Truth in an Age of Information
Alan Dix
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.
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
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
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
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
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
As we saw in the last example, those we regard as
‘good’ actors can also sometimes spread bad
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
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)
3.1 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
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
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
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
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
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
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 ( and
Instapaper (, 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
( 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).
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
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data and materials (Wacharamanotham, 2020). As
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academia, it is also an opportunity to use our own
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CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications