Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes
Robert West
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Jure Leskovec
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Srijan Kumar
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University of Maryland
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Stanford University
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Stanford University
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srijan@cs.umd.edu
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west@cs.stanford.edu
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jure@cs.stanford.edu
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ABSTRACT
Wikipedia is a major source of information for many
people. How- ever, false information on Wikipedia raises concerns about
its cred- ibility. One way in which false information may be presented
on Wikipedia is in the form of hoax articles, i.e.,
articles containing fabricated facts about nonexistent entities or
events. In this paper we study false information on Wikipedia by
focusing on the hoax articles that have been created throughout its
history. We make several contributions. First, we assess the real-world
impact of hoax articles by measuring how long they survive before being
de- bunked, how many pageviews they receive, and how heavily they are
referred to by documents on the Web. We find that, while most hoaxes are
detected quickly and have little impact on Wikipedia, a small number of
hoaxes survive long and are well cited across the Web.
Second, we
characterize the nature of successful hoaxes by comparing them to
legitimate articles and to failed hoaxes that were discovered shortly
after being created. We find characteristic differences in terms of
article structure and content, embeddedness into the rest of Wikipedia,
and features of the editor who created the hoax. Third, we successfully
apply our findings to address a series of classification tasks, most
notably to determine whether a given article is a hoax. And finally, we
describe and evaluate a task involving humans distinguishing hoaxes from
non-hoaxes. We find that humans are not good at solving this task and that our automated classifier outperforms them by a big margin.
1.INTRODUCTION
The Web is a space for all, where, in principle,
everybody can read, and everybody can publish and share, information.
Thus, knowledge can be transmitted at a speed and breadth unprecedented
in human history, which has had tremendous positive effects on the lives
of billions of people. But there is also a dark side to the un- reigned
proliferation of information over the Web: it has become a breeding
ground for false information [6, 7, 12, 15, 19, 43].
The reasons for communicating false information vary widely: on the one extreme, misinformation is conveyed in the honest but
∗Research done partly during a visit at Stanford University.
Copyright is held by the International World Wide
Web Conference Com- mittee (IW3C2). IW3C2 reserves the right to provide a
hyperlink to the author’s site if the Material is used in electronic
media.
WWW 2016, April 11–15, 2016, Montréal, Québec, Canada. ACM 978-1-4503-4143-1/16/04. http://dx.doi.org/10.1145/2872427.2883085.
mistaken belief that the relayed incorrect facts are true; on the other extreme, disinformation denotes
false facts that are conceived in order to deliberately deceive or
betray an audience [11, 17]. A third class of false information has been
called bullshit, where the agent’s primary
purpose is not to mislead an audience into believing false facts, but
rather to “convey a certain impression of himself” [14].
All these types of false information are abundant on
the Web, and regardless of whether a fact is fabricated or
misrepresented on pur- pose or not, the effects it has on people’s lives
may be detrimental and even fatal, as in the case of medical lies [16,
20, 22, 30].
Hoaxes. This paper focuses on a specific kind of disinformation, namely hoaxes.
Wikipedia defines a hoax as “a deliberately fabri- cated falsehood made
to masquerade as truth.” The Oxford English Dictionary adds another
aspect by defining a hoax as “a humorous or mischievous deception” (italics ours).
We study hoaxes in the context of Wikipedia, for
which there are two good reasons: first, anyone can insert information
into Wiki- pedia by creating and editing articles; and second, as the
world’s largest encyclopedia and one of the most visited sites on the
Web, Wikipedia is a major source of information for many people. In
other words: Wikipedia has the potential to both attract and spread
false information in general, and hoaxes in particular.
The impact of some Wikipedia hoaxes has been
considerable, and anecdotes are aplenty. The hoax article about a fake
language called “Balboa Creole French”, supposed to be spoken on Balboa
Island in California, is reported to have resulted in “people com- ing
to [. . . ] Balboa Island to study this imaginary language” [38]. Some
hoaxes have made it into books, as in the case of the al- leged (but
fake) Aboriginal Australian god “Jar’Edo Wens”, who inspired a
character’s name in a science fiction book [10] and has been listed as a
real god in at least one nonfiction book [24], all before it came to
light in March 2015 that the article was a hoax. Another hoax (“Bicholim
conflict”) was so elaborate that it was of- ficially awarded “good
article” status and maintained it for half a decade, before finally being
debunked in 2012 [27].
The list of extreme cases could be continued, and
the popular press has covered such incidents widely. What is less
available, however, is a more general understanding of Wikipedia hoaxes
that goes beyond such cherry-picked examples.
Our contributions: impact, characteristics, and detection of Wikipedia hoaxes. This
paper takes a broad perspective by start- ing from the set of all hoax
articles ever created on Wikipedia and illuminating them from several
angles. We study over 20,000 hoax articles, identified by the fact that
they were explicitly flagged as potential hoaxes by a Wikipedia editor at
some point and deleted after a discussion among editors who concluded
that the article was
indeed a hoax. Some articles are acquitted as a consequence of that discussion, and we study those as well.
When answering a question on the Q&A site Quora
regarding the aforementioned hoax that had been labeled as a “good
article”, Wikipedia founder Jimmy Wales wrote that “[t]he worst hoaxes
are those which (a) last for a long time, (b) receive significant traffic
and (c) are relied upon by credible news media” [33]. Inspired by this
assessment, our first set of questions aims to understand how impactful
(and hence detrimental, by Wales’s reasoning) typ- ical Wikipedia hoaxes
are by quantifying (a) how long they last,
(b) how much traffic they receive, and (c) how
heavily they are cited on the Web. We find that most hoaxes have
negligible im- pact along all of these three dimensions, but that a
small fraction receives significant attention: 1% of hoaxes are viewed
over 100 times per day on average before being uncovered.
In the second main part of the paper, our goal is to
delineate typ- ical characteristics of hoaxes by comparing them to
legitimate arti- cles. We also study how successful (i.e., long-lived
and frequently viewed) hoaxes compare to failed ones, and why some
truthful ar- ticles are mistakenly labeled as hoaxes by Wikipedia
editors. In a nutshell, we find that on average successful hoaxes are
nearly twice as long as legitimate articles, but that they look less
like typical Wi- kipedia articles in terms of the templates, infoboxes,
and inter-ar- ticle links they contain. Further, we find that the “wiki-likeness”
of legitimate articles wrongly flagged as hoaxes is even lower than that
of actual hoaxes, which suggests that administrators put a lot of
weight on these superficial features when assessing the veracity of an
article.
The importance of the above features is intuitive,
since they are so salient, but in our analysis we find that less
immediately avail- able features are even more telling. For instance,
new articles about real concepts are often created because there was a
need for them, reflected in the fact that the concept is mentioned in
many other ar- ticles before the new article is created. Hoaxes, on the
contrary, are mentioned much less frequently before creation—they are about nonexistent concepts, after all—but
interestingly, many hoaxes still receive some mentions before being
created. We observe that such mentions tend to be inserted shortly
before the hoax is created, and by anonymous users who may well be the
hoaxsters themselves acting incognito.
The creator’s history of contributions made to
Wikipedia before a new article is created is a further major
distinguishing factor be- tween different types of articles: most
legitimate articles are added by established users with many prior
edits, whereas hoaxes tend to be created by users who register
specifically for that purpose.
Our third contribution consists of the application of these find- ings by building machine-learned
classifiers for a variety of tasks revolving around hoaxes, such as
deciding whether a given arti- cle is a hoax or not. We obtain good
performance; e.g., on a bal- anced dataset,
where guessing would yield an accuracy of 50%, we achieve 91%. To put
our research into practice, we finally find hoaxes that have not been
discovered before by running our classi- fier on Wikipedia’s entire
revision history.
Finally, we aim to assess how good humans are at
telling apart hoaxes from legitimate articles in a typical reading
situation, where users do not explicitly fact-check the
article by using a search en- gine, following up on references, etc. To
this end, we design and run an experiment involving human raters who are
shown pairs con- sisting of one hoax and one non-hoax and
asked to decide which one is the hoax by just inspecting the articles
without searching the Web or following links. Human accuracy on this
task is only 66% and is handily surpassed by our classifier, which
achieves 86% on the same test set. The reason is that humans are biased
to believe
Survival time
✶ ? 
✝
t
✝ t
Creation
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Patrol
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Flagging
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Deletion
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Figure 1: Life cycle of a Wikipedia hoax article.
After the ar- ticle is created, it passes through a human verification
process called patrol. The article survives until it is flagged as a hoax
and eventually removed from Wikipedia.
that well-formatted articles are
legitimate and real, whereas it is easy for our classifier to see through
the markup glitter by also con- sidering features computed from other
articles (such as the number of mentions the article in question
receives) as well as the creator’s edit history.
The remainder of this paper is structured as
follows. Sec. 2 out- lines the life cycle Wikipedia hoaxes go through
from creation to deletion. In Sec. 3, 4, and 5 we discuss the impact,
characteristics, and automated detection of hoaxes, respectively. The
experiment with human subjects is covered in Sec. 6. Related work is
summa- rized in Sec. 7; and Sec. 8 concludes the paper.
2. DATA: WIKIPEDIA HOAXES
The Wikipedia community guidelines define a hoax as
“an at- tempt to trick an audience into believing that something false
is real”, and therefore consider it “simply a more obscure, less obvi-
ous form of vandalism” [39].
A distinction must be made between hoax articles and hoax facts. The
former are entire articles about nonexistent people, entities, events,
etc., such as the fake Balboa Creole French language men- tioned in the
introduction.1 The latter are false facts
about exist- ing entities, such as the unfounded and false claim that
American journalist John Seigenthaler “was thought to have been directly
in- volved in the Kennedy assassinations” [40].
Finding hoax facts is technically difficult, as
Wikipedia provides no means of tagging precisely one fact embedded into a
mostly correct article as false. However, in order to find hoax
articles, it suffices to look for articles that were flagged as such at
some point. Hence we focus on hoax articles in this paper.
To describe the mechanism by which hoax articles are
flagged, we need to consider Wikipedia’s page creation process (schema-
tized in Fig. 1). Since January 2006 the privilege of creating new
articles has been limited to logged-in users (i.e.,
we know for each new article who created it). Once the article has been
created, it ap- pears on a special page that is monitored by trusted,
verified Wiki- pedians who attempt to determine the truthfulness of the
new article and either mark it as legitimate or flag it as suspicious by
pasting a template2 into the wiki markup text of the article.
This so-called patrolling process
(introduced in November 2007) works very promptly: we find that 80% of
all new articles are pa- trolled within an hour of creation, and 95%
within a day. This way many suspicious articles are caught and flagged
immediately at the source. Note that flagging is not restricted to patrol
but may hap-
1Occasionally users create
articles about existing unimportant en- tities and present them as
important, as in the case of a Scottish worker who created an article
about himself claiming he was a highly decorated army officer [37]. We
treat these cases the same way as fully fabricated ones: whether Captain
Sir Alan Mcilwraith never existed or exists but is in fact a Glasgow
call center employee does not make a real difference for all intents and
purposes.
Fractionflaggedwithindayst
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0.851.000.950.90
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minute
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hour
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day
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week month
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year
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survivingProb.moredaysofd
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0.0 0.60.21.00.80.4
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Time t between patrolling & flagging
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Days t already survived
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(a)
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(b)
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Figure 2: (a) Cumulative distribution function (CDF)
of hoax survival time. Most hoaxes are caught very quickly. (b) Time
the hoax has already survived on x-axis; probability of surviv- ing d more days on y-axis (one curve per value of d). Dots in bot- tom left corner are prior probabilities of surviving for d days.
pen at any point during the lifetime of the article.
Once flagged, the article is discussed among Wikipedians and, depending
on the ver- dict, deleted or reinstituted (by removing the hoax
template). The discussion period is generally brief: 88% of articles
that are even- tually deleted are deleted within a day of flagging, 95%
within a week, and 99% within a month. We define the survival time of a hoax as the time between patrolling and flagging (Fig. 1).
In this paper we consider as hoaxes all articles of
the English Wikipedia that have gone through this life cycle of
creation, patrol, flagging, and deletion. There are 21,218 such articles.
3.REAL-WORLD IMPACT OF HOAXES
Disinformation is detrimental if it affects many
people. The more exposure hoaxes get, the more we should care about
finding and removing them. Hence, inspired by the aforementioned Jimmy
Wales quote that “[t]he worst hoaxes are those which (a) last for a long
time, (b) receive significant traffic, and (c) are relied upon by
credible news media” [33], we quantify the impact of hoaxes with respect
to how long they survive (Sec. 3.1), how often they are viewed (Sec.
3.2), and how heavily they are cited on the Web (Sec. 3.3).
3.1Time till discovery
As mentioned in Sec. 2, since November 2007 all
newly created articles have been patrolled by trusted editors. Indeed,
as shown by Fig. 2(a), most of the hoaxes that are ever discovered are
flagged immediately at the source: e.g., 90%
are flagged within one hour of (so basically, during) patrol. Thereafter,
however, the detection rate slows down considerably (note the
logarithmic x-axis of Fig. 2(a)):
it takes a day to catch 92% of eventually detected hoaxes, a week to
catch 94%, a month to catch 96%, and one in a hundred survives for more
than a year.
Next we ask how the chance of survival changes with
time. For this purpose, Fig. 2(b) plots the probability of surviving for
at least t + d days, given that the hoax has already survived for t days, for d =
1, 30, 100, 365. Although the chance of surviving the first day is very
low at only 8% (Fig. 2(a)), once a hoax has survived that day, it has a
90% chance of surviving for at least another day, a 50% chance of
surviving for at least one more month, and an 18% chance of surviving
for at least one more year (up from a prior probability of only 1% of
surviving for at least a year). After this, the survival probabilities
keep increasing; the longer the hoax has already survived, the more
likely it becomes to stay alive.
In summary, most hoaxes are very short-lived, but those that survive patrol have good odds of staying in Wikipedia for much
Frac. w/ at least n views perday
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0.001 0.010 0.100 1.000
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Non−hoaxes
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Frac w/ at least n incominglinks
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100
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Hoaxes
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500
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Number n of active incoming links
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Number n of pageviews per day
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(a)
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(b)
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Figure 3: CCDFs of (a) number of pageviews for hoaxes and non-hoaxes (14% of hoaxes get over 10 pageviews per day dur- ing their lifetime) and (b) number of active inlinks from Web.
longer. There is a relatively small number of
longevous hoaxes, but as we show later, these hoaxes attract significant
attention and a large number of pageviews.
3.2 Pageviews
Next we aim to assess the impact of Wikipedia hoaxes
by study- ing pageview statistics as recorded in a dataset published by
the Wikimedia Foundation and containing, for every hour since De-
cember 2007, how often each Wikipedia page was loaded during that hour
[36].
We aggregate pageview counts for all hoaxes by day
and nor- malize by the number of days the hoax survived, thus obtaining
the average number of pageviews received per day between patrolling and
flagging. Since this quantity may be noisy for very short sur- vival
times, we consider only hoaxes that survived for at least 7 days.3 This leaves us with 1,175 of the original 21,218 hoaxes.
The complementary cumulative distribution function
(CCDF) of the average number of pageviews per day is displayed as a red
line in Fig. 3(a). As expected, we are dealing with a heavy-tailed dis- tribution: most hoaxes are rarely viewed (median 3 views per day; 86% get fewer than 10 views per day), but a non-negligible num- ber get a lot of views; e.g., 1% of hoaxes surviving for at least a week get 100 or more views per day on average. Overall, hoaxes are viewed less than non-hoaxes,
as shown by the black line in Fig. 3(a) (median 3.5 views per day; 85%
get fewer than 10 views per day; for each hoax, we sampled one random non-hoax created on the same day as the hoax).
The facts that (1) some hoaxes survive much longer
than oth- ers (Fig. 2(a)) and (2) some are viewed much more frequently
per day than others (Fig. 3(a)) warrant the hypothesis that hoaxes might
have a constant expected total number of pageviews until they are
caught. This hypothesis would predict that plotting the total life- time
number of pageviews received by hoaxes against their survival times
would result in a flat line. Fig. 4(a) shows that this is not the case,
but that, instead, the hoaxes that survive longer also receive more
pageviews.4
3To avoid counting
pageviews stemming from patrolling and flag- ging, we start counting days
24 hours after the end of the day of patrolling, and stop counting 24
hours before the start of the day of flagging.
4It could be objected that
this might be due to a constant amount of bot traffic per day (which is
not excluded from the pageview dataset we use). To rule this out, we
assumed a constant number b of bot hits per
day, subtracted it (multiplied with the survival time) from each hoax’s
total count, and repeated Fig. 4(a) (for various values of b).
We still observed the same trend (not plotted for space reasons), so we
conclude that Fig. 4(a) is not an artifact of bot traffic.
100 10000
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1000
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Survival time [days]
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(a)
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(b)
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Figure 4: Longevous hoaxes are (a) viewed more over their lifetime (gray line y = x plotted for orientation; not a fit) and
(b) viewed less frequently per day on average (black line: lin- ear-regression fit).
Finally, when plotting survival times against per-day
(rather than total) pageview counts (Fig. 4(b)), we observe a negative
trend (Spearman correlation −0.23). That is, pages that survive for very
long receive fewer pageviews per day (and vice versa).
Together we conclude that, while there is a slight
trend that hoaxes with more daily traffic generally get caught faster
(Fig. 4(b)), it is not true that hoaxes are caught after a constant
expected number of pageviews (Fig. 4(a)). It is not the case that only
obscure, practi- cally never visited hoaxes survive the longest;
instead, we find that some carefully crafted hoaxes stay in Wikipedia for
months or even years and get over 10,000 pageviews (24 hoaxes had over
10,000 views, and 375 had over 1,000 views).
3.3References from the Web
Next we aim to investigate how different pages on
the Web link and drive traffic to the hoax articles. While in principle
there may be many pages on the Web linking to a particular Wikipedia
hoax, we focus our attention on those links that are actually traversed
and bring people to the hoax. To this end we utilize 5 months’ worth of
Wikipedia web server logs and rely on the HTTP referral informa- tion to
identify sources of links that point to Wikipedia hoaxes.
In our analysis we only consider the traffic received by the hoax during the time it was live on Wikipedia, and not pre-creation or post-deletion
traffic. There are 862 hoax articles that could poten- tially have
received traffic during the time spanned by the server logs we use. We
filter the logs to remove traffic that may have been due to article
creation, patrol, flagging, and deletion, by re- moving all those
requests made to the article during a one-day pe- riod
around these events. This gives us 213 articles, viewed 23,353 times in
total. Furthermore, we also categorize the different sources of requests
into five broad categories based on the referrer URL: search engines,
Wikipedia, social networks (Facebook and Twit- ter), Reddit, and a
generic category containing all others. We de- fine all search engine
requests for an article as representing a single inlink. For the other
categories, the inlink is defined by the URL’s domain and path portions.
We show the CCDF of the number of inlinks for the hoax articles in Fig.
3(b). On average, each hoax article has 1.1 inlinks. Not surprisingly,
this distribution is heavily skewed, with most articles having no
inlinks (median 0; 84% hav- ing at most one inlink). However, there is a
significant fraction of articles with more inlinks; e.g., 7% have 5 or more inlinks.
Table 1 gives the distribution of inlinks from
different sources. Among the articles that have at least one inlink,
search engines, Wikipedia, and “others” are the major sources of inbound
connec- tions, providing 35%, 29%, and 33% of article inlinks on
average.
Metric
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SE
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Wiki
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SN
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Reddit
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Others
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Average inlinks
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0.78
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2.1
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0.08
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0.15
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1.3
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Median inlinks
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1
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1
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0
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0
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1
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Inlinks per article
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35%
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29%
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0.6%
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3%
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33%
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Table 1: Number of inlinks per hoax article (“SE” stands for search engines, “SN” for social networks).
These hoax articles have 2.1 inlinks from Wikipedia and 1.3 from “other” sources on average.
Overall, the analysis indicates that the hoax
articles are accessi- ble from multiple different locations, increasing
the chances that they are viewed. Moreover, hoaxes are also frequently
reached through search engines, indicating easy accessibility.
4. CHARACTERISTICS OF SUCCESSFUL HOAXES
In the present section we attempt to elicit typical
characteristics of Wikipedia hoaxes. In particular, we aim to gain a
better under- standing of (1) how hoaxes differ from legitimate
articles, (2) how successful hoaxes differ from failed hoaxes, and (3)
what features make a legitimate article be mistaken for a hoax.
To this end we compare four groups of Wikipedia articles in a descriptive analysis:
1.Successful hoaxes passed
patrol, survived for significant time (at least one month from creation
to flagging), and were fre- quently viewed (at least 5 times per day on
average).
2.Failed hoaxes were flagged and deleted during patrol.
3.Wrongly flagged articles were temporarily flagged as hoaxes, but were acquitted during the discussion period and were hence not deleted.
4.Legitimate articles were never flagged as hoaxes.
The set of all successful hoaxes consists of 301
pages created over a period of over 7 years. The usage patterns and
community norms of Wikipedia may have changed during that period, and we
want to make sure to not be affected by such temporal variation. Hence
we ensure that the distribution of creation times is identical across
all four article groups by subsampling an equal number of articles from
each of groups 2, 3, and 4 while ensuring that for each successful hoax
from group 1 there is another article in each group that was created on
the same day as the hoax.
Given this dataset, we investigate commonalities
and differences between the four article groups with respect to four
types of fea- tures: (1) Appearance features (Sec. 4.1) are properties
of the arti- cle that are immediately visible to a reader of the
article. (2) Net- work features (Sec. 4.2) are derived from the so-called ego network formed by the other articles linked from the article in question.
(3) Support features (Sec. 4.3) pertain to mentions
of the consid- ered article’s title in other articles. (4) Editor
features (Sec. 4.4) are obtained from the editor’s activity before
creating the article in question.
4.1 Appearance features
We use the term appearance features to refer to characteristics of an article that are directly visible to a reader.
l
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least
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(a)
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(b)
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Figure 5: CCDFs of appearance features; means and medians in brackets.
Fig. 5(a) demonstrates that successful hoaxes are
particularly long: their median number of content words is 134 and thus
nearly twice as large as the median of legitimate articles (71).
Further, and maybe surprisingly, failed hoaxes are the second most
verbose group: with a median of 105 words, they are nearly 50% longer
than legitimate articles.
Fig. 5(b) reveals striking differences between
article groups. On one extreme, legitimate articles contain on average
58% plain text. On the other extreme, failed hoaxes consist nearly
entirely of plain text (92% in the mean). Successful hoaxes and wrongly
flagged articles take a middle ground.
This suggests that embellishing a hoax with markup
increases its chances of passing for legitimate and that, conversely,
even le- gitimate articles that do not adhere to the typical Wikipedia
style are likely to be mistaken for hoaxes. It is not so much the amount
of bare-bones content that matters—wrongly flagged articles (me- dian 81 words; Fig. 5(a)) are similarly long to unflagged legitimate articles (median 71)—but rather the amount of mixed-in markup.
While the number of wiki links is similar for
legitimate articles and hoaxes, we saw previously that successful hoaxes
are nearly twice as long as legitimate articles on average. Hence
another in- teresting measure is the density of wiki links, defined here
as the number of wiki links per 100 words (counted before markup strip-
ping because wiki links may be embedded into markup such as templates).
Under this measure the picture changes: as evident
in Fig. 6(b), successful hoaxes have significantly fewer outlinks per 100
words than legitimate articles (medians 5 vs. 7).
Wrongly flagged articles (median 2) look again more like hoaxes than
legitimate articles, which is probably a contributing factor to their
being suspected to be hoaxes.
Frac.w/ at least n mentions
|
1.0
|
Legit.
|
1.0
|
1.0
|
Legit.
|
|||
0.8
|
0.8
|
Wrongly fl.
|
||||||
0.8
|
(10,364)
|
|||||||
Succ. hoax
|
||||||||
Wrongly fl.
|
Failed hoax
|
|||||||
0.6
|
(134,18702)
|
0.6
|
0.6
|
|||||
IP
|
||||||||
Succ. hoax
|
Article
|
|||||||
0.4
|
(2,70)
|
0.4
|
0.4
|
Creator
|
||||
Failed hoax
|
||||||||
0.2
|
(3,222)
|
0.2
|
0.2
|
|||||
0.0
|
withincreatedFrac.t
|
createdmentionFirstby
|
||||||
101
|
102
|
0.0
|
Hour
|
Day
|
0.0
|
|||
100
|
MonthYear
|
|||||||
Number of prior mentions n
|
Time since first mention t
|
|||||||
(a) Prior mentions (b) 1st prior mention (c) 1st-men. creator
Figure 7: Support features: (a) CCDF of number of
mentions prior to article creation (means/medians in brackets). (b) CDF
of time from first prior mention to article creation. (c) Proba- bility
of first prior mention being inserted by hoax creator or anonymous user
(identified by IP address), respectively.
4.2 Link network features
Above we treated features derived from embedded
links as ap- pearance features, since the links are clearly visible to a
reader of the article. But they are at the same time features of the
hyper- link network underlying Wikipedia. While outlinks constitute a first-order
network feature (in the sense that they deal only with di- rect
connections to other articles), it is also interesting to consider higher-order
network features, by looking not only at what the arti- cle is
connected to, but also how those connected articles are linked amongst
each other.
Fig. 6(d) shows that legitimate articles tend to
have larger clus- tering coefficients than successful hoaxes, which
implies that their outlinks are more coherent. It appears to be difficult
to craft a fake concept that is embedded into the network of true
concepts in a re- alistic way. In other words, making an article look
realistic on the surface is easy; creating a realistic network
fingerprint is hard.
As an aside, Fig. 6(d) is stratified by ego-network size because otherwise clustering coefficient and ego-network
size could be con- founded, as shown by the negative trend: when an
article links to many other articles, they tend to be less tightly
connected than when it links to only a few selected other articles—akin to a preci- sion/recall tradeoff.
4.3 Support features
Something completely fabricated should never have
been referred to before it was invented. Therefore we expect the
frequency with which an article’s name appears in other Wikipedia
articles before it is created to be a good indicator of whether the
article is a hoax.
Number of prior mentions. To
test this hypothesis, we process Wikipedia’s entire revision history
(11 terabytes of uncompressed text) and, for each article A included in one of our four groups, identify all revisions from before A’s creation time that contain A’s title as a substring.
n wiki links
|
0.200
|
||||||
w/ at least
|
0.020
|
Legitimate (20, 12)
|
|||||
Wrongly flagged (14, 3)
|
|||||||
Frac.
|
0.002
|
Successful hoax (25, 11)
|
|||||
Failed hoax (5, 0)
|
|||||||
1
|
2
|
5
|
10
|
20
|
50
|
100
|
|
Number n of outgoing wiki links
|
|||||||
(a) Number of wiki links
w density
|
1.0
|
Legit.(9,7)
|
w density
|
1.0
|
Legit. (.6,0)
|
|||||||
0.8
|
0.8
|
|||||||||||
Wrongly fl.
|
Wrongly fl.(.38,0)
|
|||||||||||
Succ. hoax (.35,0)
|
||||||||||||
0.6
|
(5,2)
|
0.6
|
||||||||||
least
|
Succ. hoax
|
least
|
Failed hoax (.1,0)
|
|||||||||
0.4
|
(6,5)
|
0.4
|
||||||||||
at
|
at
|
|||||||||||
Failed hoax
|
||||||||||||
w/
|
w/
|
|||||||||||
0.2
|
(2,0)
|
0.2
|
||||||||||
Frac.
|
Frac.
|
|||||||||||
0.0 0
|
0.0 0
|
|||||||||||
5
|
10
|
15
|
20
|
25
|
30
|
1
|
2
|
3
|
||||
(b)
|
(c)
|
coefficient
|
0.3 0.4
|
Legitimate
|
|
Successful hoax
|
|||
Local clustering
|
0.0 0.1 0.2
|
||
(10,20]
|
(20,30]
|
(30,40]
|
|
Size of ego network
(d) Ego-net clustering coefficient
Figure 6: Link characteristics: CCDFs (means/medians in brackets) of (a) number of wiki links, (b) wiki-link density, and (c) Web- link density. (d) Ego-network clustering coefficient as function of ego-network
size (nodes of outdegree at most 10 neglected because clustering
coefficient is too noisy for very small ego networks; nodes of outdegree
above 40 neglected because they are very rare).
Of course, such a crude detector is bound to produce false posi- tives.5 But since the false-positive
rate is likely to be similar across groups of articles, it is
nonetheless useful for comparing different groups in a relative fashion,
as done in Fig. 7(a), which shows that the two types of non-hoaxes (wrongly flagged and unflagged, i.e.,
legitimate) have very similar distributions of prior mentions; anal-
ogously, the two types of hoaxes (successful and failed) resemble each
other. One important difference between successful and failed hoaxes,
however, is that of the successful ones, 40% are mentioned in at least
one other article before creation, whereas this is the case for only 20%
of the failed ones. (At 60% the rate is much higher for non-hoaxes.)
Time of first prior mention. Part of the reason why so many hoaxes have a mention before creation is due to the aforementioned false-positive
rate of our simplistic mention detector. But there is a second reason:
smart hoaxsters may carefully prepare the environ- ment for the launch
of their fabrication by planting spurious men- tions in other articles,
which creates an illusion of external support.
Consider Fig. 7(b), which plots the cumulative
distribution func- tion of the time between the appearance of the first
mention of an article A in some other article and the creation of A itself.
Legiti- mate articles are referred to long before they are created: 75%
have been mentioned for over a year by the time the article is created,
and under 5% have been mentioned for less than an hour. Suc- cessful
hoaxes, on the contrary, have a probability of only 35% of having been
mentioned for over a year when the hoax is created,6 and a probability of 24% of having been mentioned for less than an hour—up by a factor of about 5 compared to non-hoaxes.
We suspect that it is in many cases the hoaxster herself who inserts
the first mention so briefly before creating the hoax in order to lend it
artificial support.
Creator of first prior mention. Looking
for additional evidence for this hypothesis, we explicitly investigate
who is responsible for the first mention. To this end, Fig. 7(c) plots
the fraction of first mentions made by the article creator herself.
(Recall from Sec. 2 that we always know which user created an article,
since anony- mous users do not have permission to create new articles.)
We expected most hoaxes to have been first mentioned by the hoaxster
5For instance, a mention of the newspaper The Times will be spu- riously detected in the Bob Dylan article because it mentions the song The Times They Are a-Changin.
6This number is much
larger for failed hoaxes, which begs an ex- planation. Eyeballing the
data, we conjecture that this is caused by obvious, failed hoaxes often
being created with mundane and com- monplace names, such as “French
immigrants” or “Texas style”.
herself, but inspecting the figure we see that this
is not the case: the fraction of first mentions inserted by the article
creator is only slightly larger for hoaxes than for non-hoaxes (21% vs. 19%).
It seems that hoaxsters are smarter than that: Fig. 7(c) also tells us that 45% of first mentions are introduced by non-logged-in
users identified only by their IP address, whereas the baseline over le-
gitimate articles is only 19% here. Hence it seems likely that the
anonymous user adding the first mention is often the hoaxster her- self
acting incognito, in order to leave no suspicious traces behind.
We conjecture that a significant fraction of first mentions from logged-in
users other than the hoaxsters in fact stem from the hoax- sters, too,
via fake “sockpuppet” accounts, but we have no means of verifying this
hypothesis.
4.4 Editor features
The evidence from the last subsection that hoaxsters
may act un- dercover to lend support to their fabricated articles
motivates us to take a broader look at the edit histories of article
creators.
Number of prior edits and editor age. Consider
Fig. 8, where we explore article creators’ edit histories under two
metrics: the time gone by since the user registered on Wikipedia (Fig.
8(a)) and the number of edits they have made prior to creating the
article in question (Fig. 8(b)).
The originators of typical legitimate articles are established mem- bers of the Wikipedia community: three-quarters
of all such articles were started by editors who have been registered
for more than a year, with a median of over 500 prior edits.7 On
the contrary, the three groups of articles that are flagged as hoaxes
(whether they re- ally are hoaxes or not) are created by much more
recent accounts, in the following order: failed-hoax
authors are the youngest mem- bers, followed by the creators of
successful hoaxes, and finally by those of articles flagged wrongly as
hoaxes.
In particular, while only about 3% of legitimate-article
authors create the article within the hour of registration, the
fractions are 60% for creators of failed hoaxes, and 25% for those of
successful hoaxes and wrongly flagged articles. In the case of wrongly
flagged articles, we suspect that inexperience may cause users to write
ar- ticles that do not comply with Wikipedia’s standards (cf. Fig.
5). This in combination with the concern that, due to the recent reg-
istration date, the account might have been created specifically for
creating the hoax might lead patrollers to erroneously suspect the new
article of having been fabricated.
7In order to limit the
number of calls to the Wikipedia API, we col- lected at most 500 edits
per user. Therefore, the median measured in this setting (500) is a
lower bound of the real median.
h
|
1.0
|
||||||
Frac. of articles written within
|
Frac. w/ at least n prior edits
|
1.0
|
|||||
0.8
|
0.8
|
Legit.
|
|||||
0.6
|
Wrongly fl.
|
||||||
0.6
|
Succ. hoax
|
||||||
Failed hoax
|
|||||||
0.4
|
0.4
|
||||||
0.2
|
0.2
|
||||||
0.0
|
Day
|
MonthYear
|
0.0
|
101
|
102
|
||
Hour
|
100
|
Time since registration h
|
Number of prior edits n
|
(a) Time since registration
|
(b) Number of prior edits
|
Figure 8: Editor features: (a) CDF of time between
account registration and article creation. (b) CCDF of number of edits
by same user before article creation.
Feature
|
Group
|
Appearance (Sec. 4.1)
|
|
Appearance
|
|
Appearance
|
|
Appearance
|
|
Network (Sec. 4.2)
|
|
Number of prior mentions
|
Support (Sec. 4.3)
|
Time of first prior mention
|
Support
|
Creator of first prior mention
|
Support
|
Number of prior edits
|
Editor (Sec. 4.4)
|
Editor age
|
Editor
|
Table 2: Features used in the random-forest classifiers.
5.AUTOMATIC HOAX DETECTION
Having gained several valuable insights on the
characteristics of Wikipedia hoaxes and their differences from other
types of arti- cles, we are now in a position to apply these findings by
building machine-learned classifiers to automate some important decisions revolving around hoaxes. We consider the following four tasks:
1.Will a hoax get past patrol?
2.How long will a hoax survive?
3.Is an article a hoax?
4.Is an article flagged as such really a hoax?
The first two tasks take the hoaxster’s perspective
and ask how high the chances are of the hoax being successful. The
latter two tasks take the patrollers’ perspective and aim to help them
make an accurate decision during patrol and after.
All classifiers use the same algorithm and features,
but are fitted on different training sets of positive and negative
examples. This allows us to analyze the fitted weights in order to
understand what features matter most in each task.
Classification algorithm. We experimented with a variety of clas- sification algorithms—logistic regression, support vector machines, and random forests—and found the latter to work best. Hence all results reported here were obtained using random forests [4].
We use balanced training and test sets containing
equal numbers of positive and negative examples, so random guessing
results in an accuracy, as well as an area under the receiver operating
character- istic (ROC) curve (AUC) of 50%.
Features. All features used by the classifier have been discussed in detail in Sec. 4 and are summarized in Table 2.
In the rest of this section we provide more details
on each of the four tasks (Sec. 5.1) and then move on to presenting and
discussing the results we obtained (Sec. 5.2).
5.1 Classification tasks
Task 1: Will a hoax get past patrol? Here the objective is to predict if a hoax will pass the first hurdle in its life cycle (Fig. 1), i.e., if it will manage to trick the patroller into believing that it is a legitimate article.
Such a classifier could tell the hoaxster whether
the hoax is ready to be submitted to the patrolling process yet. It
would also be useful from the patroller’s perspective because the fitted
feature weights can give us insights into which features make a hoax
slip through patrol; we could then counteract by scrutinizing those
characteris- tics more carefully.
Here the set of positive examples consists of all
2,692 hoaxes that were not flagged by the users who patrolled them. The
negative examples are sampled randomly from the set of 12,901 hoaxes
that are correctly flagged by the patroller, while ensuring that for each
positive article we have a negative article created on the same day.
Task 2: How long will a hoax survive? Our
second task is to pre- dict the survival time of hoaxes that have
managed to pass patrol, defined as the time between patrol and flagging
(Fig. 1). We phrase this as a binary decision problem by fixing a
threshold τ and asking whether a hoax will survive for at least τ minutes. We repeat this task for various values of τ, ranging from one minute to one year.
Given τ, the positive examples are all hoaxes that survived for at least τ minutes from patrol to flagging. The negative set consists of hoaxes flagged within τ minutes from patrol. The larger of the two sets for the given τ is subsampled to match the smaller set in size.
Task 3: Is an article a hoax? In
this task, the classifier is supposed to assess if an article that has
passed patrol is a hoax or not. In the language of Fig. 1, the task aims
to automate the flagging step. This classifier could be employed to double-check the decisions made by human patrollers and thereby decrease their false-negative rate.
Here the positive examples are the 2,692 articles
that passed pa- trol but were later flagged as hoaxes and deleted. As
argued in the introduction, the most detrimental hoaxes are those that
survive for a long time and attract significant traffic. In order to equip
our clas- sifier with the ability to detect this subclass, we include
only those 301 hoaxes as positive examples that have existed for at
least 30 days from creation to flagging and that have received an average
of at least 5 pageviews during this time. For each hoax in the positive
set we randomly sample one negative example from among all ar- ticles
that were created on the same day as the hoax and were never flagged or
deleted.
Task 4: Is an article marked as such really a hoax? The
final classification task deals with the scenario in which an article has
been flagged as a hoax by a Wikipedia user, and our goal is to double-check
if the article is indeed a hoax. That is, this classifier is supposed to
act as a safeguard between the flagging and deletion steps (Fig. 1).
In other words, while task 3 aims to decrease human patrollers’ false-negative rate, the classifier developed here may decrease their false-positive
rate. This could be very valuable because false posi- tives come at a
large cost: if an article is unjustly deleted as a hoax, this might
discourage the editor to contribute further to Wikipedia.
The negative set comprises the 960 articles that were wrongly flagged, i.e.,
that were later acquitted by having the hoax flag re- moved and were
never deleted. Candidates for positive examples are all articles that
were flagged as hoaxes and eventually deleted. To create a balanced
dataset, we pair each negative example with a positive examples whose
creation and flagging dates are closely aligned with those of the
negative example (we use propensity score matching [31] to perform the
pairing).
Average AUC
1.0
|
Will a hoax get past patrol?
|
1.0
|
Is an article a hoax?
|
1.0
|
||||||||
+Appearance
|
+Support
|
|||||||||||
0.9
|
Appearance
|
+Editor
|
+Network
|
+Support
|
Average AUC
|
0.9
|
+Network
|
0.9
|
||||
Average AUC
|
||||||||||||
0.8
|
0.8
|
Editor
|
0.8
|
|||||||||
0.7
|
0.7
|
0.7
|
||||||||||
0.6
|
AUC with all
|
0.6
|
AUC with all
|
0.6
|
||||||||
0.5
|
features = 72%
|
0.5
|
features = 98%
|
0.5
|
||||||||
Is an article marked as such really a hoax?
Editor
|
+Support
|
+Appearance
|
+Network
|
Average AUC
|
AUC with all
|
||||
features = 86%
|
0.80
|
Survival time of hoax
|
|||
0.75
|
||||
0.70
|
||||
0.65
|
||||
0.60
|
||||
0.55
|
||||
0.50
|
||||
Minute
|
Hour
|
Day
|
Month
|
Year
|
t: Split value of survival time of hoax
|
||||
(a) Task 1
|
(b) Task 3
|
(c) Task 4
|
(d) Task 2
|
Figure 9: (a–c) Results of forward feature selection for tasks 1, 3, 4. (d) Performance (AUC) on task 2 as function of threshold τ.
5.2Results
Table 3 reports the performance on tasks 1, 3, and 4 when using all features of Table 2. Task 2 depends on the threshold τ, so we plot the AUC as function of τ in Fig. 9(d).
Task
|
Acc.
|
AUC
|
|
1
|
Will a hoax get past patrol?
|
66%
|
71%
|
3
|
Is an article a hoax?
|
92%
|
98%
|
4
|
Is an article flagged as such really a hoax?
|
76%
|
86%
|
Table 3: Classification results; for task 2, cf. Fig. 9(d).
Maybe surprisingly, deciding if an article is a
hoax or not (task 3) is the easiest task, with an accuracy (AUC) of 92%
(98%). Perfor- mance is also quite high on the task of deciding whether
some- thing that has been flagged as a hoax is really one (task 4); here
we achieve an accuracy (AUC) of 76% (86%). The hardest tasks are to
predict if a hoax will pass patrol (task 1; accuracy 66%, AUC 71%) and
how long it will survive once it has passed patrol (task 2): Fig. 9(d)
shows that the AUC increases with the threshold τ, but levels off at 75% around τ =
1 day. That is, one day seems to be a natural threshold that separates
successful from failed hoaxes. This echoes our finding from Fig. 2(b),
where we saw that surviving the first day immensely boosts the odds of
surviving for longer.
Feature importance. In
order to understand which features are important for which task, we
evaluate smaller models that consist of only one of the four feature
groups (Table 2). The performance of these smaller models is shown by
the vertically aligned dots in the leftmost columns of Fig. 9(a)–9(c).
For tasks 3 and 4, which deal with deciding if something is a hoax,
features of the creator’s edit history are most effective; on task 3
(hoax vs. non-hoax), the network feature (ego-network
clustering coefficient) does equally well. Task 1, where we predict if a
given hoax will pass patrol, profits most from appearance and editor
features.
Next, we perform forward feature selection to
understand what the marginal values of additional features are. The
results are plot- ted as the black curves in Fig. 9(a)–9(c).8 The conclusion is that all feature groups contribute their share, but with diminishing returns.
Trawling Wikipedia for hoaxes. In order to find hoaxes that are still present in Wikipedia, we deployed the hoax-vs.-non-hoax clas- sifier on Wikipedia’s entire revision history. We discuss the results in detail online.9 To give but two examples, our algorithm identi- fied the article about “Steve Moertel”, an alleged Cairo-born U.S.
8We performed forward
feature selection on the training set and report performance on the
testing set. This is why the first selected feature may have lower
performance than other features.
9http://snap.stanford.edu/hoax/
popcorn entrepreneur, as a hoax. The article was
deleted by an edi- tor who confirmed the article’s hoax status after we
had flagged it— and after it had survived in Wikipedia for 6 years and 11
months. Similarly, we flagged the article about “Maurice Foxell”, an
alleged children’s book author and Knight Commander of the Royal Victo-
rian Order; the article was deleted by an editor after it had survived
for 1 year and 7 months.
6. HUMAN GUESSING EXPERIMENT
The observational analysis of Sec. 4 allowed us to
gain many insights, but it also has some shortcomings. First, survival
time defined by the period between patrol and flagging is not a perfect
indicator of the quality of a hoax, as the hoax may have survived for a
long time for a variety of reasons; e.g., it
may be the case that the false information is disguised in a truly
skillful manner, or simply that it was sloppily patrolled and was
afterwards seen by only few readers who could have become suspicious. So
by only considering the observational data we have analyzed above, we
cannot know which hoax survived for which reason.
Second, the binary label whether a hoax passed
patrol or not is not necessarily representative of how likely a regular
Wikipe- dia reader, rather than a patroller, would be to believe the
hoax. Patrollers are encouraged to base their decision on all available
in- formation, including fact-checking on the Web via
search engines, verifying included references, inspecting the article
creator’s edit history, etc. We suspect that most Wikipedia readers do
not use such devices during casual reading and are therefore more likely
to fall prey to a hoax that looks legitimate on the surface.
To overcome these shortcomings and understand what
makes a hoax credible to average readers rather than patrollers, we now
complement our observational findings with an experiment. The idea is to
(1) create an identical situation of scrutiny across a va- riety of
hoaxes, thus mitigating the first concern from above, and
(2) disallow the use of external resources such as search engines, thus addressing the second concern.
6.1 Methodology
In designing the experiment, we start by selecting
64 successful hoaxes according to the definition from the beginning of
Sec. 4. We then create an equally sized set of legitimate, non-hoax
articles such that (1) for each hoax we have a legitimate article
created on the same day as the hoax and (2) the two sets have nearly
identi- cal distributions of the appearance features of Sec. 4.1, which
we achieve via propensity score matching [31].10
We then created 320 random hoax/non-hoax pairs such that each hoax was paired with 5 distinct non-hoaxes and vice versa. These
10We additionally balance the sets with respect to the numbers of sections, images, and references in the articles.
1
|
4
|
0.4
|
||||||||
2
|
||||||||||
0.2
|
||||||||||
0
|
0
|
0.0
|
||||||||
−1
|
−2
|
−0.2
|
||||||||
−4
|
−0.4
|
|||||||||
Diff. Suspected Diff. Actual
|
Diff. Suspected Diff. Actual
|
Diff. Suspected Diff. Actual
|
||||||||
(a)
|
(b)
|
(c)
|
||||||||
Figure 10: Human bias in the guessing experiment with respect to three appearance features f. Left boxes: difference δ of sus- pected hoax minus suspected non-hoax. Right boxes: differ- ence δ∗ of actual hoax minus actual non-hoax.
pairs were then shown side-by-side in
random order to human raters on Amazon Mechanical Turk, who were asked
to decide which of the two articles is a hoax by only looking at the
text and not search- ing the Web. Each pair was given to 10 raters, so
we collected 3,200 labels in total (50 per hoax). We assured the quality
of raters as described in the appendix.
6.2Results
Human vs. classifier accuracy. Human accuracy on all rated pairs is 66%. The macro-average
that gives equal weight to all users (hoaxes) is 63% (66%). Given that
random guessing on the task would give 50%, this performance is
surprisingly weak.11 In com- parison, we tested our hoax-vs.-non-hoax
classifier (task 3 of Sec. 5) on the same pairs shown to humans and
achieved an accuracy of 86%, thus outperforming humans by a large
margin.12
This classifier used all features of Sec. 5. The
human, however, saw only the articles themselves and was not allowed
(and for most features not even able to) take network, support, and
editor features into account. To allow for a fairer comparison, we
therefore also tested a version of our classifier that uses only
appearance features, obtaining an accuracy of only 47%. This weak
(roughly random) performance is to be expected, since the sets of hoaxes
and non- hoaxes were constructed to have very similar distributions
with re- spect to appearance features (cf. above), so these features should be uninformative for the task.
We conclude that features that look beyond the
surface, such as the article creator’s edit history, the mentions
received from other articles, and the density of the article’s ego
network, are of crucial importance for deciding whether an article is a
hoax: they make the difference between random and above-human performance.
Human bias. Our next goal is to understand what factors hu- mans go by when deciding what is a hoax. We proceed as follows: given a feature f of interest (such as plain-text length), compute the within-pair difference δ of the suspected hoax minus the sus- pected non-hoax for each pair. Similarly, compute the difference δ∗ of the actual hoax minus the actual non-hoax, and compare the distributions of δ and δ∗. Now, if δ tends to be lower than δ∗, this implies that humans tend to think that lower values of f indicate
hoaxes, although they would have had to choose the higher values more
frequently in order to guess perfectly; in other words, they are biased
to believe that articles with lower values of f are hoaxes.
11One might object that humans possibly did guess randomly, but we guarded against this via the quality-assurance mechanism de- scribed in the appendix.
12Since
testing is done on pairs, we also trained the classifier on pairs: as
the feature vector for a pair, we use the difference of the feature
vectors of the left and right articles, and the classifier is tasked to
predict whether the left or right article is the hoax. The
training pairs did not contain articles appearing in the test pairs.
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Easy hoaxes Hard hoaxes
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Figure 11: Comparison of easy- and hard-to-identify hoaxes with respect to three appearance features.
Our findings from this analysis are displayed in the
boxplots of Fig. 10. Here, the left box of each subfigure summarizes the
distri- bution of δ, and the right box, that of δ∗. For instance, Fig. 10(a) shows that the suspected hoax tends to be shorter than the sus- pected non-hoax, whereas the actual hoax tends to be longer than the actual non-hoax.
So humans have a bias towards suspecting short articles to be hoaxes
that is not warranted by the dataset at hand. Similarly, we find that
humans are led to believe that articles with a lower wiki-link density (Fig. 10(b)) and, to a lesser extent, with a higher plain-text-to-markup ratio (i.e.,
less wiki markup; Fig. 10(c)), are hoaxes. Flipped around, from the
hoaxster’s per- spective this means that a hoax stands a higher chance
of succeed- ing if it is longer and looks more like a typical Wikipedia
article.
Next we create two groups of hoaxes: those that are
easy, and those that are hard, to detect for humans. To define these
groups we first rank all hoaxes in increasing order according to the
proba- bility with which humans identified them correctly; the upper
third then defines the easy, and the lower third the hard, cases. For
each feature we then compare the distributions within the two groups.
The results, shown in Fig. 11, indicate that the log median num- ber of plain-text words of the hard group is higher by about 1 than that for the easy group, i.e., hard-to-recognize hoaxes are in the (non-log) median about e1 ≈ 2.7 times as long as easy-to-recog- nize hoaxes. Similarly, hoaxes with many wiki links (Fig. 11(b)) and a low plain-text-to-markup ratio (Fig. 10(c)), i.e., with many wiki-specific elements, are difficult to recognize.
Examples. Of course, it is not only simple structural and super- ficial features such as the length, link density, and presence of wiki-specific
elements that determine if an article is recognized as a hoax. It is
also, and to a large extent, the semantic content of the information
conveyed that matters. Therefore we conclude our discussion of the human
experiment with some qualitative remarks. Table 4 lists the hardest
(top) and easiest (bottom) hoaxes (left) and non-hoaxes
(right) for humans to identify correctly, where “hard- ness” is captured
by the fraction of humans who failed to identify the article correctly
across all pairs it appeared in. Hard-to-identify hoaxes are often elaborate articles about fake people, whereas the easy ones are oftentimes already given away by their titles.
The non-hoaxes that were least credible to raters frequently have titles that sound tongue-in-cheek.
The article on the (real) Philip- pine radio station DXMM might have
been mistaken so often be- cause the version used in the experiment was
very short and had no wiki links and sections, or because it was
clumsily phrased, calling the station “the fruit of missions made by the
Missionary Oblates of Mary Immaculate in the difficult and harsh fields
of Mindanao and Sulu archipelago in southern Philippines.”
7. RELATED WORK
Hoaxes on Wikipedia are an example of disinformation [17, 11]. Wikipedia defines disinformation as “intentionally false or inaccu-
Acc.
|
Hoax
|
Acc.
|
||
0.333
|
TV5 (Malaysia)
|
0.292
|
T, it,eica
|
|
0.341
|
Tom Prescillo
|
0.312
|
DXMM
|
|
0.362
|
Alexander Ivanovich
|
0.364
|
Better Made Potato Chips Inc.
|
|
Popov
|
0.370
|
Olympiacos B.C. vs Punch Delft
|
||
0.391
|
Noah Chazzman
|
(prehistory)
|
||
0.400
|
Dav Sorado
|
0.378
|
Don’t Come Home for Christmas
|
|
. . .
|
. . .
|
. . .
|
. . .
|
|
0.867
|
The Oregon Song
|
0.872
|
List of governors of Islamic Egypt
|
|
0.875
|
Nicktoons: Dark Snap
|
0.891
|
Bobby Brown discography
|
|
0.884
|
Breast Touching Festival
|
0.907
|
List of Naruto episodes (season 4)
|
|
of China
|
0.957
|
Alpine skiing at the 2002 Winter
|
||
0.955
|
Burger King Stunners
|
Olympics – Women’s slalom
|
||
0.957
|
Mama Mo Yeah
|
0.958
|
USS Charles P. Crawford
|
|
Table 4: Hoaxes (left) and non-hoaxes (right) that were hardest (top) and easiest (bottom) for humans to identify correctly.
rate information that is spread deliberately. It is
an act of deception and false statements to convince someone of
untruth.” Disinfor- mation is frequently distinguished from misinformation, which is information that is unintentionally false.
Several pieces of related work analyze the impact of
false infor- mation on Web users. In particular, a number of papers
[25, 34, 19] investigate which factors boost or hurt credibility, and by
which strategies users can evaluate the credibility of online sources.
Such survey-based studies have been carried out both on the
Web in gen- eral [12, 13, 26] as well as on Twitter in particular [28].
Our work focuses on hoaxes as an example of disinformation and adds to
this line of work by showing that people do not perform particularly
well when trying to distinguish false information from the truth.
When false information in the form of rumors, urban
legends, and conspiracy theories appears in a social network, users are
of- ten led to share and disseminate it [7, 9]. There is a rich line of
empirical investigations and case-based studies of how this prop- agation happens, e.g.,
in Facebook [9, 15], Twitter [16], and Sina Weibo [42]. Additionally,
researchers have proposed theoretical models of how rumors and
misinformation propagate in social net- works and how their spread may
be contained [32, 1]. Other work has developed approximation algorithms
for the problem of limit- ing the spread of misinformation by selecting a
small number of nodes to counteract the effect of misinformation [5,
29]. Our work relates to this line of research by studying
misinformation on Wi- kipedia and assessing its impact on the community
and the broader ecosystem of the Web.
More related to our present work is prior research
that aims to build automatic methods for assessing the credibility of a
given set of social media posts [6, 22, 42, 43]. Most of the work in
this area has focused on engineering features that allow for detecting
rumor- ous, fake, and deceptive content in social media [22]. For
example, Kwon et al. [21] identify temporal, structural, and linguistic fea- tures of rumors on Twitter; Gupta et al. [16]
use social reputation and influence patterns to predict whether images
being transmitted on Twitter are real or fake; and Qazvinian et al. [30]
attempt to predict if tweets are factual or not, while also identifying
sources of misinformation. There are two main differences with respect
to our work: first, by working with collaboratively authored Wikipe- dia
content, we investigate a rather different domain; and second, Wikipedia
hoaxes do not spread like social media posts, but are sub- ject to more
subtle processes, involving volunteers who constantly patrol Wikipedia
in order to detect and block such content.
A final line of related work aims at developing
metrics and tools for assessing the quality of Wikipedia articles. Such
metrics are of- ten based on textual properties of the article such as
word counts [3], or on the edit history of the article [8, 41]; most
approaches, how- ever, focus on reputation mechanisms and the
interactions between articles and their contributors [23, 18, 44].
Common to all these
approaches is that editor reputation has good predictive value of article quality: edits performed by low-reputation
authors have a larger probability of being of poor quality [2]. It is
important to note that these projects develop metrics to assess the
quality of any Wikipedia article and assume that such articles are
legitimate and true, while possibly not entirely complete. The work we
present here, on the contrary, investigates the distinct problem of
differen- tiating between truthful and false information on Wikipedia.
8. CONCLUSION
In this paper we investigate impact,
characteristics, and detec- tion of hoax articles on Wikipedia. We
utilize a rich labeled dataset of previously discovered hoaxes and use
it to assess the real-world impact of hoax articles by
measuring how long they survive before being debunked, how many
pageviews they receive, and how heav- ily they are referred to by
documents on the Web. We find that the Wikipedia community is efficient at
identifying hoax articles, but that there is also a small number of
carefully crafted hoaxes that survive for a long time and are well cited
across the Web.
We also characterize successful hoaxes by comparing
them with legitimate articles and with failed hoaxes that were
discovered shortly after being created. We uncover characteristic
differences in terms of article structure and content, embeddedness into
the rest of Wi- kipedia, and features of the editor who created the
hoax.
We rely on these lessons to build an automatic
classification sys- tem to determine whether a given article is a hoax.
By combining features derived from the article’s appearance, its
mentions in other articles, and its creator, as well as the Wikipedia
hyperlink network, our approach achieves an AUC/ROC of 98%. We also
compare our automatic hoax detection tool with the performance of human
eval- uators and find that humans without any specialized tools are not
skilled at discerning hoaxes from non-hoaxes (63%
accuracy). Our experiments show that, while humans have the tendency to
rely on article appearance features, those alone are not sufficient to
make accurate judgments. In contrast, our algorithms are able to utilize
additional signals, such as the embeddedness of the article into the
rest of Wikipedia, as well as properties of the article creator, in or-
der to accurately identify hoaxes. To turn our insights into actions, we
apply our learned model to Wikipedia’s entire revision history and find
hoaxes that have been hidden in it for a long time.
There are many avenues for future work. Perhaps
surprisingly, our experiments have shown that even by using only
superficial “content” features (e.g., article length, number of links) automatic methods can quite accurately identify hoaxes. Nonetheless, a more in-depth semantic analysis of hoax content would be an intrigu- ing avenue of future research. We observe that many well-crafted
hoaxes attempt to reinforce their credibility by including links to
external Web resources, some of them serious, others fictional. Un-
derstanding these mechanisms of generating spurious support could
further strengthen our hoax detection methods, as would a more thorough
understanding of the role of sockpuppet accounts. Finally, it would be
intriguing to attempt to better understand the intentions of users who
create hoaxes: is their motivation the sheer joy of vandalism or the
desire to make a profit of some kind? Answering such questions will help
us design future information systems that are more effectively
safeguarded against the creation and propaga- tion of disinformation.
Ackowledgments. Supported in part by NSF CNS-1010921, IIS-1149837,
ARO MURI, W911NF11103, W911NF1410358, W911NF09102, DARPA XDATA,
SIMPLEX, SDSI, Boeing, Facebook, SAP, VW, Yahoo, and a Wikimedia
Research Fellowship (Robert West). We thank the Wikimedia Foundation,
and Leila Zia in particular, for granting data access.
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APPENDIX
A.QUALITY ASSURANCE IN HUMAN
GUESSING EXPERIMENT
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