies from mistakes, and reason from rhetoric.

Similarly, journalists need the means to help them

keep track of the surfeit of data and information so

that the stories they tell us are rooted in solid

evidence.

Crucially in increasingly politically fragmented

societies, we need to help citizens explore their

conflicts and disagreements, not so that they will

necessarily agree, but so that they can more clearly

understand their differences.

These are not easy problems and do not admit trite

solutions. However, there is existing work that offers

hope: tracking the provenance of press images (ICP,

2016), ways to expose the arguments in political

debate (Carneiro, 2019), even using betting odds to

track the influence of news on electoral opinion

(Wall, 2017).

I hope that this paper will give hope that we can

make a difference and offer challenges for future

research.

Dix, A.

Truth in an Age of Information.

DOI: 10.5220/0011632000003323

In Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2022), pages 7-14

ISBN: 978-989-758-609-5; ISSN: 2184-3244

2 THE B-MOVIE CAST OF

MISINFORMATION

Deliberate misinformation is perhaps the most

obvious problem we face. There is extensive data

science studies by academics and data journalists

attempting to understand the extent and modes of

spread (e.g. Albright, 2016; Vosoughi, 2018).

Crucially false information appears to spread more

rapidly than true information; possibly because it is

more novel (Vosoughi, 2018). Although there is

considerable debate as to the sufficiency of their

responses, both Facebook and Twitter are constantly

adjusting algorithms and policies to attempt to

prevent or discourage fake news (Dreyfuss, 2019;

NPR, 2022; Twitter, 2022). Within the HCI

community there has been considerable work

exploring the human aspects around the spread of

misinformation online (Flintham, 2018, Geeng, 2020;

Varanasi, 2022), ways to visualise it (Lee, 2022b),

tools for end-users to help identify it (Heuer, 2022)

and CHI workshops (Gamage, 2022; Piccolo, 2021).

2.1 Bad Actors

Much of the focus on misinformation is on ‘bad

actors’: extremist organisations, ‘foreign’ powers

interfering in elections, or simply those aiming to

make a fast buck. In the context of mis-information,

‘bad’ can mean two things:

1. They are intrinsically bad people, bad states, or

bad media.

2. They use bad methods and/or spread bad

information (including misinformation and

hateful or violent content).

The first of these can be relative to clear criteria

such as human rights or terrorism, but may simply

mean those we disagree with; and, of course, the

boundary between the two may often be unclear.

When the two forms of ‘bad’ agree the moral

imperative is clear, even though implementation may

be harder. Forced in part by government and popular

pressure, social media platforms have extensive

mechanisms both to attempt to suppress bad

information and suspend accounts of those who

promulgate it (Guardian, 2018).

Probably the most high-profile example of the

later was Twitter’s suspension of

@realDonaldTrump. This was both met with

widespread relief, but also caution due to its potential

impact on free speech (Noor, 2021), especially given

Twitter’s arguments for why it was suspended when

it was (Twitter, 2021).

Of course, sometimes bad actors may spread true

(or even good) information.

In some cases this is simply because few are

altogether bad. For example, those who believe and

then promulgate Covid conspiracy theories; many

will be well meaning, albeit deeply misguided, and

some of the information may be accurate.

However, true information can also be cynically

used to give credence to otherwise weak or

misleading arguments; for example a recent study of

cross-platform misinformation (Micallef, 2022)

found a substantial proportion of cases where a

YouTube video with true information about Covid

was referenced by a tweet or post that in some way

mis-interpreted the material or used it out of context.

In addition, many Astroturfing accounts will

distribute accurate information as a means to create

trust before disseminating misinformation. It can be

hard to distinguish these and it is not uncommon for

politicians or other campaign groups to inadvertently

re-tweet or quote true or at least defensible

information that originated from very unsavoury

groups, thus giving them credence.

2.2 When Good Actors Spread Bad

Information

As we saw in the last example, those we regard as

‘good’ actors can also sometimes spread bad

information.

Sometimes this is deliberate. An extreme case is

during war when misinformation campaigns in an

enemy country are regarded as a normal and indeed

relatively benign form of warfare (Shaer, 2017). In

peace time deliberate misinformation is likely to be

less extreme and more often stretching or

embroidering the truth, or selectively reporting.

It may also be accidental. For example, Figure 1

shows a “Q&A” (form of fact check) on the BBC

news web site following a claim made by Boris

Johnson in January 2018 regarding UK contributions

to the EU budget. The overall thrust of the Q&A is

correct, the net amount that was sent to the EU at that

time was substantially less than the £350 million

figure that Johnson claimed, but the actual figures are

wrong, the Q&A suggested that around 2/3 of the

gross figure was returned, when the actual figure was

close to a half. This is probably because at some point

a journalist lost track of which figure the half was

referring to, but the overall effect was to create a

substantially incorrect figure.

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

Figure 1: Q&A on BBC new web site January 2018 (BBC,

2018). Note this Q&A pop-up is no longer in the news item;

instead there a link to a ‘Realty Check’ page which is

correct, but with no explicit retraction.

In between are the subtle biases are simply

assumptions of journalists that play out in the

selection of which stories to report and also in the

language used. For example, in crime or conflict

reporting passive language may be used (“the

assailant was shot”, or “shells fell on”) compared with

active language (“AAA shot BBB” or “XXX fired

shells on”) depending on which side is doing the

shooting or bombing.

Personally, while I may despair or be angry at the

misinformation from those with whom I disagree, I

am most upset when I see poor arguments from those

with whom I agree. This is partly pride, wanting to

be able to maintain a moral high ground, and partly

pragmatic, if the arguments are poor then they can be

refuted.

In an age of adversarial media, any mistakes,

misrepresentation or hyperbole can be used to

discredit otherwise well-meaning sources and

promote alternatives that are either ill-informed or

malicious. This was evident in the US during the

2016 presidential campaign when many moderate

Republican supporters lost faith in the reputable

national press in favour of highly partisan local

papers; a trend which has intensified since (Gottfried,

2021; Meek, 2021)

3 SEEKING TRUTH

3.1 The Full Cast

We have already considered the ‘B-movie’ bad/good

guy roles, of the producers and influencers, both of

whom can mislead whether ill-intentioned or ill-

informed. In reality even the ‘bad’ actors may be

those with genuinely held, albeit unfounded, beliefs

about 5G masts or communist take-over of US

government. Of course, those of us who would

consider ourselves ‘good’ actors, may still distort or

be selective in what we say albeit for the best of

reasons.

In addition, those who receive misinformation

and are confused or misled by it may differ in levels

of culpability. It is easier to believe the things that

make life easier, whether it is the student grasping at

suggestions that the impact of Covid may be over

exaggerated in order to justify a party, or the

professional accepting climate change scepticism to

justify buying that new fuel-hungry car.

Of course, the purveyors of news and information

are under pressure, and may not be wholly free in

what they say, or may run risks if they do. Even in

the last year we have seen many journalists, bloggers

and authors arrested, sanctioned, stabbed and shot.

Perhaps more subtle is the interplay within the

ecology of information: journalists and social media

modify what and how they present information in

order to match the perceived opinions and abilities of

their readership.

3.2 Two Paths

The greatest effort currently appears to be focused on

fighting back against bad actors. This includes

algorithms to detect and counter mis-information,

such as Facebook’s intentions to weed out ani-

vaccination. These are predominantly aimed at the

bad actors.

However, in addition we need to think about

doing better, ways for the good actors to disseminate

and understand information so that they are in a better

position to evaluate sources of information and ensure

that they do not inadvertently create bad information.

We’ll look briefly at four areas where appropriate

design could help us to do better:

• echo chambers and filter bubbles

• better argumentation

• data and provenance

• numeric data and qualitative–quantitaive

reasoning

These are not the only approaches, but I hope they

will stimulate the reader to think of more.

3.3 Echo Chambers and Breaking

Filter Bubbles

Social media was initially seen as a way to

democratise news and information sharing and to

allow those in the ‘long-tail’ of small interest groups

to find like-minded people in the global internet.

However, we now all realise that an outcome of this

has been the creation of echo chambers, where we

increasingly only hear views that agree with our own.

Truth in an Age of Information

In some ways this has always been the case, both in

choices of friendship groups for informal

communication and the audiences of different

newspapers. However, social media and the

personalisation of digital media has both intensified

the effect and made it less obvious – you know that a

newspaper has a particular editorial line, but do not

necessarily recognize that web search results have

been tuned to your existing prejudice.

This is now a well-studied area with extensive

work analysing social media to detect filter bubbles

an understand the patterns of communication and

networks that give rise to them (Terren, 2021,

Garimella, 2018; Cinelli, 2021). Notably, one of

these studies (Garimella, 2018) highlighted the role

of ‘gatekeeper’, people who consume a broad range

of content, but then select from this to create partisan

streams. Perhaps more sadly, the same study notes

that those who try to break down partisan barriers pay

a “price of bipartisanship” in that balanced

approaches or multiple viewpoints are not generally

appreciated by their audiences.

In addition, there has been work on designing

systems that in different ways attempt to help people

see beyond their own filter bubbles (e.g. Foth, 2016;

Jeon, 2021), but on the whole this has been less

successful, especially in actual deployment. Indeed,

attempts to present opposite arguments can end up

deepening divides if they are too different and too

soon.

3.4 Argumentation

It is easy to see the flaws in arguments with which we

disagree, we know it is wrong and can thus hunt for

the faults – the places where our intuitions and the

argument disagree are precisely the places where we

are expecting holes in the reasoning. Of course, we

all create bad arguments. It is very hard to notice the

gaps in one’s own reasoning, but also the fallacious

arguments of others when one agrees with their final

conclusions.

Of course, those who disagree with us will notice

the gaps in our arguments, thus increasing their own

confidence and leading them to discount our

opinions!

It is crucial therefore to have tools that both help

the public to interrogate the arguments of politicians

and influencers, and also to help those who are aiming

to create solid evidence-based work (including

academics) to ensure valid arguments.

There is of course long-standing work on

argumentation systems, such as IBIS (Noble, 1988)

and work in the NLP community to automatically

analyse arguments. Much of this is targeted towards

more professional audiences, but there are also steps

to help the general public engage with media, such as

the Deb8 system (Carneiro, 2019) developed at St

Andrews, an accessible argumentation system that

allows viewers of a speech or debate to

collaboratively link assertions in the video to

evidence from the web.

This is an area which seems to have many

opportunities for research and practical systems

aimed at different audiences including the general

public, journalists, politicians, academics, and fact

checkers. This could include broad advice, for

example, ensuring that fact checkers clearly state

their interpretation of a statement before checking it

to avoid inadvertently debunking a strawman

misinterpretation. Similarly, we could imagine

templates for arguments, for example, given an

implication of the form “if A then B”, it is important

to keep track of the assumptions. In particular, while

more formal logics and some forms of argumentation

schemes focus on low-level argumentation, it seems

that the tools needed perhaps need to focus on the

higher-level argumentation, the information and

assumptions that underly a statement, more than the

precise logic of the inference.

In addition, in the AI community there are now a

variety of tools to help automatically detect possible

bias in data or machine learning algorithms. Maybe

some of these could be borrowed to help human

reasoning, for example shuffling aspects of situations

(e.g. gender, political party or ethnicity), to help us

assess to what extent our view is shaped by these

factors.

3.5 Data and Provenance

One of the forms of misinformation is the deliberate

or accidental use of true information or accurate data

divorced from its context. For the spoken word or

text, this might be a quotation, for photographs or

video the choice of a still, segment or even parts

edited together that give a misleading impression.

Indeed the potential for digital media to be

compromised in different ways lead some to look for

technology such as blockchains to prevent tampering,

or the use of analogue or physical representations

(Haliburton, 2021).

One example of work addressing this issue was

the FourCorners project (ICP, 2016), a collaboration

between OpenLab Newcastle, the International

Centre for Photography and the World Press Photo

Foundation, which embeds provenance into

photographs allowing interrogation such as "what are

CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications

the frames before and after this photograph?", "are

there other photos at the same time and place?". One

can imagine similar things for textual quotes, in the

manner of Ted Nelson’s vision of transclusion

(Nelson, 1981), where segments quoted from one

document retain their connection back to the original.

This is an area I’ve worked on personally in the

past with the Snip!t system, originally developed in

2003 following a study of user bookmarking practice

(Dix, 2003). Snip!t allowed users to ‘bookmark’

portions of a web page and automatically kept track

not just of the quoted text, but where it came from

(Dix, 2010). Later work in this area by others has

included both commercial systems such as Evernote,

and academic research, such as Information Scraps

(Bernstein, 2008). Currently there is an explosion of

personal knowledge management (PKM) apps, some

of which, such as Readwise (readwise.io) and

Instapaper (instapaper.com), help with the process of

annotating documents. However, these systems are

mostly focused on retaining the context of captured

notes and quotes; we desperately need better ways to

retain this once the quote is embedded in another

document or web page.

This connection to sources is also important for

data. In the example from the BBC in Figure 1, the

journalist had clearly lost track of the original data on

UK/EU funding and so misremembered aspects.

Can we imagine tools for journalists that would

help them keep track of the sources for data and

images. Indeed, it would be transformative if

everyday office tools such as word processors and

presentation software made it easy to keep references

to imported images. In work with humanities and

heritage, we have noted how file systems have barely

altered since the 1970s (Dix, 2022) – the folder

structures allow us to store and roughly classify, but

there is virtually no support for talking about

documents and about their relationships to one

another. Semantic desktop research (Sauermann,

2005), which seemed promising at the time, has never

found its way into actual operating systems.

Happily there are projects, such as Data Stories

(2022) that are helping communities to use data to tell

their own stories, so that the online world can allow

open discourse and interpretation, whilst connecting

to the underlying data on which it is based.

Furthermore, one of the popular PKM apps Obsidian

(obsidian.md) supports semi-structured meta-data for

every note.

3.6 Numeric Data and

Qualitative–quantitaive Reasoning

Going back to the example in figure 1, part of the

problem here may well simply be that journalists are

often more adept with words than numbers. We are

in a world where data and numerical arguments are

critical. This was true of Covid where the

understanding of exponential growth and

probabilistic behaviour was crucial, but equally so for

issues such as climate change.

One of the arguments put forward by climate

change sceptics, is that it is hard to believe in long-

term climate models given forecasters sometimes

struggle to predict whether it is going to rain next

week. This, at first sight, is not an unreasonable

argument; although anyone who has deal with

stochastic phenomena knows that it is often easier to

predict long-term trends than short-term behaviour.

Indeed, it is also relatively easy to communicate this

– we can all say with a degree of reliability that a

British winter will be wetter and colder than the

summer, even though we’ll struggle to know the

weather from day to day.

This form of argument is not about exact

numerical calculation, nor about abstract

mathematics, but something else – informal reasoning

about numerical phenomena. Elsewhere I’ve called

this qualitative–quantitative reasoning (Dix, 2021a,

2021b) and seems to be a critical, but largely missing,

aspect for universal education. Again this is an area

that is open for radical contributions, for example,

iVolver (Nacenta, 2017) allows users to extract

numerical and other data from visualisations, such as

pie charts, in published media. My own work has

included producing table recognisers in commercial

intelligent internet system OnCue in the dot-com

years (Dix, 2000) and more recently investigating

ways to leverage some of the accessibility of

spreadsheet-like interfaces and simple ways to allow

users to combine their own data (Dix, 2016).

4 CALL TO ACTION

We are at a crucial time in a world where information

is everywhere and yet we can struggle to see the truth

amongst the poorly sourced, weakly argued,

deliberately manipulated or simply irrelevant.

However, there are clear signs of hope in work that is

being done and also opportunities for research that

can make a real difference.

Of course, as academics we are also in the midst

of a flood of scholarly publication, some more

Truth in an Age of Information

scholarly than others! There are calls for us to ‘clean

up our own act’ too including rigour of academic

argumentation (Basbøll, 2018) and transparency of

data and materials (Wacharamanotham, 2020). As

well as being a problem we need to deal with within

academia, it is also an opportunity to use our own

academic community as a testbed for tools and

techniques that could be used more widely.

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