Keynote delivered at ACM Hypertext conference on 6th of September 2023. Abstract: You’re probably getting a bit worn out from all these talks about misinformation and Twitter-based experiments. The fact that Twitter is now called Platform X is probably not enough of a change to keep you awake during my talk! But I think, or hope, to bring up a few things in this talk that you might not have come across or thought about much before. I believe that having fact-checks that call out false or misleading claims is very important in our fight against misinformation. But we’re still not quite sure if and how they impact the spread of wrong information and how they could help set things right online. So, in this talk, I’ll dive into how we’re all prone to falling for misinformation and make a case for needing data and tools to help us see how both ourselves and others engage with false or unreliable information over long periods of time. I’ll also share what we’ve learnt from our research about how these fact-checks affect how wrong info spreads, and I’ll give you the scoop on what happened when we tried using automatic replies to correct misinforming posts on Twitter, oops, I mean platform X. If all of this still feels like old news to you, well, there’s always that email inbox to keep you awake during my keynote.

1. Intro Slide Title 1
Misinformation vs
Fact-Checks
The Ongoing Battle
Harith Alani
Knowledge Media Institute
6 Sept 2023 – ACM Hypertext, Rome
@halani

2. 2
Gregoire Burel Martino Mensio Tracie Farrell Miriam Fernandez
Lara Piccolo Ali Tavakoli
Contributors include:

3. Tracking social interactions at Hypertext 2009

4. 4

5. 5
Take Home Messages
Acknowledge the susceptibility of
everyone to misinformation
Need for tools to assess our, and
other’s information reliability
Promoting accurate information and
accounts is as important as demoting
inaccurate ones
Release of fact-checks impacts the
spread of misinformation
Account for the influence of
misinformation interventions on
bystanders

7. Giuseppe Garibaldi, Rome
Vittorio Emanuele,
Rome
Carlo Alberto,
Rome
Giuseppe Garibaldi,
La Spezia
Which one died in battle?
NONE

8. 8
“They don’t. The persistence of error is well
illustrated in this myth, which goes back a couple
of thousand years or so. No matter how many
biologists and zoologists continue to deny the
truth of this belief, it's still with us.”
Ostriches [don’t] bury
their heads in the sand

9. Which claims resist corrections?
9

10. 10
The psychological drivers of misinformation belief and its resistance to correction
Ullrich K. H. Ecker, Stephan Lewandowsky, John Cook, Philipp Schmid, Lisa K. Fazio, Nadia Brashier, Panayiota
Kendeou, Emily K. Vraga & Michelle A. Amazeen. Nature Reviews Psychology volume 1, (2022)

11. Fake War
MISINFORMATION
No ‘one size fits all’

12. “Errare humanum est, sed in
errare perseverare
diabolicum.”
“To err is human, but to persist
in error is diabolical.”
Seneca, 4 BC – AD 65

13. 13
Spread of Misinformation vs their fact-checks
Spread of Misinformation
and their fact-checks

14. Who fact-checks the
fact-checkers?
International Fact-Checking Network (IFCN)
https://ifcncodeofprinciples.poynter.org/signatories

15. …
"claimReviewed": "Elon Musk allowed Donald Trump to
rejoin Twitter on April 15, 2022",
…
"datePublished": "2022-04-14",
… "url": "https://www....",
"reviewRating” … "False"

16. Claims + their Fact-Checks
misinformation Fact-check
misinfo URL factcheck URL

17. 17
MisinfoKG
140 thousand claims and corresponding
fact-checks
From 70 fact-checkers from 32 countries.
Spans 23 languages
Over 30K semantic entities in 1.7 million
RDF triples
Daily automatic updates
A Knowledge Graph of ClaimReview data
0 5 10 15
Amount of fact−checkers
10000 20000 30000
Amount of fact−checks created by countries

18. 18
MisinfoKG Search
https://explorer.cimple.eu/
Github: https://purl.org/net/misinfo-kg/claimreview-data
SPARQL https://cimple-kg.tools.eurecom.fr/fct

19. 19
Do COVID-19 misinformation and fact-
checks spread similarly?
How do these spread patterns differ
with topics, demographics, and time?
Does sharing fact-checks affect the
diffusion of misinformation?
Co-Spread of
Misinformation
and corresponding
Fact-Checks

20. 20
7,370 Misinforming URLs
9,151 Fact-checking URLs
Data collected
December 2019 to
January 2021
Poynter
~360K Tweets
misinfo URL factcheck URL
t
o
p
i
c
Tweets with
misinfo URL
Tweets with
factcheck URL
{Organisation,
Individual}
T
r
u
e
False
M
i
x
e
d
T
y
p
e

21. 21
Relative analysis:
Data is aligned
based on their
initial sharing date
Analysis Levels
0 – 3 days
4 – 10 days
10+ days
initial
early
late
Compare spread of Misinformation
with spread of Fact-checks in
different time periods
• Non-parametric MANOVA/ANOVA (Analysis of
Variance)
Relation analysis between the spread
of Misinformation and their Fact-
Checks
• Can we predict the spread of one from the
other? (causation analysis)
• Do changes in spread of one impact the spread
of the another? (Impulse response analysis)

22. 22
Annotation Classes
- Origins → Where COVID-19 emerged and how
- Transmission → How COVID-19 spreads
- Prevention and Cures → How to prevent or
cure COVID-19
- Vaccine → COVID-19 vaccines.
- Conspiracy → COVID-19 conspiracies
- Government and Authorities → How
governments and authorities responded
- People and Organisations → What people or
organisations said
https://www.poynter.org/coronavirusfactsalliance/

23. 23
Misinformation is shared far
more than fact-checks
(~3:1)
Significant differences in
how misinformation and
fact-checks spread
globally

24. Different types of misinformation spread differently
As the time periods increase, spreading behaviour converge.
Misinformation and fact-checks about Covid-19 Causes and Conspiracy theories
continue to spread differently in the late period

25. 25
Initial fact-checking
response with decreasing
trend.
No clear misinformation
spread trend
Misinformation spread can be
predicted from fact-checking spread
and fact-checking spread can be
predicted from misinformation spread
Misinformation impulse generates an
initial fact-checking uptake.
Release of fact-checks have a strong
short-term impact on misinformation
reduction
Burel, G.; Farrell, T.; Alani, H. (2021). Demographics and topics impact on the co-spread of COVID-19
misinformation and fact-checks on Twitter. Information Processing & Management, 58(6).

26. “we can be blind to the obvious
and we are also blind to our
blindness”
Daniel Kahneman, psychologist
2002 Nobel Prize winner in Economics

27. Longitudinal
Credibility
Measurement
• Most works are focused on measuring the credibility of:
• Content
• Source
• Measuring the overall credibility of a social media
account is not well addressed

28. MisinfoMe
https://misinfo.me/

29. MisinfoMe
https://misinfo.me/
Reliability of
Content and Source
Provides a credibility
score for Tweets with
links
Points to related fact-
checks or source-
assessments

30. 30
Mensio, M.; Burel, G.; Farrell, T.; Alani, H. (2023). MisinfoMe: A Tool for Longitudinal Assessment of Twitter Accounts’ Sharing of
Misinformation. ACM Conf. on User Modeling, Adaptation and Personalization, ACM pp. 72–75.

31. 31
“Most of the studied interventions
were not implemented and tested
in a real social media environment
but under strictly controlled
settings or online crowdsourcing
platforms”
Gwiaździński P., Gundersen A.B., Piksa, M., Krysińska I., Kunst J.R., Noworyta,
K., Olejniuk A., Morzy M., Rygula R., Wójtowicz T., Piasecki J. Psychological
interventions countering misinformation in social media: A scoping
review, Frontiers in Psychiatry, 13, 2023
Misinformation Intervention
in the real world

32. Bot for targeted
corrections
No installation required
Corrections visible to all
Enables real-world testing
32

33. Search for
misinforming
tweets
Corrective replies to posts with misinforming URLs
Fact-checks
Database
Reply with a
correction
Misinforming
URLs, verdicts,
date
Found Tweet
with link to
misinforming
article
Reply sent by
Co-Inform Bot
Analyse
impact

34. 34
Reply
Templates
Factual Alerting
Identity Suggestive
Empathetic Alarming Friendly
Identified via crowdsourcing

35. 35
Factual
Alerting
Identity
Suggestive
Empathetic
Alarming
Friendly Hi there! Please note that the link you shared contains a claim that was fact-checked and
appears to be <VERDICT>. Fact-check <FACT-CHECK-URL>.
Please, note that the link you shared contains a claim that was fact-checked and appears
to be <VERDICT>. Fact-check: <FACT-CHECK-URL>
Oops… it seems something might be wrong! The link you shared contains a claim that
was fact-checked <FACT-CHECK-URL> and appears to be <VERDICT>.
I’m a bot fighting misinformation spread. I noticed the link you shared contains a claim
that was fact-checked <FACT-CHECK-URL> and appears to be <VERDICT>.
I know, it's hard to distinguish fact from fiction 😩. The link you shared contains a claim that
was fact-checked and appears to be <VERDICT>. Fact-check: <FACT-CHECK-URL>.
How about double-checking this? This link contains a claim that was fact-checked
<FACT-CHECK-URL> and appears to be <VERDICT>.
Misinformation can be really harmful! 😬 Please, note that the link you shared contains a
claim that was fact-checked and appears to be <VERDICT>. Fact-check: <FACT-CHECK-URL>

36. 36
Positive, Negative, and Unknown
Measuring reactions
Corrected
person
Corrective reply
Reply [negatively]
Block the bot
Do nothing Like the bot’s tweet
Retweet the bot’s tweet
Follow the bot
Delete the misinfo tweet
POSITIVE
REACTION
NEGATIVE
REACTION
UNKNOWN
REACTION
MIXED
REACTION

37. 37
4790
485
42
28
0 1000 2000 3000 4000 5000 6000
Unknown
Negative
Positive
Mixed
Reactions to Bot’s Interventions
Between May 2021 and June 2023, the bot posted 5345
corrections as replies to Tweets containing misleading URLs
The vast majority showed no discernible response
In total, 583 took some type of action
Do nothing
Block bot
Reply negatively
Follow bot
Delete misinfo post
Like bot’s tweet
Retweet bot’s tweet

38. 38
Many replies of the Alarming and Empathetic templates did not go through Twitter’s API, probably due to emojis!
No significant correlation was found between the templates and Positive and Negative reactions (Chi-square p-values of 0.2 and
0.7 respectively)
Templates and Mixed reactions are correlated (Chi-square p-value 0.018)
Total in
replies
988
978
963
1015
382
978
41
0% 5% 10% 15% 20% 25% 30%
Alarming
Alerting
Empathetic
Factual
Friendly
Identity
Suggestive
Reactions to Different Templates
Mixed Positive Negative

39. 39
304
33
7
167
128
11
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Replies Likes Retweets
Actions by Targets vs Audience
Action by target Action by audience

40. 40
13
1
259
3
20
0 50 100 150 200 250 300
Target liked the bot's reply and replied back
Target liked the bot's reply & blocked the bot
Target blocked the bot
Target deleted misinfo tweet & blocked the bot
Target deleted misinfo tweet
Reactions of Bot Targets
“Target” is a person who posted a tweet containing a misinforming link

41. 41
Targets are grouped into low, medium, or high
categories using quantiles of their followers and
followings.
A significant correlation is found for the Following
categories and reactions (Chi square 9, P-value: 0.045)
0 100 200 300 400 500 600
Positive reaction
Negative reaction
Mixed
Categories of people Followed
High Medium Low

42. Type of Replies
to the Bot
Distrust fact-
checkers
“fact-checkers are paid by pharma industry”, “controlled by Facebook and government”, “they
stop diversity of opinion”, “who checks the fact-checker?”, “who’s paying them?”
Anti-fact-
check sites
cite websites that speak against fact-checkers - eg einprozent.de.
Anti-bots
“you are a bot” , “you are a big pharma bot”, “When ‘They’ send a fact bot after me …then i
know I’m on to something”
Other
supporting
articles
point to another article with similar claims that was not fact-checked
Refer to
non-related
claims
bring up other claims to support their position, e.g., against a vaccine – “what about this, eh?”
Debating
“if you would do your research, you would learn that every 20,000 years the earth goes
through a cycle that changes the weather patterns. “
Discrediting
a source
highlight a previous inaccurate statement by a source (eg media outlet, politician) to discredit
Accuse of
censorship
“Freedom of speech”, “a contested opinion is still an opinion”, “this is censorship”, “police
state”, “ministry of truth”
Innocent “Thank you misinfobot, I merely posted in critique of the owner of the Twitter post ”
Appreciative “Thank you, It's hard to find the right information ” , “Thanks for the reaction”.
Positive
replies
3% Neutral
replies
15%
Negative
replies
82%
Positive replies Neutral replies
Negative replies

43. 43
Bot v2
Sends fact-check summaries
Switches between referencing fact-checks and not
Does not state the credibility of the post
Launched 30th August 2023
~30 corrections posted so far
0 blocks, 6 likes, 1 retweet, and 3 replies (2 positives,
from Targets)

44. 44
Challenges
in Bot
Design
Aligning language of bot's responses with target
posts
Twitter search cover a fraction of all Tweets.
Delay between finding a misinforming post and
sending a corrective response
Determining the intent behind a misinforming post is
challenging
Balancing intervention intensity while avoiding
platform blocks.
ClaimReview errors can cause fact-check and
misinforming URLs mismatch.
Many false claims are on platforms where bots are
not permitted

45. 45
LLMs and
Misinformation

47. “Nemo repente fuit
turpissimus”
“No one ever
becomes utterly bad
all at once.”
Seneca, 4 BC – AD 65
When does misinformation become
entrenched, and how can we time
interventions effectively to prevent this?
What's the strategic approach for timing
fact-check releases to precede the tipping
points of false claims?
How could we personalise our
interventions, by tuning them to Conspiracy
theorists, Influencers, Extremists, accidental
misinformers, etc?
Could we more directly and effectively
reach the misinformation sharer's audience
with corrective information?
Future Directions

48. 48
Take Home Messages
Acknowledge the susceptibility of
everyone to misinformation
Need for tools to assess our, and
other’s information reliability
Promoting accurate information and
accounts is as important as demoting
inaccurate ones
Release of fact-checks impacts the
spread of misinformation
Account for the influence of
misinformation interventions on
bystanders
Misinformation vs
Fact-Checks
The Ongoing Battle
Harith Alani
Knowledge Media Institute
6 Sept 2023 – ACM Hypertext, Rome
@halani