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Listen to what they say: Better understand and detect online misinformation with user feedback

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Social media users who report content are key allies in the management of online misinformation, however, no research has been conducted yet to understand their role and the different trends underlying their reporting activity. We suggest an original approach to studying misinformation: examining it from the reporting users perspective at the content-level and comparatively across regions and platforms. We propose the first classification of reported content pieces, resulting from a review of c. 9,000 items reported on Facebook and Instagram in France, the UK, and the US in June 2020. This allows us to observe meaningful distinctions regarding reporting content between countries and platforms as it significantly varies in volume, type, topic, and manipulation technique. Examining six of these techniques, we identify a novel one that is specific to Instagram US and significantly more sophisticated than others, potentially presenting a concrete challenge for algorithmic detection and human moderation. We also identify four reporting behaviours, from which we derive four types of noise capable of explaining half of the inaccuracy found in content reported as misinformation. We finally show that breaking down the user reporting signal into a plurality of behaviours allows to train a simple, although competitive, classifier on a small dataset with a combination of basic users-reports to classify the different types of reported content pieces.
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Listen to what they say: Better understand and
detect online misinformation with user feedback
Hubert Etiennea,b and Onur Çelebia
aFacebook AI Research; bEcole Normale Supérieure
Social media users who report content are key allies in the manage-
ment of online misinformation; however, no research has been con-
ducted yet to understand their role and the different trends underly-
ing their reporting activity. We suggest an original approach to study-
ing misinformation: examining it from the reporting users’ perspec-
tive at the content-level and comparatively across regions and plat-
forms. We propose the first classification of reported content pieces,
resulting from a review of c. 9,000 items reported on Facebook and
Instagram in France, the UK, and the US in June 2020. This allows
us to observe meaningful distinctions regarding reporting content
between countries and platforms as it significantly varies in volume,
type, topic, and manipulation technique. Examining six of these tech-
niques, we identify a novel one that is specific to Instagram US and
significantly more sophisticated than others, potentially presenting a
concrete challenge for algorithmic detection and human moderation.
We also identify four reporting behaviours, from which we derive four
types of noise capable of explaining half of the inaccuracy found in
content reported as misinformation. We finally show that breaking
down the user reporting signal into a plurality of behaviours allows
to train a simple, although competitive, classifier on a small dataset
with a combination of basic users-reports to classify the different
types of reported content pieces.
Misinformation |False news |Reporting |Facebook |Instagram |
ocial media users who report content (thereafter abbrevi-
ated ‘reporters’) are key allies in the management of online
misinformation. By posting comments expressing disbelief and
providing fact-checking materials, they constitute the first line
of defence against the potential virality of a hoax on social
media and reduce the impact of false news on people’s beliefs.
By reporting on these claims and encouraging others to do
so, reporters also provide moderators with relevant signals
supporting misinformation detection. However, social media
users’ reports are often considered noisy signals, complex to
integrate into algorithmic detection models supporting the
prioritisation of relevant content for fact-checking.
explains why reporters are still absent from the growing liter-
ature on misinformation, which instead focuses on those who
spread hoaxes. Another gap in the literature relates to the
lack of web-data-based comparative analysis between countries
and platforms, although such research is necessary to develop
an in-depth understanding of misinformation. We suggest an
original approach to fill some of these gaps: leveraging mixed
methods to examine misinformation from the reporters’ per-
spective, at the content-level (content marked by reporters as
misinformation) comparatively across regions and platforms.
This paper aims to demonstrate the relevance of such an
approach for improving the understanding of misinformation
on social media and developing better moderation methods.
Detection models leverage many signals to attribute a prevalence score to a given content item.
User reports are only one of these and this paper’s scope is limited to better understand it.
Its contribution to this objective is threefold. First, it proposes
a general classification of reported content (GCRC) resulting
from the human review of c. 9,000 content items reported on
Facebook and Instagram in France, the UK, and the US in
June 2020. The methodology provided is extensively detailed
to enable future studies to use it. This allows us to draw
meaningful distinctions between countries and temper the
discourse on a global ‘infodemic’
; for example, it seems
that misinformation on Covid-19 did not strike France as
severely it did the US, differing in both volume, type, topics,
and manipulation techniques. Second, in addition to five
traditional information manipulation techniques, the study
identifies a novel one, which is specific to Instagram US and
presents significantly more challenges for algorithmic detection
and human moderation. Third, it suggests four reporting
behaviours that can explain the majority of the inaccuracy
(55%) in reporting. Breaking down the inaccuracy into four
types of noise associated to these different behaviours, we
show how a gradient boosting classification model trained on
a combination of user reports can accurately classify these
types of noise.
A reporter-oriented approach to studying misinforma-
tion at the content-level
General approach.
Misinformation research benefits from a
diversity of methodologies, selected countries, and social plat-
forms. This, however, makes it difficult to compare results
and generalise findings. Furthermore, with a few exceptions
)), the selection of several countries and platforms in
web-data-based research is justified by data augmentation
purposes, rather than for comparative analysis. The resulting
taxonomies of misinformation content (
) thus do not pro-
vide information on specific platforms and countries, whereas
the annual Reuters Institute Digital News Report suggests
that misinformation is significantly sensible to these variables.
Our comparative approach aims to contribute to this effort,
differentiating misinformation manifestations and practices
across platforms and regions for which existing research is
abundant (Facebook, especially the US and the UK) and those
for which it is minimal or inexistant (Instagram, especially
France). While the proportion of people using social networks
to access the news has been relatively stable across Facebook,
Twitter, and YouTube (+0% +4.9% +4.6% CAGR), this fig-
ure has increased five-and-a-half-fold for Instagram between
2014-2020 (
). We thus expect this latter platform to pro-
vide a better observation point to arrive to new insights on
misinformation types and practices.
Etienne et al. 1–14
arXiv:2210.17166v1 [cs.SI] 31 Oct 2022
The high volume of misinformation content and the limited
access to relevant datasets lead most researchers to study mis-
information indirectly such as via surveys or at the aggregated-
data level. Surveys are valuable to capture people’s general
sentiments about misinformation (
) and how perceived mis-
information impacts their trust in information sources (
However, they are limited by the respondents’ memory, sincer-
ity, and ability to detect hoaxes (
). Aggregated data analyses
are valuable to verify a hypothesis using large datasets. How-
ever, they do not allow content-level observations and are
subject to major methodological limitations. These include a
strong dependency on fact-checkers’ ratings, who have their
own guidelines (as not all false news call for moderation), only
review items reaching specific virality thresholds (in terms of
engagement and impressions) and whose ratings serve as feed-
back to train detection algorithms (responsible for enqueuing
relevant content for fact-checking). We aim to overcome these
limitations by observing misinformation at the closest level,
conducting a human review of reported content without any
pre-selection based on relevance criteria, and establishing our
own classification to analyse it. We thereby built a dataset
reflecting the users’ perception of misinformation.
Fig. 1. The General classification of reported content (GCRC).
A general classification for reported content.
The review and
labelling of a sample S1 of c. 9,000 content items reported
on Facebook and Instagram in France, the UK, and the US
in June 2020 allowed us to propose the general classification
of reported content (GCRC) below. Information related to
S1, the review methodology and how it allowed us to address
the four main difficulties we identified in labelling reported
content are presented in Annex 1.
Country and platform specificities of content reported
as misinformation
Previous research has observed that misinformation topics
may vary with countries (9) and that people express concern
about different types of content across regions (
). We find
that false news may also greatly differs in volume, type, and
technique used between countries and platforms.
Reported content varies by volume and type across regions
and platforms.
Figures 2 illustrate the distributions of S1 per
class and country. The green line represents the normalised
distribution of reported content per class. The blue bars
represent the deviation of number of reports between the two
platforms for the same country.
Fig. 2. Distribution of reports per class and platforms for each country
A first observation is that the relative homogeneity in M1,
M2, and M3 content across countries breaks at the platform-
level in France and the UK. M3 content seems more specific
to Facebook with 159 items in FB FR & FB UK versus 36
for IG FR & IG UK, while M1 and M2 are more specific to
Instagram (resp. 214 in IG FR & IG UK vs. 60 in FB FR &
FB UK). This may be explained by the fact that M3 mostly
2Etienne et al.
refer to denunciations, warnings, and calls for support, which
better fit semantic posts than pictural ones. In contrast, most
M1 and M2 items represents violent, offending, or sexually
suggestive content, for which pictural posts are better suited.
A second point relates to the great difference in the volume
of controversial content reported. C1 and C2 are 1.8x less
numerous in FB FR than in FB UK and 2.0x less numerous
compared to FB US. This trend is even more apparent on
Instagram (resp. 3.6x and 7.3x lower) where only 4 items were
classified as C1 in France (vs. 40 on IG UK and 86 on IG
US). This could be explained either by a lower (a) volume of
false news circulating in France, (b) reporting activity from
French users, or (c) capacity or willingness of French users to
report C content. Hypothesis (b) should be rejected, at least
for Instagram, where the number of distinct content items
reported per a thousand monthly active users is similar across
the countries (0.78 FR, 0.81 UK, and 0.81 US). Hypothesis
(c) is also hard to defend considering that 950 of the 1,492
reported content items in IG FR were associated to some kind
of policy offense (vs. 186 for IG UK and 133 for IG US),
suggesting great seriousness in French reporting. Humprecht
et al. (
) also recently suggested that French people may
be four times more resilient to online misinformation than
Americans. Finally, the qualitative analysis of S1 provides
another argument supporting hypothesis (a) over (c). Whereas
the great majority of C2 content in the US subsets was very
likely to be a serious hoax, C2 items in FR subsets were much
less likely to express false news. While they were mostly
classified as C2 because of our reporter’s best intention (RBI)
assumption (see Annex 1), they seemed to come from a general
scepticism towards the government, especially concerning the
management of masks, which was at the centre of a political
scandal in June 2020 (10).
Despite similar reporting rates and polarised contexts
(Covid-19 restrictions and anti-police riots happening in the
three countries), reported content was significantly smaller
in both volume and severity in France than in the US, with
the UK occupying an in-between position. While they were
seem to have not been really concerned by misinformation
issues, French Instagram users experienced a different issue
due to the significantly reported use of spam, with MS items
accounting for 58% of IG FR. Lastly, in France, and to a lesser
extent the UK, there are great differences in the distribution
of reported content per classes between the two platforms. It
is clear that C content is predominant on Facebook, rather
than Instagram. Such variations, however, tend to disappear
in the US, where the great symmetry in reported content
types suggests an increasing uniformization of content on both
platforms, therefore that such a belief is not accurate anymore.
Topics of reported content vary by region and platform.
ure 3shows the main topics expressed in reported content
across S1 subsets. The qualitative scale ranges from light blue
(a few items), to medium blue (a significant number of items),
to dark blue (a great number of items).
Our review confirms that there are significant variations
across regions in the topics considered polemical (as reported)
by users. Less intuitively, we observe that the relative ho-
mogeneity in content between the two platforms in the US
decreases in the UK and disappears in France. Besides the
Fig. 3. Qualitative representation of S1 subsets’ topics
two universal topics present in all subsets (the pandemic and
riots), only one other topic is present in both FB FR and IG
FR, namely animal welfare, and it is not even expressed in the
same way–IG posts express political claims related to animal
rights, whereas FB posts only denounce particular cases of
animal cruelty. In contrast, there are similar concerns about
weight loss products and the Armenian conflict in IG FR and
IG UK. Furthermore, even when users’ activity is monopolised
by common topics associated to global events, it does not
necessarily focus on the same aspects–e.g., while most con-
troversial posts in FB US and IG US relate to serious hoaxes
about masks, vaccines, and the reality of the pandemic, those
from FB FR and IG FR generally criticise the government’s
restrictions and not science.
Another finding is that most of the C2 items from FR and
UK that were confirmed to contain hoaxes by fact-checkers
(what we label VM) were concentrated into a narrow cluster
of identical posts (n < 10) replicated dozens of times. This
contrasts with the US subsets, where C2 posts with a high
chance of being hoaxes are highly diversified. We also found a
number of identical controversial posts in both the FB US and
FB UK samples and to a lesser extent in the FB FR. These are
likely coming from the US, often related to Covid-19 but also
to US-centred events (BLM riots, including local events such as
hoaxes about vandalised statues and cemeteries). In contrast,
no posts related to French or English topics were found in the
US samples. This suggests that several hoaxes are transmitted
from the US to Europe and better circulate between countries
on the same platform rather than between platforms in the
same country. This was not obvious considering that language
is expected to be a strong barrier to content circulation, all
reporters of S1 content also have a Facebook account linked
to their Instagram account, and many IG US C2 items are
suspicious screenshots of Twitter posts.
Manipulation techniques vary by region and platform: the ris-
ing complexity of Instagram.
We identified six main techniques
supporting misinformation associated to four psychological
leverages: scepticism ((1)the revelation and (2) the critical
tipping point), pragmatism ((3) false facts supported by false
evidence, (4) misleading presentation of facts), empathy ((5)
the confusion of feelings) and coolness ((6) the excuse of casu-
alness). They are presented in Annex 2. While the first five
techniques are consistent with existing typologies (
Etienne et al. 3
the excuse of casualness seems both recent and specific to In-
stagram, especially IG US, emerging from the type of content
shared on the platform. It characterises items with humour
(jokes, ironical statements, memes, caricatures) and/or an
artistic dimension (cartoons, short format videos, songs), mak-
ing more or less explicit reference and reacting to a sub-claim
using a frivolous tone. This content differs from both ‘paro-
dies’ (
) and ‘satires’ as it is not ‘meant to be perceived as
unrealistic’ (
) but rather hides an assumed claim behind a
veil of frivolity. Based on suggestion and sarcastic humour,
this content does not introduce a new hoax but rather may
constitute an efficient second-layer relay to support a hoax’s
viral propagation. The apparent lack of seriousness makes the
content both harder to detect and to moderate.
We observed significant variations in the manipulation tech-
niques deployed across countries and platforms: the few contro-
versial content in FB FR was mostly associated to (1) and (2)
while techniques (1), (2), (3), and (5) were mostly leveraged in
the UK and all strategies were used in the US from (1) to (5)
on Facebook and principally (6) on Instagram. Additionally,
fake reshares and screenshots of fabricated public figures’ posts
were found to be particular to IG US (also found in FB US
but not elsewhere), while false individuals’ testimonies (e.g.,
‘my friend working at the CDC said.. . ’) is particular to FB
US. From a general perspective, C2 content was found to be
significantly more subtle on IG US than anywhere else: claims
are more suggestive, often based on multimodal combinations
(picture and caption), and satire and parody is less obvious
such as memes presenting rioters with streamers, whose text
edited in a non-obvious way. Another typical example is the
trend to create parodies of Donald Trump’s Twitter-style posts,
which may result in unintentional diffusion of misinformation.
In addition to the excuse of casualness, the larger use of video
formats makes it harder to detect false claims made within
long videos that also include personal opinions and testimonies.
The strategies to spread misinformation by coordinated
groups of unauthentic actors are still expanding in technicality
to avoid bulk detection (
). At the content level, however,
misinformation innovation does not proceed from technology
improvements such as deepfakes as Brennen et al.(
) found,
we observed that altered reported posts come from low-tech
photo and video edits but language refinement and social
cues. This follows the evolution of marketing techniques on
social networks, recently illustrated by tobacco companies’
efforts to leverage Instagram influencers’ coolness to adver-
tise e-cigarettes, vapes, and nicotine pouches (
) or Mike
Bloomberg’s strategy to hire Instagram influencers ‘to make
him seem cool’ with memes (
). While people do not neces-
sarily believe the false news they share (
), it was observed
that repeating a claim increased the perceived truthfulness of
it (
), which could result in this new kind of ‘grey’ content
that is just as harmful as fabricated news. Presenting these
posts as frivolous may make them not only harder to detect
and moderate but may also increase their virality potential
and thus their impact due to their repetitiveness.
A plurality of reporting behaviours allows different
leverages for noise reduction
Identification of four reporting behaviours.
It is certainly con-
jectural to infer users’ intentions based on their reporting
activity. The diversity of reported content, however, supports
the hypothesis of a plurality of reporting behaviours associated
to distinct goals, which several signals allow us to identify.
1. Reporting false news to flag it to moderators.
is the expected use of the reporting feature, and several
signals support this hypothesis. For example, 41.8% of all
labelled content pieces in S1 were classified as C, of which
17.3% were confirmed to be false news by fact-checkers
(VM), suggesting an accurate use of the reporting tool by
a significant portion of reporters. Comment sections are
also being used by a number of users to post comments
expressing disbelief (e.g., ‘fake’, ‘fake news’, ‘this is a
hoax’), communicate that they have reported a post, and
encourage others to do so (e.g. ‘reported!’, ‘this is fake,
report it’), often providing links to material that debunks
claims from fact-checking websites. Some users even act
as ‘super-reporters’, flagging an impressive number of
relevant content items. For example, 43 Instagram users
from S0 were responsible for more than 1,000 reports each
over 90 days (May-July 2020). One user logged 2,962
reports for 60 distinct content pieces, of which 11 were
labelled in S1, containing 8 C2, 2 C0, and 1 MS. Another
one logged 1,175 reports for 37 distinct items; 10 were in
S1, in which 5 were C2, 4 C1, 1 C0, and 8 out of the 10
were confirmed to be misinformation by fact-checkers.
2. Reporting due to disagreement or jealousy to an-
noy the content creator.
The significant number of O
content, especially in FB US (22.3%), suggests that many
users report opinions they disagree with and news they be-
lieve but dislike. This is consistent with previous research
on Instagram (
). In addition, a significant portion
of reported items do not even contain a claim, nor do they
qualify for other policy violations. This suggests that
reporting is not only used as a ‘dislike button’, namely
to send negative feedback to users expressing divergent
opinions, but also to annoy them, perhaps to express
jealousy resulting from negative social comparison. This
hypothesis is suggested by the great number of I content,
of which many items are related to romantic relationships
(pictures of couples, often with captions expressing love
and happiness, or public notifications such as ‘X is in
relationship with Y’) and body image (selfies at the gym),
two top topics known for triggering negative social com-
parisons (
). As a limitation to this a priori irrelevant
reporting, it should be noted that a number of I items
were associated to other kinds of user-level offenses (e.g.,
fake account, impersonation, property rights). This ap-
plies to 7.5% of I content in S1, but it may even apply to
a larger number of reported items for which the violation
has not yet been detected.
3. Reporting inappropriate content that is not misin-
formation misinformation.
All the M content 23%,
of which 55% was confirmed policy-breaking by moder-
ators (what we label VO) supports the hypothesis that
users ‘misreport’ a number of items. While this content
may be problematic, users select the wrong option. M1
content, especially on Instagram, often contains photos
that are sexually suggestive or shows quasi-nudity, without
however qualifying as pornography or sexual solicitation.
M2 content often includes expressions of brutality and
4Etienne et al.
MS content refers to scams, spams, and fake accounts. It
is understandable that scams and unauthentic accounts
could be associated to false information although dedi-
cated categories exist to specifically report these. This
supposed ‘mistake’ is however more surprising for M1 and
M2 content because to report a post as false news on
Instagram a user has to select ‘inappropriate’ instead of
‘spam’, then scroll down to the ‘false news’ option, which
is the last one in a list of eight categories which better fit
all types of items classified as M1/M2.
4. Reporting to draw the moderators’ attention to
a problematic situation.
M3 items are posts soliciting
the user community to support or be aware of an issue
which is not primarily political and usually has a personal
connexion to the content sharer. It varies from warnings
(reporting scams and bad experiences with businesses or
artisans), to unidentified accusations (e.g., ‘the waiter of
company X was racist to me’), to identified exposure (‘this
man is a racist, expose him’ accompanied with screenshots
of private messages), often calling for public shaming
and sometimes including serious indictments (‘X sexually
assaulted me last week’). We find these accusations in
every country (51 in FB US, 74 in FB UK, 60 in FB
FR), but it is hard to tell whether reporters aim to have
moderators take action against the content creator (a few
of these turned out to be associated with harassment or
created by false accounts), to flag a danger associated
to the public shaming of a potentially innocent person,
or to support the post sharer in hopes that moderators
might alert the police. This latter hypothesis could help
understand why when users explicitly ask the community
for support (‘please block X’, ‘please report X’, ‘report
this false news’), users often report the whistle-blower
instead of the post or user they are asked to flag [50,51,52].
The absence of other typologies of reporting behaviours
does not allow us to compare this typology with other ones.
Therefore, we suggest that these categories are considered a
first draft of a typology of reporting behaviours, which would
benefit from further research using different methods, notably
psychometrical analysis and sociological interviews.
Splitting the noise to decrease reporting inaccuracy.
GCRC has been shown relevant to analyse users’ reports and
motivations; we shall now explain that it can also help content
moderators better analyse such signal for detection purpose.
To this end, we re-order the GCRC classes according to the
moderators’ interest as in Fig. 4. Moderators care about
the relevance of reports from a misinformation moderation
perspective, focusing on the signal’s accuracy. The traditional
approach, calculating the ratio VM/N(S1) gives us a 0.93 inac-
curacy of the reported content, which explains why user reports
are often considered very ’noisy’ and reporters untrustworthy.
This metric is however subject to several limitations including
the fact that the number of S1 items reviewed by fact-checkers
is unknown. The identification of reporters’ behaviours then
allows us to re-evaluate the accuracy of user reporting and
assess its performance using more suitable metrics. Such an
approach consists in splitting this ‘relative’ inaccuracy into
different types of noise, which can be linked to various report-
ing behaviours and upon which distinct actions can be taken
to improve the overall signal.
F alse noise
P(C1,C2,C 3,C0,C2)
indicates the propor-
tion of coherent, although not necessarily accurate, re-
porting. It accounts for 0.35 of the overall inaccuracy
and could be associated to the reporting behaviour 1.
We call it false noise as it probably mainly results from
credible reporting done by people with a low capacity to
detect false news or understand the moderation policies
and types of misinformation prioritised by fact-checkers
(e.g., C0). The use of educative campaigns, communica-
tion efforts explaining platforms’ moderation policies, and
self-fact-checking material could help reduce this noise.
Quasi noise
P(V O,M 2,MS,M3)
indicates the pro-
portion of relevant, although not necessarily coherent,
reporting. It accounts for 0.2 of the inaccuracy and could
be associated to the reporting behaviour 3. We call it
quasi-noise because it refers to content which can be mod-
erated but not in the context of misinformation. The
reporting is most probably credible, and additional inves-
tigations should be conducted to understand the source
of the confusion surrounding the reporting feature.
Sof t noise
P(M1,H2,H 3,O2)
indicates the proportion of
doubtful, although not necessarily irrelevant, reporting. It
accounts for 0.03 of the inaccuracy and could be associated
to any reporting behaviour. We call it soft noise because
it is difficult to make any strong assumptions about it.
Har d noise
P(H1,O1,I,J )
indicates the proportion of
probably irrelevant reporting. Accounting for 0.34 of the
Fig. 4.
Cumulative proprortion of content items per class associated to reporting behaviours. This table should be read from left to right as such: before C1 is the sum of all VM
content and the remaining C1 content.
Etienne et al. 5
inaccuracy, it could be associated to the reporting be-
haviour 2. We call it hard noise because it most probably
results from unfaithful reporting which should be filtered.
As we interpret it, around 55% of the reporting inaccuracy
(false and quasi-noise) could probably not be attributed to the
reporters’ lack of seriousness but rather to a confusion sur-
rounding the reporting features and moderating rules. While
our RBI assumption inflates the number of C2 content, cate-
gorising content as ‘false noise’ when it actually is unfaithfully
reported, a number of C items have, however, not been re-
viewed by fact-checkers and could then have been rated VM.
Likewise, a portion of the ‘hard noise’ may also contain unde-
tected policy-breaking items, which may be rated VO later on.
We are now proposing a way to classify these different types
of reporting noise which could potentially be used to redirect
feedback towards the relevant moderation channels.
Leveraging multi-channel reporting to classify reported con-
tent per type.
Our objective is to identify the false noise (C),
most relevant for misinformation moderation purposes, and
the quasi-noise (M), relevant for another moderation chan-
nel. We focus on Instagram as it has been shown to be the
platform in which new techniques are appearing. To better
match the behaviours and noise types previously identified,
we aggregate classes as such: C (C0, C1, C2, C3, C2*), M
(M2, M3, MS), HM (H2, H3, M1), OH (H1, O1, O2), and I.
Figure 5represents the distribution per aggregated class of the
number of reports received for each Instagram item of S1 over
90 days. We considered a 0.999 quantile, excluding 4 outliers
(1 C, 1 OH, 1 M, 1 I) with a number of reports superior to
329,279. The total number of reports received for an item
clearly appears to be a meaningful signal to distinguish C from
other classes, especially from I and M. It, however, seems less
accurate to differentiate C and OH under a 20,000 reports
threshold, and uncapable of separating M from I.
Fig. 5. Distribution of IG content reports count per aggregated class
We confirm these observations by comparing the means of
the dependent variable for each pair of aggregated class. The
results are presented in Figure 6, where the p-values displayed
correspond to each two-by-two comparisons. We use Welch’s
t-test to accept a one-sided alternative hypothesis with signifi-
cance level of 5% (or 10% for grey arrows). We did not assume
same variance among classes since the reporting behaviour
may significantly vary, and it is empirically verified. The
mean normality assumption was verified by the D’Agostino-
Pearson test. Kolmogorov-Smirnov tests were also conducted
to point significant distribution shift between aforementioned
Fig. 6. Partial order of aggregated classes based on number of reports
Finally, we train a gradient boosting classification model
to identify four classes (C, M, I, others) from ten features
corresponding to the main platform reporting categories (’false
news’, ’nudity/sexual solicitation’, ’violence’, ’harassment’,
’suicide/injury’, ’spam’, ’hate speech’, ’unauthorised sales’,
’inappropriate content’, ‘I don’t like it’) on IG(S1) with test
sample of 10%. The model’s general performance presented
in Figure 7reaches an F1 of 0.56 on C/
C and of 0.63 on
M. More interesting is the performance per country: the
model claims an F1= 0.84 on M/
M for IG FR, where spam
was identified as the main issue, and an F1 = 0.72 on IG
US, where misinformation was identified to be the main issue.
While they significantly vary in order between countries, the
most important features are ’false news’, ’spams’, ’hate speech’
and ’inappropriate content’. This simple model shows that it
is possible to learn to classify reported content according to
our typology.
Fig. 7.
Precision-recall curves per country for aggregated classes detection on S1(IG)
6Etienne et al.
By studying content reported as misinformation, compara-
tively between regions and platforms, and at the content-level,
our goal was to refute the idea that user reports are a low-
accuracy signal that would not be not very suitable for online
misinformation detection. Instead, we show that user report-
ing offers a complex signal, composed of different feedback
that should be understood and assessed separately. The con-
tent review allowed for us to observe meaningful distinctions
regarding content reporting between countries and platforms
as there was significant variation in the volume, type, topic,
and manipulation technique. Two key findings are the quasi-
absence of C content on IG FR, which instead has a spamming
issue, and the apparent convergence between Instagram and
Facebook in the US. This uniformization in volumes and topics
is accompanied by the emergence of a new type of manipulative
technique which is both harder to detect and moderate, raising
important challenges for misinformation management. The
fact that misinformation innovation is not driven by sophis-
ticated techniques such as deepfakes but rather by language
refinement and social cues supports the idea that leveraging
users’ support for a more participative online content mod-
eration is an interesting direction to improve misinformation
detection in the future. We show here that examining the
variety of behaviours present in a given signal can help identify
other relevant data points that can be combined to increase
the signal’s overall quality. We also show that the typology
we propose is predictable, and that a basic model trained a
small dataset (n = 4,056) and relying only on a few basic data
points can reach a significant performance at detecting its
main classes.This may now open perspective to improve the
performance of detection algorithms, not only by filtering the
reporting signal (based on the different behaviours identified
and users’ credibility), but also as a feedback within a commu-
nication framework (by investigating further the reasons why
well-meaning users seem to misreport a number of items).
Author Affiliations.
Facebook AI Research, 9 rue Ménars,
75002 Paris Ecole Normale Supérieure, Department of Philos-
ophy, 45 rue d’Ulm, 75005 Paris
We would like to thank Joelle Pineau,
Antoine Bordes and Jerome Pesenti for their support in having this
paper published.
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Supplementary materials
Annex 1.A: Composition of the data sample S1.
From all the content
reported as ‘false information’ by Facebook and Instagram users
from France, the UK, and the US between June 3rd and July
3rd, 2020, we extracted a subset S0 that contained all items that
included data relevant to our study i.e., basic information related
to content, reporters, and sharers. Because Instagram has only
recently allowed people to register their gender, S0 only contains
items reported by Instagram users who have linked their account to
a Facebook account with a registered gender. From S0, we extracted
the sample S1, which is composed of randomly selected items that
ensure a relative balance between reporters’ countries, genders,
and age categories while maximising the diversity of content and
reporters (i.e., minimising the number of identical posts shared or
reported by different users and that of different items reported by
the same user). S1 is composed of 11,463 content pieces, of which
8,975 received a class label, 2,004 were removed by users before
being labelled, and 484 were not published in French or English.
Among all labelled content, we shall refer to S1 subsets as follows:
FB FR (n = 1,422), FB UK (n = 1,519), FB US (n = 1,527), IG FR
(n = 1,491), IG UK (n = 1,516), and IG US (n = 1,500). Although
the subsets’ sizes may seem small compared to S0’s (which contains
97.2 million entries for Instagram reports alone), only 0.23% of IG
S0’s content items are distinct, compared to 97% in IG S1. This
results in the 1,491 labelled items of IG FR representing 6.3% of all
the different items reported on Instagram France in S0.
Annex 1.B: Review methodology.
The review process was composed
of four steps. As there was no existing typology for reported content,
an exploratory review of c. 300 items per country allowed us to lay
the foundations for the classification. Such a review was conducted
independently by the authors, who then compared their results
to define the final version of the GCRC. We then reviewed each
of S1’s items, associating them with a GCRC class. The process
was ordered to review content from the same media type, then
platform, then country together to assess differences and similarities
across these three layers. Every time that all items from a given
platform in a given country were labelled, a confirmation review was
conducted per content class to ensure the coherence of the labels.
Once all items were classified, a final review was conducted per class
labels across all content. In addition to detecting misclassified items
and ensuring the coherence of the labelling across platforms and
countries, this final round also permitted us to include fact-checkers’
most recent ratings (as of December 7th, 2020). The resulting
classification, presented below, contains six classes and fourteen
sub-classes. Note that it does not aim to assess the reporters’
performance that is, distinguishing reported content containing
accurate vs. false claims but credibility differentiating content
that could contain false news from that which could not. This, in
addition to the reporter-oriented approach irigger builds on, makes
it relevant from a research perspective but should not be used as a
turnkey tool to operationalise content moderation.
While we recognise that the fact for the review to have been
conducted by the authors may be seen as a methodological limitation,
we think that this is justified by the particular difficulty of the
labelling task and the granularity of the classification. To be clear,
we consider that such a task requires researchers familiar with
misinformation topics and literatur and that it could not have
been performed in good conditions by a variety of non-specialised
annotators. This is also the reason why we elaborate extensively
below on the challenges of such a task and the mythological choices
they resulted in.
Annex 1.C: Four difficulties in labelling content reported as misin-
Labelling content from a misinformation viewpoint is
a complex task which cannot be considered an exact science. It
requires someone to (1) identify a claim within a structured piece
of content. If it is explicit, they must (2) determine whether it
could theoretically be falsified and how difficult the verification
process would be in practice. When it is implicit, they must (3)
assess the underlying claim’s degree of obviousness. Finally, (4) the
difference between the subjective perception of the content sharer
and the reporter should also be considered. We identified four main
difficulties associated to these steps.
The first difficulty is design-specific, resulting from the content’s
structure. Some items are multi-layer posts, composed of content
pieces (e.g., a link, picture, or video), meta-content (e.g., a caption
or edits of the video) and second-level meta-content (e.g., a caption
reacting to a reshared post, which already included a caption),
making the identification of the reported claim difficult. To address
this, we adopted a holistic reporter-oriented approach, holding a
reporter’s best intention (RBI) assumption: if any of the possi-
ble claims expressed in any layer of the item could be reasonably
considered controversial, we assumed the user reported the item
because of this claim. We herein aimed to minimise the number of
false negatives items labelled as irrelevant whereas reporters did
try to report accurately at the cost of false positives. Consistent
with our position of assessing the content’s falsifiability and not
its veracity, there is a high ratio of C2 classes among shared links
on Facebook (c. 40%) as articles’ headlines often contain factual
claims. The RBI, however, allowed us to realise that many posts
that seemed irrelevant at first did not derive from frivolous reporting
but contained detected policy violations other than misinformation.
The second difficulty relates to a claim’s degree of falsifica-
tion, as two claims may be equally true or false but differ in the
resources needed to verify them. Consistent with our reporter-
oriented approach, we adopted a best fact-checking resources (BFR)
assumption, considering all identified controversial claims as rel-
evant whatever the resources required to verify them. Reporters
have little information about the fact-checkers’ capacities, which
should then be orthogonal to their reporting credibility. While we
did not aim to rank controversial claims by importance, it was nev-
ertheless possible to draw a relevant distinction between small-scale
and large-scale impact news. The impact scale differs from the
geographic scale as events with a small geographic scale may have a
large-scale impact. Many M2/C2 content reported in the US herein
does not necessarily relate to large protests but to local events from
which larger associations could be made e.g., an individual’s act
of violence presented as epitomizing the whole Black Lives Matter
(BLM) movement. Whereas an event’s geographic scale was often
found to have little relevance for assessing its potential impact, a
small-scale impact was usually associated to a small geographic
scale. As the reporting of such content also seems to proceed from
a different intention, we distinguished small-scale impact (C0) from
large-scale impact (C1, C2, C3) content.
The third difficulty is semantic, grounded in the thin distinction
between assertion and suggestion. Many items do not explicitly
endorse a controversial claim but instead suggest it in various ways.
It is even more complicated to assess this content when it includes
emojis, multi-modal associations (e.g., on its own, the caption or
image is not controversial, but their combination is [49]), or refers
to a commonly known idea without a direct reference. The several
rounds of reviews allowed us to develop a general understanding
of the top viral topics and reclassify the items whose suggestive
references had previously been missed. The RBI also allowed us
to classify items with a suggested claim as belonging to C1 or
C2 according to the obviousness of the reference and the level of
The fourth difficulty is metacognitive, resulting from a double
asymmetry. The first one is that between the actual intention IA(A)
of a user A when posting a post PA and the given intention IB(A)
inferred by user B of A when seeing PA. This is particularly the case
when A makes a metaphorical use of statistics (e.g., ‘99% of people
recover from Covid-19’; suggesting that the large majority of people
recover, which is accurate even though the exact statistics may not
be) or hyperboles (e.g., ‘everybody recovers from Covid-19’, sug-
gesting that most people do). The second one affects A’s expected
reception of PA by B and B’s actual reception. This especially
applies to humoristic posts, understood as such by some people but
taken seriously by others. This pitfall is central as it puts an agent’s
subjectivity in tension with that of others; whereas we aimed to stick
to the RBI, a number of reported items of content clearly expressed
irony. However, many humoristic posts also contained a sub-claim
that was often controversial, making humourful posts a difficult-to-
moderate vector for hoax dissemination. To satisfy the diversity
of cases, we broke content using humour into three sub-classes ac-
8Etienne et al.
cording to the content’s obviousness and potential to offend. Many
items also contained a mixture of opinions and fact-checkable news
or combined inappropriate elements and controversial claims. We
classified the former as C instead of O because mixed posts remain
relevant from a misinformation reporting perspective, and the latter
as C instead of M, aligned with the RBI.
Finally, the RBI revealed itself as a solid asset that preserved
the labelling process against the reviewers’ personal opinions. In
such a context of great uncertainty, it saved us from the temptation
of classifying posts as I when they contained claims that seemed
obviously accurate, debunked later on, or obviously false but that
were ultimately confirmed. Based on the content’s nature, GCRC
classes are sufficiently objective to be robust to the plurality of
opinions that reviewers may have, while subclasses are more pen-
etrated by reviewers’ opinions. This two-level classification thus
combines the advantages of a highly consensual labelling process at
the class-level and the integration of meaningful additional signals,
that are however less univocal, at the subclass-level. Comparing
with fact-checkers’ ratings, we find that 97% of confirmed false
news are rated C. The other 3% relate to O1 content, for which we
disagree with these ratings.
Annex 2: Six manipulative strategies to convey misinformation.
first two strategies target people with a certain degree of scepticism.
1. The revelation
technique mostly characterises typical con-
spiracy theories (C1). It challenges people’s egos and encour-
ages them to ‘wake up’ instead of being a ‘sheep’, making direct
references to the cabal, Masons, a world elite, and a new order.
These posts are usually marked by semantic patterns either
related to the general concepts of truth, trust, the elite, and
the establishment (e.g., ‘news’, ‘media’, ‘wake up’, ‘masons’,
‘governments’, ‘truth’, ‘sheep’, ‘facts’, ‘distracting’) or to more
contextual theories such as QAnon or anti-vax conspiracies
(e.g., ‘pizza’, ‘paedophile’, ‘Hollywood’, ‘children’, ‘Clinton’,
‘Trump’, ‘Bill Gates’, ‘chip’) and intend to gain virality by
soliciting viewers to reshare (‘spread the word’, ‘share before
it gets deleted’, and ‘share to expose them’). Congruent with
other research (
), such findings also confirm that semantic
cues can constitute a useful signal to detect misinformation, no-
tably by monitoring the frequency of words contained in posts
verified as hoaxes by fact-checkers. This signal, however, might
mostly be useful to detect the most caricatured conspiracies
on Facebook (C1) such as hoaxes that could be debunked with
a quick web search. Moreover, the people most susceptible to
fall for these messages certainly are those already in contact
with conspiracies theories, and they may not be sensitive to
debunking material being a conspiracist is less a question of
being given accurate information than a psychological posture.
Semantic methods may also be challenged by counter-detection
strategies (e.g., ‘c0r0navirus’).
2. The critical tipping point
technique consists in leveraging
a real fact (e.g., a public scandal or polemical claims from
a controversial figure) as an entry point to encourage people
to reconsider all their beliefs. The fact is usually presented
in a twisted way, often accompanied by exaggerated empathy
(‘this is despicable’) or an invitation to generalise (‘if they
lied about this. . . what else did they lie about?’). A variant
consists in creating a false mystery around an accurate fact
(‘this is happening, why is nobody talking about it?’). This
technique may best work to encourage people who are already
sceptical or indignant about a recent scandal to start accepting
The two following techniques target people who are sensitive
to pragmatic arguments.
3. False facts supported by false evidence
are typically false
statements about events that have allegedly happened and
false quotes from public figures, often backed by unauthentic
documents (e.g., inauthentic ‘leaked’ documents from the FBI,
CDC, or BLM management), unreferenced scientific data, or
personal testimonies from a mysterious authority (‘a friend
working at the NHS’, ‘the head of the resuscitation department
of this hospital’) whose source is impossible to verify. It also
includes modified pictures and videos, although we did not
find any deepfakes.
4. The misleading presentation of facts
consists in present-
ing authentic documents or accurate facts in a misleading way
to encourage a targeted erroneous interpretation (e.g., quotes
and pictures taken out of their original context, truncated
videos, partial references to history). While similar to the
previous one, this technique is harder to debunk because of
the accuracy of the facts it is based on.
The fifth technique plays on people’s empathy.
5. The confusion of feelings
consists in presenting either false
news or authentic facts in a twisted way to provoke an emo-
tional reaction against a given target. This was particularly
observed when someone leveraged an isolated action to dis-
credit a whole movement through the mobilisation of symbols
(e.g., protester’s violence against a veteran, acts of police bru-
tality against a peaceful protester, profanation of a military
cemetery, destruction of a public figure’s statue).
The sixth technique plays on the register of coolness.
6. The excuse of casualness
characterises items with humour
(jokes, ironical statements, memes, caricatures) and/or an artis-
tic dimension (cartoons, short format videos, songs), making
more or less explicit reference and reacting to a sub-claim
using a frivolous tone. This content differs from both ‘parodies’
(Wardle 2016) and ‘satires’ as it is not ‘meant to be perceived
as unrealistic’ (Molina et al. 2019, 198) but rather hides an
assumed claim behind a veil of frivolity.
Etienne et al. 9
Annex 3: Selected examples of reported content from S1.
10 Etienne et al.
Etienne et al. 11
12 Etienne et al.
Etienne et al. 13
14 Etienne et al.
ResearchGate has not been able to resolve any citations for this publication.
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2016) 6 types of misinformation circulated this election season
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Wardle C (2016) 6 types of misinformation circulated this election season. Columbia Journalism Review 18.