This document analyzes Wikipedia hoaxes to understand their impact, characteristics, and methods for detection. It finds that while most hoaxes are caught quickly, some can last a long time, receive significant traffic, and be referenced by credible news media, making them more impactful. Hoaxes are distinguishable from real articles in that they tend to be longer with more plain text but fewer references, have incoherent link networks, receive fewer prior mentions from suspicious editors, and are created by recently registered editors with less experience. Machine learning models can detect hoaxes with 98% accuracy using editor and network features, outperforming humans who are often fooled by more coherent-appearing hoaxes. Non-appearance features are important for
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Overview of In-a-Moon's new payment infrastructure. Shows how one can have micropayments for websites. Is this the missing infrastructure that will keep newspapers from going out of business?
In this era of fake news and paid news artificial intelligence is more and more used as a political tool to manipulate and dictate common people. Through big data, biometric data AI analyses online profiles and behaviors in social media and smart phones. But the days are not far when AI will also control the politicians and the media too presentation from Dr. DatchanaMoorthy Ramu
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7.3 Digital Divides y
7.4 Control of Our Devices and Data
7.5 Making Decisions About Technology A
Exercises a
A
356 Chapter 7 Evaluating and Controlling Technology
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Whole books focus on these topics. The presentations here are necessarily
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7.1 Evaluating Information
A little learning is a dang 'rous thing;
Drink deep, or taste not the Pierian spring;
There shallow draughts intoxicate the brain,
And drinking largely sobers us again.
—Alexander Pope, 1709'
7.1.1 The Need for Responsible Judgment
What is real? What is fake? Why does it matter?
We can get the wrong answer to a question quicker than our fathers and
mothers could find a pencil.
—Robert McHenry^
There is a daunting amount of information on the Web—^and much of it is wrong.
Quack medical cures abound. Distorted history, errors, outdated information, bad
financial advice—it is all there. Marketers and pubUc relations firms spread unlabeled
advertisements through blogs, social media, and video sites. Search engines have
largely replaced librarians for finding information, but search engines rank informa
tion sources at least partially by popularity and give prominent display to content
providers who pay; librarians do not. Wikipedia, the biggest online encyclopedia, is
immensely popular, but can we rely on its accuracy and objectivity when anyone can
edit any article at any time? On social journalism sites, readers submit and vote on
news stories. Is this a good way to get news? The nature of the Intemet encourages
people to post their immediate thoughts and reactions without taking time for con
templation or for checking facts. How do we know what is worth reading in contexts
where there are no editors selecting well-written and well-researched articles?
Faking photos is not new; photographers have long staged scenes and altered
photos in dark rooms. When we see a video of a currently popular performer sing
ing with Elvis Presley (who died in 1977), we know we are watching creative
7.1 Evaluating Information 357
entertainment—digital magic at work. But the same technologies can deceive, and
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Fake news: Identifying, debunking and discussing false narratives with learnersLearningandTeaching
Fake news. It was the 2017 word of the year, but how is it understood by the student of today?
Students today are often heavily engaged in the online community, moving in social spheres that may be foreign to their teachers. With studies revealing that 48% of Australians now use social media as a news source, it is increasingly important for educators to understand how their students are engaging with online content and communities. As educators, we must equip ourselves with the tools and skillsets needed to debunk false, misleading and biased content and to show our students how to do the same.
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7.1 Evaluating Information7.2 Neo-Luddite Views of Compute.docxsleeperharwell
7.1 Evaluating Information
7.2 Neo-Luddite Views of Computers, Technology, and Quality of Life
7.3 Digital Divides y
7.4 Control of Our Devices and Data
7.5 Making Decisions About Technology A
Exercises a
A
356 Chapter 7 Evaluating and Controlling Technology
In this chapter, we consider such questions as these: Does the openness and
"democracy" of the Web increase distribution of useful information or of inac
curate, foohsh, and biased information? How should we handle the latter? How
can we evaluate complex computer models of physical and social phenomena?
Is computing technology evil? Why do some people think it is? How does access
to digital technology ditfer among dilferent populations? How should we control
technology to ensure positive uses and consequences? How soon will robots and
digital devices be more intelligent than people? What will happen after that?
Whole books focus on these topics. The presentations here are necessarily
brief. They introduce issues, arguments, and many questions.
7.1 Evaluating Information
A little learning is a dang 'rous thing;
Drink deep, or taste not the Pierian spring;
There shallow draughts intoxicate the brain,
And drinking largely sobers us again.
—Alexander Pope, 1709'
7.1.1 The Need for Responsible Judgment
What is real? What is fake? Why does it matter?
We can get the wrong answer to a question quicker than our fathers and
mothers could find a pencil.
—Robert McHenry^
There is a daunting amount of information on the Web—^and much of it is wrong.
Quack medical cures abound. Distorted history, errors, outdated information, bad
financial advice—it is all there. Marketers and pubUc relations firms spread unlabeled
advertisements through blogs, social media, and video sites. Search engines have
largely replaced librarians for finding information, but search engines rank informa
tion sources at least partially by popularity and give prominent display to content
providers who pay; librarians do not. Wikipedia, the biggest online encyclopedia, is
immensely popular, but can we rely on its accuracy and objectivity when anyone can
edit any article at any time? On social journalism sites, readers submit and vote on
news stories. Is this a good way to get news? The nature of the Intemet encourages
people to post their immediate thoughts and reactions without taking time for con
templation or for checking facts. How do we know what is worth reading in contexts
where there are no editors selecting well-written and well-researched articles?
Faking photos is not new; photographers have long staged scenes and altered
photos in dark rooms. When we see a video of a currently popular performer sing
ing with Elvis Presley (who died in 1977), we know we are watching creative
7.1 Evaluating Information 357
entertainment—digital magic at work. But the same technologies can deceive, and
circulation of a fake photo on the Internet can start a riot or bring death threats to an
innocent person. Here is a.
NASW Workshop: The Secret Life of Social MediaDennis Meredith
What you think you know about social media is probably wrong. This session will discuss how these tools actually operate, often at odds with promoted functions. Based on data collected and analyzed by panelists and online science publications, we will discuss Digg, reddit, StumbleUpon, Slashdot, Facebook, Twitter, and other social media tools (with background materials for the uninitiated).
Quora ML Workshop: Sock Puppets and Hoaxes on the WebQuora
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What makes fake news fake? As a society, we have been bombarded with the idea that the news we consume every day is fabricated, but the truth is far more complicated than that. Join Indiana University East librarian KT Lowe as she discusses the identifiable traits of fake news, offers tips on how to tackle fake news claims and demonstrates what makes real news real.
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# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
Disinformation on the Web: impact, characteristics and detection of Wikipedia hoaxes
1. Disinformation on the Web:
Impact, Characteristics and
Detection of Wikipedia Hoaxes
Srijan Kumar Univ. of Maryland
Robert West Stanford Univ.
Jure Leskovec Stanford Univ.
1
Originally presented at the 25th International World Wide Web Conference,
Montreal, Canada, April 2016
2. Web: Source of information
2
62% adults
in U.S.A.
rely on
social media
for news
28% of 18-
24 year olds
use social
media as
primary
news source
4. Types of false information
4
Misinformation
honest mistake
Disinformation
deliberate lie to mislead
Hoax
“deliberately
fabricated falsehood
made to masquerade
as truth”
Wikipedia
5. Why Wikipedia?
The free encyclopedia that anyone can edit
5
Easy to add (false)
information
• Freely accessible
• Large reach
• Major source of
information for
many
10. Impact of hoaxes
“The worst hoaxes are those which
(a) last for a long time,
(b) receive significant traffic,
(c) are relied upon by credible news media.”
Jimmy Wales on Quora
10
11. Impact of hoaxes
“The worst hoaxes are those which
(a) last for a long time”
11
Time t between patrolling and flagging
0.990.90
12. Impact of hoaxes
“The worst hoaxes are those which
(b) receive significant traffic”
12
10 100 500
Number n of pageviews per day
13. Impact of hoaxes
“The worst hoaxes are those which
(c) are relied upon by credible news media”
13
1.08
active inlinks
per hoax article,
on average
7% of hoax
articles have at
least 5
active inlinks
15. 15
Successful hoax
pass patrol
survive for a month
viewed 100+/day
Failed hoax
flagged and
deleted during
patrol
Wrongly flagged
temporarily flagged
Legitimate
articles
never flagged
Hoax
Non-hoax
16. Characteristics of hoaxes
16
Appearance:
how the article
looks
Link-network:
how the article
connects
Support:
how other
articles refer to it
Editor:
how the article
creator looks
17. Characteristics of hoaxes
17
Surprisingly, hoax articles are
longer than non-hoax articles!
Features:
o Plain-text length
Appearance:
how the article
looks
Link-network:
how the article
connects
Support:
how other
articles refer to it
Editor:
how the article
creator looks
18. Characteristics of hoaxes
18
Surprisingly, hoax articles are
longer than non-hoax articles!
but
they mostly have plain text and
have fewer web and wiki links.
Appearance:
how the article
looks
Link-network:
how the article
connects
Support:
how other
articles refer to it
Editor:
how the article
creator looks
Features:
o Plain-text length
o Plain-text-to-markup ratio
o Wiki-link density
o Web-link density
19. Characteristics of hoaxes
19
Clustering coefficient = 0
incoherent article
Clustering coefficient > 0
coherent article
Legitimate articles are more
coherent than successful hoaxes
Appearance:
hoaxes mostly
have text and
few references.
Link-network:
how the article
connects
Support:
how other
articles refer to it
Editor:
how the article
creator looks
20. Characteristics of hoaxes
20
Hoax mentions are less in number.
Features:
o Number of prior mentions
Appearance:
hoaxes mostly
have text and
few references.
Link-network:
hoaxes have
incoherent
wikilinks.
Support:
how other articles
refer to it
Editor:
how the article
creator looks
21. Characteristics of hoaxes
21
Hoax mentions are less in number,
mostly created by article creator or
anonymously, and are more
recently created.
Features:
o Number of prior mentions
o Creator of first mention
o Time since first mention
Appearance:
hoaxes mostly
have text and
few references.
Link-network:
hoaxes have
incoherent
wikilinks.
Support:
how other articles
refer to it
Editor:
how the article
creator looks
22. Characteristics of hoaxes
22
Hoax creators are more recently
registered, and
have lesser editing experience.
Features:
o Creator’s time since registration
o Creator’s experience
Appearance:
hoaxes mostly
have text and
few references.
Link-network:
hoaxes have
incoherent
wikilinks.
Support:
hoaxes have few,
recent, suspicious
mentions.
Editor:
how the article
creator looks
24. Detection of hoaxes
24
Will a hoax get
past patrol?
Is an article
a hoax?
Is an article flagged
as hoax really one?
AUC = 71%
Appearance
features
AUC = 98%
Editor and
Network features
AUC = 86%
Editor and
support features
25. We discovered previously unknown hoaxes!
25
Flagged by us and deleted by Wikipedia administrators
Steve Moertel
American
popcorn
entrepreneur
Article survived over
6 years 11 months!
26. Can readers identify hoaxes?
26
Results
320 random hoax and non-hoax pairs
10 raters on Amazon Mechanical Turk rated each pair
Casual readers are gullible to hoaxes.
Accurate detection needs non-appearance features.
50%
Random
66%
Human
86%
Classifier
27. What fools humans?
27
Humans get fooled when article looks more “genuine”,
and it is assumed to be credible.
Comparing easy- vs hard-to-identify hoaxes
28. How to identify misinformation on the web?
28
● Appearance
○ How well referenced is the information source?
○ What is the content of the article?
● Editor
○ Who created the information?
● Network
○ How related is this information to other information it
references to?
● Support
○ Is there any evidence of the information, prior to its
creation?
29. Wikipedia Hoaxes
29
Impact
of hoaxes
Characteristics
of hoaxes
Detection
of hoaxes
Hoaxes are
different from non-
hoaxes in many
respects
Most hoaxes are
caught soon, but
some hoaxes are
impactful
Non-appearance
features are
important to
detect hoaxes
Web is a space for all, where anyone can read, publish and share information.
It is rapidly becoming one of the major sources of news and information for everyone.
In fact, 62% of adults in USA rely on social media for news, and more than a quarter of youngsters, between the age of 18 and 24, rely primarily on social media for news, even more than they rely on television.
And in the third dimension, we look at how much the hoax article has spread across the web.
For that, we use Wikipedia server’s click logs, to look at which links were clicked from across the web, both within and outside Wikipedia, that lead to the hoax article.
And we find that on an average, each hoax article has 1.08 inlinks that were actually clicked and the reader came to read the article. These links were from search engines, social networks, and from within Wikipedia too.