Overview of Fake News Detection Methods in Knowledge and the Web
Knowledge and the Web 2017/18
(2) Overview of fake-news detection
Bettina Berendt
Last updated: 2017-10-06
Structure for today‘s lecture:
The
process
of knowledge discovery
CRISP-DM
•
CRoss Industry Standard
Process for Data Mining
•
a data mining process
model that describes
commonly used
approaches that expert
data miners use to tackle
problems.
http://www.crisp-dm.org/Images/187343_CRISPart.jpg
•
1. “Business understanding”
•
What IS fake news?
Fake news – a definition
•
Fake news […] is where individuals or organisations
intentionally publish hoaxes, propaganda and other
misinformation and present it as factual.
•
This can include blog and social media posts and fake
online media releases.
•
It does not include news satire sites such as The Onion or
The Shovel as they are not presenting their content as
legitimate factual news. Their intention is satire rather than
misinformation.
•
It also does not include articles that are written from the
perspective of a particular opinion or editorial standpoint,
provided the information included is factually correct.
Slide from (Melbourne Atheneum Library, n.d.)
Fake news – a definition
•
Fake news […] is where individuals or organisations
intentionally
publish hoaxes, propaganda and other
misinformation and present it as factual
.
•
This can include blog and social media posts and fake
online media releases.
•
It does not include news satire sites such as The Onion or
The Shovel as they are not presenting their content as
legitimate factual news. Their intention is satire rather than
misinformation.
•
It also does not include articles that are written from the
perspective of a particular opinion or editorial standpoint,
provided the information included is factually correct.
Fake news – a definition
•
Fake news […] is where individuals or organisations
intentionally publish hoaxes, propaganda and other
misinformation and present it as factual.
•
This can include blog and social media posts and fake
online media releases.
•
It
does not include news satire sites
such as The Onion or
The Shovel as they are not presenting their content as
legitimate factual news. Their intention is satire rather than
misinformation.
•
It also does not include articles that are written from the
perspective of a particular opinion or editorial standpoint,
provided the information included is factually correct.
•
Actually, “fake news” was often used to
denote precisely these news outlets before
Trump made the term’s current meaning
popular …
Weekend Update
Beginning in 1975
with Chevy Chase,
Weekend Update
quickly became a
favorite skit among
all of Saturday
Night Live’s
infamous sketches.
“Good night, and have
a pleasant tomorrow.”
-Chevy Chase
Slide from (Bennett et al., 2016)
Weekend Update
Focusing on satirical
commentary of actual
events, Weekend
update also features
complete fabrication.
See Tina Fey in
Weekend Update
Slide from (Bennett et al., 2016)
The Daily Show
First aired on July
22
nd
, 1996 by Craig
Kilborn, who was
later replaced by
current host and
“anchorman” Jon
Stewart
The Daily Show
exists
as a news parody
program, and has
gained a reputation as
one of the sharpest
political commentaries
on television
Slide from (Bennett et al., 2016)
The Daily Show
Winner of 9 Emmys, 9 other
wins, and 23 nominations
Focuses on humorous
retellings of actual current
events
Though intentionally unreliable
as a news source, many young
Americans admit to gaining the
majority of their current events
news from The Daily Show.
click to see video clips of
The
Daily Show
Slide from (Bennett et al., 2016)
The Colbert Report
Starring Stephen Colbert,
existing as a political
satire
A spin-off of
The Daily
Show
,
The Colbert
Report
parodies
personality driven political
pundit programs like
The
O’Reilly Factor
Notorious for its
“truthiness” and faux-
conservative tinge
Slide from (Bennett et al., 2016)
The Onion
Weekly published parody newspaper
Satirizes current events which are both
real and made up.
Formed in 1988 in Madison, WI. Gained
popularity with its website in 1996.
Started to break into the mainstream in
2000.
Has A.V. section which covers the arts
and entertainments truthfully, but
humorously.
Slide from (Bennett et al., 2016)
Fake news – a definition
•
Fake news […] is where individuals or organisations
intentionally publish hoaxes, propaganda and other
misinformation and present it as factual.
•
This can include blog and social media posts and fake
online media releases.
•
It does not include news satire sites such as The Onion or
The Shovel as they are not presenting their content as
legitimate factual news. Their intention is satire rather than
misinformation.
•
It also
does not include articles that are written from the
perspective of a particular opinion or editorial standpoint
,
provided the information included is factually correct.
My FN, your FN?
http%3A%2F%2Finsider.foxnews.com%2F2017%2F02%2F24%2Fwash-post-stands-behind-9-source-story-after-trump-calls-it-fake-news
Facts?
Opinions?
FN?
https://www.nytimes.com/2017/10/04/opinion/vegas-gun-
control-debate.html
Fake news – a definition
•
Fake news […] is where individuals or organisations
intentionally
publish hoaxes, propaganda and other
misinformation and present it as factual.
•
This can include blog and social media posts and fake
online media releases.
•
It does not include news satire sites such as The Onion or
The Shovel as they are not presenting their content as
legitimate factual news. Their intention is satire rather than
misinformation.
•
It also does not include articles that are written from the
perspective of a particular opinion or editorial standpoint,
provided the information included is factually correct.
Intention?
•
The intentionality of deception is also a
requirement in Rubin et al.’s (2015) definition
•
Whose intentionality?
–
Creator of the news?
•
E.g. Governments (
WMD example)
•
The press
–
Purveyor of the news?
•
The press
•
Social media
you!
•
How to capture the intention as a DM/ML
feature?
Weapons of Mass
Destruction
and the 2003 Iraq
war
https://fas.org/irp/cia/product/image016.
jpg
https://en.wikipedia.org/wiki/Iraq_and_w
eapons_of_mass_destruction
•
If A, knowing that an item X is satire, retweets
X “without the metadata that it is satire”,
•
And B reads and believes it
•
Then did A create and/or spread FN?
"
On Bullshit
" (2005), by
philosopher
Harry G. Frankfurt
, is an essay that
presents a theory of
bullshit
that defines
the concept and analyzes the
applications of bullshit in the contexts of
communication. Frankfurt determines
that bullshit is speech intended to
persuade (a.k.a.
rhetoric
), without regard
for truth.
•
The liar cares about the truth and
attempts to hide it;
•
the bullshitter doesn't care if what
they say is true or false, but rather
only cares whether or not their
listener is persuaded.
CS
approach:
Define a
“ground
truth”
http://www.fakenewschallenge.org
•
2. + 3. Data understanding and preparation
http://www.fakenewschallenge.org/
•
More options and steps possible / necessary
•
Use off-the-shelf tools for NLP processing and
feature extraction
•
Some pointers will be published on Toledo
•
4. Modelling
•
4.1. Approach: what do humans do to debunk
FN?
Slide from (Melbourne Atheneum Library, n.d.)
Slide from (Melbourne Atheneum Library, n.d.)
•
4. Modelling
•
4.2. Approach: How to formalise FN
detection?
•
What is the task?
(Human) strategies
Human strategies translate to various
machine tasks
Strategy “Read past the headline”
•
The goal of the
Fake News Challenge
is to explore how artificial intelligence
technologies, particularly machine learning and natural language processing,
might be leveraged to combat the fake news problem. We believe that these AI
technologies hold promise for significantly automating parts of the procedure
human fact checkers use today to determine if a story is real or a hoax.
•
Assessing the veracity of a news story is a complex and cumbersome task, even for
trained experts
3
. Fortunately, the process can be broken down into steps or
stages. A helpful first step towards identifying fake news is to understand what
other news organizations are saying about the topic. We believe automating this
process, called
Stance Detection
, could serve as a useful building block in an AI-
assisted fact-checking pipeline. So stage #1 of the
Fake News Challenge (FNC-1)
focuses on the task of Stance Detection.
•
Stance Detection involves estimating the relative perspective (or stance) of two
pieces of text relative to a topic, claim or issue. The version of Stance Detection we
have selected for FNC-1 extends the work of Ferreira & Vlachos
4
. For FNC-1 we
have chosen the task of estimating the stance of a body text from a news article
relative to a headline. Specifically, the body text may agree, disagree, discuss or be
unrelated to the headline.
http://www.fakenewschallenge.org/
Task “stance detection”
(à la Fake News Challenge)
http://www.fakenewschallenge.org/
Stance
detection
-
Example
http://www.fakenewschallenge.org/
Strategies “Follow links and check sources”
and “Check other news outlets”
Task:
Claim
validation
Slide from (Hanselowski & Gurevych, 2017)
Task “veracity assessment”
(via article classification or regression)
“the prediction of the chances of a particular
news article (news report, editorial, expose,
etc.) being intentionally deceptive”
(Rubin, Conroy, & Chen, 2015)
•
4. Modelling
•
4.2. Approach: How to formalise FN
detection?
•
How to do this?
Example Fake News Challenge 1
Slide from (Hanselowski & Gurevych, 2017)
Cisco’s SOLAT in the SWEN (1)
http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
SOLAT in the SWEN (2)
http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
SOLAT in the SWEN (3)
http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
Slide from (Hanselowski & Gurevych, 2017)
Example Claim Validation
•
(work in progress)
Slide from (Hanselowski & Gurevych, 2017)
Slide from (Hanselowski & Gurevych, 2017)
Slide from (Hanselowski & Gurevych, 2017)
Slide from (Hanselowski & Gurevych, 2017)
Classification by Conroy et al. (2015)
•
Linguistic approaches
–
Mainly word-based
–
Syntax-based approaches
–
Semantic analysis
•
Compare “profile” of document with others known to be genuine
–
Rhetorical structure and discourse analysis
•
Systematic differences between deceptive and truthful messages in terms of their
coherence and structure
–
Classifiers
•
Classification of sentiment: assumption that deceivers use unintended emotional
communication
–
In sum, linguistic approaches most suited to domain-specific studies (e.g.
product reviews, business), may have limited generalizability
•
Network approaches
–
Linked data
–
Social network behaviour
•
Hybrids
Classification by Conroy et al. (2015)
•
Linguistic approaches
more details: 18 October
–
Mainly word-based
–
Syntax-based approaches
–
Semantic analysis
•
Compare “profile” of document with others known to be genuine
–
Rhetorical structure and discourse analysis
•
Systematic differences between deceptive and truthful messages in terms of their
coherence and structure
–
Classifiers
•
Classification of sentiment: assumption that deceivers use unintended emotional
communication
–
In sum, linguistic approaches most suited to domain-specific studies (e.g.
product reviews, business), may have limited generalizability
•
Network approaches
–
Linked data
next week
–
Social network behaviour
invited lecture on 8 November
•
Hybrids
18 October
•
4. Modelling
•
4.3. Beyond data mining / machine learning
What about the other strategies?
citation
analysis +
reputation?!
What about the other strategies?
I would add:
Don’t assume
something is
true just
because it is
entertaining.
Maybe the satire news point to some
other cause?
•
News consumption as entertainment
•
Including satire-news?
http%3A%2F%2Fwww.fipp.com%2Fnews%2Finsightnews%
2Fchart-millennials-pay-for-entertainment-not-news
https%3A%2F%2Fwww.pinterest.co.uk%2Fmeyerlinger%2Fhead-
down-generation-smartphone-zombies
What about the other strategies?
Awareness
tools /
nudges?!
Example: The FB audience nudge (better example: the
timer nudge, but I didn’t find a picture of it)
HCI over
and
above
DM/ML!
(Wang et al., 2013)
Strategy “prevention”?!
https%3A%2F%2Fimage.slidesharecdn.com%2Fmegenerationsreport2017final-170911182329%2F95%2Fadi-media-
entertainment-generations-report-2017-10-638.jpg
www.businessinsider.com/us-millennials-pay-for-
entertainment-not-news-2015-11
Are you a subscriber to a “real”
newspaper (paper or electronic)?
Do you know what it costs?
Just one example
https://image.slidesharecdn.com/zuoraguardiankeynote0320-v3-130325161057-phpapp01/95/paywall-20-the-reinvention-of-media-15-638.jpg
•
5. Evaluation
Slide from (Hanselowski & Gurevych, 2017)
Important:
observations about the data and the solution
(from the FNC-1’s SOLAT in the SWEN team)
After exploring the dataset, a few features that are likely to be informative of
headline/body relationships became obvious -- for example:
•
The number overlapping words between the headline and body text;
•
Similarities measured between the word count, 2-grams and 3-grams; and
•
Similarities measured after transforming these counts with term frequency-inverse
document frequency (
TF-IDF
) weighting and Singular Value Decomposition (
SVD
).
Using these features, it is not necessary to use a powerful and expressive model to
learn the complex mapping from these features to the stance label.
For this, Gradient-Boosted Decision Trees were chosen because of the model’s
robustness with regard to the different scales of our feature vectors. Specifically, no
normalization is needed and it can be regularized in several different ways to avoid
overfitting. Furthermore,
XGBoost
is a very efficient, open-source implementation that
was easily applied to the handcrafted features.
http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
Human performance as an upper
bound?
•
Bond & DePaulo (2006)
•
Meta-analysis of >200 experiments
•
How good are humans at detecting lies in
text?
•
4% better than chance
Laypeople and experts?!
Another corpus: LIAR
We collected a decade-long, 12.8K
manually labeled short statements in
various contexts from POLITIFACT.COM,
which provides detailed analysis report
and links to source documents for each
case.
William Yang Wang
, "Liar, Liar Pants on
Fire": A New Benchmark Dataset for
Fake News Detection, to appear in
Proceedings of the 55th Annual Meeting
of the Association for Computational
Linguistics (
ACL 2017
)
, short paper,
Vancouver, BC, Canada, July 30-August
4, ACL.
DATA
BIB
And more
•
https://www.kaggle.com/arminehn/rumor-citation
•
A Snopes dataset from MPI
–
http://resources.mpi-inf.mpg.de/impact/web_credibility_analysis/README
–
http://resources.mpi-inf.mpg.de/impact/web_credibility_analysis/
–
http://resources.mpi-inf.mpg.de/impact/credibilityanalysis
/
•
The Fake News Challenge dataset (see their site)
•
Note: I have not inspected any of these for
quality yet!
•
6. Deployment
Thank you!
References
Bennett, R., Gustafson, G., & Paul, S. (2016). Fake News.
http://
teachingmedialiteracy.pbworks.com/f/Fake+News.ppt
Bond, C.F. Jr. & DePaulo, B.M. (2006). Accuracy of deception judgments. Personality and Social
Psychology Review, 10 (3), 214-234.
http://journals.sagepub.com/doi/abs/10.1207/s15327957pspr1003_2
Conroy, N.J., Rubin, V.L., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake
news. ASIST 2015.
https://www.asist.org/files/meetings/am15/proceedings/submissions/posters/193poster.pdf
Frankfurt, H.G. (2005). On Bullshit. Princeton University Press.
https://www.stoa.org.uk/topics/bullshit/pdf/on-bullshit.pdf
Hanselowski, A. & Gurevych, I. (2017). NLP approaches to fact checking and fake news detection.
Presentation at Dagstuhl Seminar “User-Generated Content in Social Media“. July 2017.
http://materials.dagstuhl.de/files/17/17301/17301.IrynaGurevych.Slides.pdf
Melbourne Atheneum Library (n.d.) Credible Sources.
http://www.melbourneathenaeum.org.au/images/Website/esmart/credible_sources.pps
Rubin, V., Conroy, N., & Chen, Y. (2015). Towards news verification: Deception detection methods for
news discourse. Hawaii International Conference on Systems Sciences.
http://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1046&context=fimspres
Wang, Y., Leon, P.G., Chen, X., Komanduri, S., & Norcie, G.(2013). From Facebook regrets to Facebook
privacy nudges. Ohio State Law Journal, 74, 1307-1335.
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1335&context=heinzworks
This content provides an overview of fake news detection methods in the context of Knowledge and the Web, covering topics such as the definition of fake news, business understanding, and the process of knowledge discovery. It discusses the implications of fake news dissemination and differentiates it from satire or opinion pieces. The course structure includes lectures on data mining processes and computational approaches to trust and reputation in web data mining. Overall, it emphasizes the importance of discerning misinformation from factual news in today's digital landscape.
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Presentation Transcript
Knowledge and the Web 2017/18 (2) Overview of fake-news detection Bettina Berendt Last updated: 2017-10-06
Plan Lecture (Wed 16+) Exercise session (Thu 10.30+) Your individual & team work Overview, warm-up, Entry test Do the entry test Fake news detection methods: overview Establish team + research question for project (version 1) Semantic Web + Linked Data SPARQL Work on your project around the task of Fake news detection methods: research methods overview Methods continued and: DM / ML test Journalistic fact-checking (IT) Data quality task Trust and reputation: computational approaches (IT) Project consultancy Project consultancy SW/LD in industry (IT) Project consultancy LD Fragments (IT) Peer-reviewing of projects Ethics of web data mining: issues and approaches (incl. privacy, discrimination) Peer-reviewing of projects Peer-reviewing of projects Key IT = invited talk may be skipped by 4-ECTS students Project consultancy Project presentations
Structure for todays lecture: The process of knowledge discovery CRISP-DM CRoss Industry Standard Process for Data Mining a data mining process model that describes commonly used approaches that expert data miners use to tackle problems. http://www.crisp-dm.org/Images/187343_CRISPart.jpg
1. Business understanding What IS fake news?
Fake news a definition Fake news [ ] is where individuals or organisations intentionally publish hoaxes, propaganda and other misinformation and present it as factual. This can include blog and social media posts and fake online media releases. It does not include news satire sites such as The Onion or The Shovel as they are not presenting their content as legitimate factual news. Their intention is satire rather than misinformation. It also does not include articles that are written from the perspective of a particular opinion or editorial standpoint, provided the information included is factually correct. Slide from (Melbourne Atheneum Library, n.d.)
Fake news a definition Fake news [ ] is where individuals or organisations intentionally publish hoaxes, propaganda and other misinformation and present it as factual. This can include blog and social media posts and fake online media releases. It does not include news satire sites such as The Onion or The Shovel as they are not presenting their content as legitimate factual news. Their intention is satire rather than misinformation. It also does not include articles that are written from the perspective of a particular opinion or editorial standpoint, provided the information included is factually correct.
Fake news a definition Fake news [ ] is where individuals or organisations intentionally publish hoaxes, propaganda and other misinformation and present it as factual. This can include blog and social media posts and fake online media releases. It does not include news satire sites such as The Onion or The Shovel as they are not presenting their content as legitimate factual news. Their intention is satire rather than misinformation. It also does not include articles that are written from the perspective of a particular opinion or editorial standpoint, provided the information included is factually correct.
Actually, fake news was often used to denote precisely these news outlets before Trump made the term s current meaning popular
Weekend Update Beginning in 1975 with Chevy Chase, Weekend Update quickly became a favorite skit among all of Saturday Night Live s infamous sketches. Good night, and have a pleasant tomorrow. -Chevy Chase Slide from (Bennett et al., 2016)
Weekend Update Focusing on satirical commentary of actual events, Weekend update also features complete fabrication. See Tina Fey in Weekend Update Slide from (Bennett et al., 2016)
The Daily Show First aired on July 22nd, 1996 by Craig Kilborn, who was later replaced by current host and anchorman Jon Stewart The Daily Show exists as a news parody program, and has gained a reputation as one of the sharpest political commentaries on television Slide from (Bennett et al., 2016)
The Daily Show Winner of 9 Emmys, 9 other wins, and 23 nominations Focuses on humorous retellings of actual current events Though intentionally unreliable as a news source, many young Americans admit to gaining the majority of their current events news from The Daily Show. click to see video clips of The Daily Show Slide from (Bennett et al., 2016)
The Colbert Report Starring Stephen Colbert, existing as a political satire A spin-off of The Daily Show, The Colbert Report parodies personality driven political pundit programs like The O Reilly Factor Notorious for its truthiness and faux- conservative tinge Slide from (Bennett et al., 2016)
The Onion Weekly published parody newspaper Satirizes current events which are both real and made up. Formed in 1988 in Madison, WI. Gained popularity with its website in 1996. Started to break into the mainstream in 2000. Has A.V. section which covers the arts and entertainments truthfully, but humorously. Slide from (Bennett et al., 2016)
Fake news a definition Fake news [ ] is where individuals or organisations intentionally publish hoaxes, propaganda and other misinformation and present it as factual. This can include blog and social media posts and fake online media releases. It does not include news satire sites such as The Onion or The Shovel as they are not presenting their content as legitimate factual news. Their intention is satire rather than misinformation. It also does not include articles that are written from the perspective of a particular opinion or editorial standpoint, provided the information included is factually correct.
My FN, your FN? http%3A%2F%2Finsider.foxnews.com%2F2017%2F02%2F24%2Fwash-post-stands-behind-9-source-story-after-trump-calls-it-fake-news
Facts? Opinions? FN? https://www.nytimes.com/2017/10/04/opinion/vegas-gun- control-debate.html
Fake news a definition Fake news [ ] is where individuals or organisations intentionally publish hoaxes, propaganda and other misinformation and present it as factual. This can include blog and social media posts and fake online media releases. It does not include news satire sites such as The Onion or The Shovel as they are not presenting their content as legitimate factual news. Their intention is satire rather than misinformation. It also does not include articles that are written from the perspective of a particular opinion or editorial standpoint, provided the information included is factually correct.
Intention? The intentionality of deception is also a requirement in Rubin et al. s (2015) definition Whose intentionality? Creator of the news? E.g. Governments ( WMD example) The press Purveyor of the news? The press Social media you! How to capture the intention as a DM/ML feature?
https://fas.org/irp/cia/product/image016. jpg https://en.wikipedia.org/wiki/Iraq_and_w eapons_of_mass_destruction Weapons of Mass Destruction and the 2003 Iraq war
If A, knowing that an item X is satire, retweets X without the metadata that it is satire , And B reads and believes it Then did A create and/or spread FN?
"On Bullshit" (2005), by philosopher Harry G. Frankfurt, is an essay that presents a theory of bullshit that defines the concept and analyzes the applications of bullshit in the contexts of communication. Frankfurt determines that bullshit is speech intended to persuade (a.k.a. rhetoric), without regard for truth. The liar cares about the truth and attempts to hide it; the bullshitter doesn't care if what they say is true or false, but rather only cares whether or not their listener is persuaded.
CS approach: Define a ground truth http://www.fakenewschallenge.org
More options and steps possible / necessary Use off-the-shelf tools for NLP processing and feature extraction Some pointers will be published on Toledo
4. Modelling 4.1. Approach: what do humans do to debunk FN?
4. Modelling 4.2. Approach: How to formalise FN detection? What is the task?
Human strategies translate to various machine tasks
Strategy Read past the headline The goal of the Fake News Challenge is to explore how artificial intelligence technologies, particularly machine learning and natural language processing, might be leveraged to combat the fake news problem. We believe that these AI technologies hold promise for significantly automating parts of the procedure human fact checkers use today to determine if a story is real or a hoax. Assessing the veracity of a news story is a complex and cumbersome task, even for trained experts 3. Fortunately, the process can be broken down into steps or stages. A helpful first step towards identifying fake news is to understand what other news organizations are saying about the topic. We believe automating this process, called Stance Detection, could serve as a useful building block in an AI- assisted fact-checking pipeline. So stage #1 of the Fake News Challenge (FNC-1) focuses on the task of Stance Detection. Stance Detection involves estimating the relative perspective (or stance) of two pieces of text relative to a topic, claim or issue. The version of Stance Detection we have selected for FNC-1 extends the work of Ferreira & Vlachos 4. For FNC-1 we have chosen the task of estimating the stance of a body text from a news article relative to a headline. Specifically, the body text may agree, disagree, discuss or be unrelated to the headline. http://www.fakenewschallenge.org/
Task stance detection ( la Fake News Challenge) http://www.fakenewschallenge.org/
Stance detection - Example http://www.fakenewschallenge.org/
Strategies Follow links and check sources and Check other news outlets Task: Claim validation Slide from (Hanselowski & Gurevych, 2017)
Task veracity assessment (via article classification or regression) the prediction of the chances of a particular news article (news report, editorial, expose, etc.) being intentionally deceptive (Rubin, Conroy, & Chen, 2015)
4. Modelling 4.2. Approach: How to formalise FN detection? How to do this?
Ciscos SOLAT in the SWEN (1) http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
SOLAT in the SWEN (2) http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
SOLAT in the SWEN (3) http://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
Example Claim Validation (work in progress)
