# Temporal and geographic analysis of the Hydroxychloroquine controversy in
the French Twittosphere

M.Mauro Faccin  E.Emilien Schultz  F.Floriana Gargiulo  DIMEC, University of
Bologna, Bologna, Italy  Médialab, Sciences Po, Paris, France  Gemass, CNRS,
Paris, France

###### Abstract

At the beginning of the COVID-19 pandemic, the urge to find a cure triggered
an international race to repurpose known drugs. Chloroquine, and next
hydroxychloroquine, emerged quickly as a promising treatment. While later
clinical studies demonstrated its inefficacy and possible dangerous side
effects, the drug caused heated and politicized debates at an international
scale, and social media appeared to play a crucial role in those
controversies. Nevertheless, the situation was largely different between
countries. While some of them rejected quickly this treatment as France,
others relied on it for their national policies, as Brazil. There is a need to
better understand how such international controversies unfold in different
national context. To study the relation between the international controversy
and its national dynamics, we analyze those debates on Hydroxychloroquine on
the French-speaking part of Twitter, focusing on the relation between
francophone European and African countries. The analysis of the geographic
dimension of the debate revealed the information flow across countries through
Twitter’s retweet hypergraph. Tensor decomposition of hashtag use across time
points out that debates are linked to the local political choices. We
demonstrate that the controversial debates find their center in Europe, in
particular in France, while francophone Africa has a lower participation to
the debates, following their early adoption of the familiar Hydroxychloroquine
and rejection of WHO recommendations.

COVID-19,

Hydroxychloroquine,

Twitter,

hypergraph,

tensor factorization,

topic detection,

geographic analysis,

online media,

medical controversy,

###### keywords:

nntf short=NNTF, long=non-negative tensor factorization, hcq short=HCQ,
long=hydroxychloroquine ihu short=IHU, long=French Marseille University
Hospital Institute ngo short=NGO, long=Non-Governmental Organization who
short=WHO, long=World Health Organization ijaa short=IJAA, long=International
Journal of Antimicrobial Agents

Research

## Context

The recognition between January and February 2020 of the international
dimension of the COVID-19 epidemic started a race to identify efficient
therapeutic solutions. Among the many attempts of drug repurposing, the
proposal to use chloroquine first and then hcq received exceptional attention
[1, 2]. Preliminary results raised high expectations and attracted massively
the attention of the scientific community, governments and general public.
This raised to numerous public debates on individual choice to use or not this
therapeutic solution, the liberty of physicians to prescribe it, and how
scientific proofs should inform political choices. Due to their scale and
their intensity, those debates built one of the most recent public controversy
on health [3]: scientists and physicians, but also citizens and political
representatives, argued for or against the use of this drug, fostering
circulation of contradictory claims in a context of scientific uncertainty. As
for other health controversies [4], social media have played a central role in
the circulation of both scientific information and political debates, and
participated to transform the hope for a cure to an _infodemics_ [5]
characterized by a strong polarization [6], especially on Twitter [7].

The in vitro efficacy of chloroquine was first mentioned in the early 2020 in
a Chinese scientific report leading to the onset of numerous clinical trials
around the world to test the drug efficacy in different combinations [8]. The
media coverage in the public sphere was largely triggered by Professor Didier
Raoult and his team at the research center ihu, in particular by their
publication on the “ijaa”, dated 20th March 2020. Along with post on Youtube
platform and Twitter, this publication gave visibility to the promises of hcq
effectiveness in the treatment of COVID-19 (in combination with Azithromycin)
[9]. Following this first claim, Twitter played a central role [10] for the
diffusion of the topic outside the French borders. This is best shown by Elon
Musk’s tweet:

> Hydroxychloroquine probably better.  (Elon Musk, March 17, 2020)

followed by the US President Donald Trump:

> “HYDROXYCHOLOROQUINE & AZITHROMYCIN, taken together, have a real chance to
> be one of the biggest game changers in the history of medicine. The FDA has
> moved mountains — Thank you! Hopefully they will BOTH (H works better with
> A, International Journal of Antimicrobial Agents) be put in use IMMEDIATELY.
> PEOPLE ARE DYING, MOVE FAST, and GOD BLESS EVERYONE!”.  (US President Donald
> Trump, March 21, 2020)

increasing the visibility of Professor Didier Raoult and boosting its position
in France [11]. This important media coverage in both legacy media and social
media had strong consequences on the following political decisions. On the
wave of this paper the governments of several counties allowed the use of hcq
for COVID-19 treatments, while many clinical trials were started to assess hcq
efficacy.

Due to the time needed to achieve randomized clinical trials and deliver
robust evidence, the uncertainty on hcq efficacy persisted for several months
which fueled debates. In several countries, as the US or France, the debates
were boosted by the political polarization between those who saw this
treatment as a solution for the pandemic and those who doubted that it was a
silver bullet and promoted other strategies to manage the virus, as lock-down,
social distancing and masks [6]. This division and the subsequent uncertainty
was reinforced by the publication of an article in the Lancet journal the 22nd
of May 2020 that claimed of hcq inefficacy and even toxicity. Clinical trials
all around the world were stopped and countries, as France, withdrew the
authorization for the clinical use of hcq for COVID-19 patients, leading to a
re-intensification of the debate on the topic. The data of this article was
then denounced as a fraud and the paper was retracted two weeks after [12]. In
particular a large Twitter activity has derived by the use of the Pr. Raoult’s
follower of the hashtag #LANCETGATE, accusing the government to have stopped
the use of hcq based on a retracted paper. Besides, those debates assumed
extremely harsh tones, especially on social media, where many scientists and
doctors were threatened and attacked [13, 9].

The controversy on hcq had a worldwide reach, and had consequences on health
behaviours, political decision and scientific organization. One of its
specificity was to bring debates on the functioning of research and the
conditions of medical evidence, particularly on clinical trials, into the
public and political debate. For this reason, several social science and
public health studies focused on this topic [14, 15, 16, 17]. Those works
showed that national specificity, such as the way government handled the
pandemic, or the audience of central actors may have consequences on how the
debate unfolded. For instance, it appeared that the type of drug that attract
the media attention can differ regarding the country [18]. In France, hcq was
presented by doctors and the media as a popular African drug [19]. An initial
fierce media and political debate on the scientific controversy, notably due
to the communication capacity of the ihu and Pr. Didier Raoult, was followed
by a gradual dilution. With the advent of vaccination and the government’s
strategy of prioritizing this crisis management, the promotion of hcq becomes
an element of the mobilizations that oppose this management [9].

Although evidence has accumulated on the ineffectiveness of these molecules in
treating this disease and even on their negative consequences [20, 1], the
drug were still administered in countries where national leaders have
supported its use [21], leading to less public debates. For instance, in
Brazil, hcq is integrated into the government’s response to the crisis, to the
extent that observers have spoken of a “ _hcq alliance_ ” [22]. Many countries
in West Africa integrated it into care protocols [23], notably because of the
“familiarity” with this anti-malarial drug [24]. The Cameroonian government,
for instance, recommended the use of hcq and made it the national standard
care. The Ministry of Health promoted the national industry by ensuring an
investment plan in the Institute for Research and Study of Medicinal Plants.
The debates in the public space focused on the issue of counterfeit drugs and,
in a second time, on the scandal of a misappropriation of public money by the
Minister of Health and the Minister of Scientific Research and Innovation,
with few references to the WHO recommendation.

The case of hcq allows us to directly address the issue of the circulation of
debates in the context of the management of the COVID-19 pandemic, and to
explore the relation between an international controversy and specific
national debates. While existing studies targeted either specific countries,
as France, Brazil or the US, or a specific period of the controversy, we will
focus on French-speaking countries and in particular to their relationship
with France itself [25], the country that triggered the initial debate.
Moreover, while in the US [6] and Europe debates have been deeply discussed,
little attention have been given to Africa. If some observers claimed that hcq
debates was not a “French obsession” [26], there is little evidence of the
extension of those debates in French-speaking countries. On previous epidemics
outbreaks, such as Ebola, the issue was specific to African countries [27],
limiting the possibility to make comparisons. The particularity of the history
of a repurposed drug for malaria, endemic in Africa, and the post-colonial
history shared between France and several West African countries directly
raises the question of the extension reached by the debates.

Based on a computational social science analysis of debates on a social media,
Twitter, we propose to explore the scale effects of the debates that took
place on hcq between countries, with a focus on francophone African countries.
In this paper we will first analyze the temporal dimension of the discussion
on hcq in the French speaking Twittosphere. We will show that the debates
follow precise topic waves, that reflect the scientific and political agenda
of the hcq debate. Secondly we will analyze the geographic distribution of the
debate, showing a general national-level recirculation of the COVID-19
information structure, but also a high localization of the hcq debate in the
Northern Hemisphere.

## Methods

### Data collection

We collect tweets and retweets from Twitter using its search and stream APIs,
leveraging the Gazouilloire [28] python software. We used three set of
keywords to independently extract tweets related to three topics: COVID-19,
vaccines and hcq, and merged the three initial datasets, keeping only the
tweets mentioning either COVID-19 related keywords or hcq related keywords.
For the full set of keywords, see [29, 30]. In this way we collected
approximately 85M tweets from 768k users [31].

### Geographic analysis

A subset of Twitter users includes in their online profiles geographic data.
For each of them we checked their _user description_ , _user real name_ and
_user location_ fields for the presence of geographic related keywords. In
particular, we searched for:

  * •

UTF-8 characters corresponding to a country flag.

  * •

a capital’s name,

  * •

demonyms of a country,

  * •

a country name in different languages.

Twitter users fitting in more than one country are ignored unless one of the
two countries was France (often, France related keywords may refer to the
spoken language). In this way we geotagged approximately 380k users.

To further extend the initial set of geotagged users we considered the
dilation of this set under the retweet network. For each untagged user we
consider the fraction of retweets towards or from users with a given
geographic tag. If this fraction surpasses 90%, the user acquires the same
geographic tag. After this extension, geotagged users reached the number of
approximately 488k.

This let us build a subset of tweets posted or shared by a tagged user linked
to a given country.

### Information flow across countries

To study the information flow within or between countries we leverage the
hypergraph representation of the retweet topology. In particular, for any
tweet in the dataset, we add a forward hyperedge [32] between the tweeting
user (hyperedge tail) and the retweeting users (hyperedge head) [30, 33]. To
simulate the information flow on such topology, we consider a Markov-chain
such as a random walk. The walker selects a hyperedge incident to its position
with even probability and then jump to any of the head users with even
probability. The evolution of the random walker describes the information flow
across the Twitter user base, and we can write the probability of reaching any
user $j$ from any other user $i$, $p(i\to j)$, and the probability
distribution $p(i)$ of visiting a user $i$ at the equilibrium.

Considering the partition of user into countries, one can compute country wide
quantities such as the probability of the information to flow through a
country (visiting probability):

$p_{C}=\sum_{i\in C}p(i),$ (1)

and the probability that information from country $C$ reach country $D$:

$p_{CD}=\sum_{i\in C,j\in D}p(i)p(i\to j).$ (2)

Given the relations above, we compute the trapping probability as the
probability of a piece of information to stay trapped within one of the
partitions (in our case the partition of users into countries).

$p_{C}^{\circlearrowleft}=\frac{p_{CC}}{p_{C}}=\frac{\sum_{ij\in C}p(i)p(i\to
j)}{\sum_{i\in C}p(i)}$ (3)

for any country $C$.

### Topic detection with nntf

To extract the main topics and the evolution of the discussion across time and
countries we extract the temporal hashtag co-occurrence and the country
hashtag co-occurrence tensors. The former tensor $T$ is defined when $T_{ijk}$
represents the number of tweets and retweets that hold both hashtags $i$ and
$j$ on day $k$: Similarly, the country hashtag co-occurrence tensor $K$ is
defined when $K_{ijk}$ represents the number of tweets and retweets that hold
both hashtags $i$ and $j$ and the tweeting or retweeting user is from country
$k$.

To extract the time evolving topics we use the nntf [34, 35, 36] as in [37].
In nntf, the given tensor $T$ is decomposed in three non-negative matrices
$A,B$ and $C$ where each entry of the tensor is as close as possible to:

$T_{ijk}\sim\sum_{c=1}^{N_{C}}A_{ic}B_{jc}C_{kc}$ (4)

where $N_{C}$ is the number of components fixed by the user. In a symmetric
framework as in our case, the first two matrices coincide $A=B$ and represent
the hashtag composition of each component. The third matrix $C$ represents the
activity of each component along the third dimension, being it time or
country. Since the temporal/country activity of the components do not depend
on the hashtag composition, we compute the strength of each component as
follows:

$S_{kc}=C_{kc}\sum_{i=1}^{N}A_{ic}$ (5)

Where $N$ is the number of hashtags.

In the following we fix $N_{C}=9$ using the elbow criterion, although the
results are robust to the variation of this parameter.

## Results

### The timeline of the hcq debate

Figure 1: Database statistics. COVID-19 daily incidence in France for
comparison (upper plot). Frequency of daily tweets and retweets on COVID-19
(second plot from above) or on hcq (third plots). Number of users per country
(lower plot).

Fig 1 displays the tweeting frequencies for tweets containing either a keyword
related to COVID-19 ($\sim$40M) or keyword related to hcq ($\sim$5M). While
both timelines display an initial peak with a high volume of tweets, they also
have specific characteristics. The discussion related to COVID-19 follows the
epidemic peaks with a higher attention in the first pandemic period due to the
emotional impact of the event. The profile of the discussion on hcq is
influenced by the scientific and political agenda related to the debate.
During this period specific events influence the sudden user engagement in the
hcq discussion:

March 27

The resonance of the publication of first Pr. Raoult’s observational study (on
March 20) and the political echo given by Donald Trump to this publication,
generate a first attention peak in the debate;

May 22

Publication of the Mehra et al. article in the Lancet alerting about hcq
toxicity;

June 2

Expression of concern from the Lancet for the article about hcq, Pr. Raoult
launch the hashtag #lancetGate;

June 5

Retraction of the Lancet article;

June 24

Pr. Raoult speaks in front of the French Parliament (commission on the
management of COVID-19).

In the second period of the debate (after June 2020) the engagement on hcq
debates is almost monotonically decreasing for the whole period, with an
exception on September 2020 influenced by the Gilead affair in which Pr.
Raoult shows the conflicts of interest of some member of the Scientific
Council with Gilead, the industry producing Remdesivir.

Figure 2: Topics of the online discussion within tweets on hcq and related
keywords. Word clouds of each component (above). Temporal evolution of each
component strength (stacked plot below).

To better characterize the composition and the timing of the debate, we
extracted the subset of tweets focused on hcq and related topics and computed
the temporal tensor of the hashtags co-occurrence (as described in the methods
section). See Fig. 2. This procedure allows to identify the mesoscopic
structure of the debate, i.e. the different topics debated and the period in
which each subject was actively discussed. The latter can be summarized as
follows:

  * •

The first set of hashtags, E0, represents the institutional debate on hcq
which is active from the presentation of the ijaa paper until the suspension
of the clinical use of hcq for COVID-19 treatment;

  * •

The topic E1 is also connected to the initial debate on hcq but with a focus
on the figure of Pr. Raoult;

  * •

E2 has two clear temporal spikes and is connected to the appearances on
television of Pr. Raoult;

  * •

E3, E4 and E5 are temporally concentrated between May and September and
represent the political debate on the stop to the use of hcq for COVID-19
treatments (the institutional debate above all in E3), the lancet gate (E5)
and the beginning of the discussion on “remdesivir” as the alternative to hcq
(E4);

  * •

E6 concentrates in September has its focus on the Gilead conflicts of interest
with the Scientific Council;

  * •

E7 focus on the political figure of Pr. Raoult in Marseille;

  * •

E8 is the late debate in relation with the political system, also showing a
peak during the summer, related to the introduction of the sanitary pass.

### The geography of the hcq debate

In the following we will analyze how the discussion on hcq entered the more
general Twitter debate on COVID-19 in different French-speaking countries.

A country analysis of the user base of this database reports a heterogeneous
distribution of the activity, with most involved users associated to France
($\sim$ 254k) while other countries counting from 1k to 40k users (see Fig. 1,
bottom).

The intra- and inter-country flow of information is captured by the hypergraph
framework and the random-walk model for the dynamical spreading of information
[33, 30]. With this framework we can capture the probability of a single piece
of information to be trapped into the original country as in Eq. 3.

Figure 3: Inward information flow. Amount of information trapped within the
country borders on the global hypergraph projected to countries (dark blue)
and removing French users (light blue). In both cases the probability of being
trapped in a country reaches 90%. The same analysis is performed on the subset
of tweets on hcq (dark and light orange). In this case when ignoring the
contribution of France the trapping probability drops to 80% in the first
period.

In Fig. 3 we compare the whole debate on COVID-19 and hcq along the whole
period, to the discussion on hcq. The total trapping probability
$p^{\circlearrowleft}=\sum_{C}p_{C}^{\circlearrowleft}$ form Eq. 3, is high in
both cases, reaching 90% over the whole time window. Since users from France
account for 52% of all geotagged users, we removed their tweets and retweets
to have a better overview of the international discussion. Fig. 3 reports,
with dimmed colors, the total trapping probability for the full dataset and
for hcq subset, when ignoring France. In this case, the discussion on hcq is
slightly less trapped inside the country borders, lowering to around 80–85%
the total trapping probability, in particular in the first period. This
reveals a slightly more international dimension of the discussion on hcq on
other countries, that is hidden by the huge volume of tweets coming
specifically from France (dark orange).

Figure 4: Comparison of topics extracted by nntf with (left) and without
(right) France. Word clouds of hashtags per component (above, hashtag
saturation change is for improved readability). The only component that
includes hcq related hashtags is A3, a long-lasting component in the
discussion including French users. When considering all countries but France
(right), no component has an important contribution to hcq topics. On the
stack plot of component strength (below), some components are temporally
limited (linked to events) while others are visible in most of the period of
interest (long-lasting topics).

The nntf let us extract the components or topics of the temporal hashtag co-
occurrence tensor $T$ and their temporal activity. Fig. 4 (top) displays the
word clouds of each of the 9 components extracted from the tensor, when
considering both COVID-19 and hcq debates. The lower part of the figure
represents the relative strength of each component. Note that the most active
topic is A3 and this is the only one that contains keywords related to hcq.
While this topic is active for the whole period, the other topics are limited
in time and are mostly related to one-time events (e.g. topics A0, A1 and A2
are related to the onset of the pandemics, A8 to the spreading of new virus
variants and the other to particular French events).

Figure 5: Comparison of topics on the first (left) and second (right) period,
per country. Word clouds representing each component (upper plots) show the
different distribution of topics in the two periods. On the lower plots, a
representation of the relative local strength of each component per country is
depicted (countries are sorted depending on the inner relative strength of
components related to hcq: C2, C4, D1 and D3).

Also in this case, to have a better international view of the topics, we
repeated the analysis removing tweets from French users, see the right part of
Fig 4. In this framework, while some topics are limited in time as for the
full dataset (see B6, B7 and B8), others have a persistent activity in time
and share a comparable volume of strength. The latter components are related
to country level discussions and in none of the cases there is a reference to
a hcq keyword.

Similarly, we extract geographic topics by factorizing the country hashtag co-
occurrence tensor $K$. We divide this analysis based on the natural temporal
separation mentioned earlier in this section, and the fact that the strength
of the hcq related components (A3 of Fig. 4 (left)) has higher expression in
the first period (up to July 2020).

In the first period (up to July 2020) all components are widely spread among
countries, although those related to hcq are stronger in European countries
(France (FR), Switzerland (CH), Belgium (BE) and Luxembourg (LU)) and Canada,
see Fig. 5 (left). On the bottom of Fig. 5, the countries are sorted depending
on the relative strength of hcq components (C2 and C4) and European countries
and Canada align to the right of the plot. This effect is even more extreme
during the second period (Fig. 5, right) where the strength of components D1
and D3 in countries other than Europe and Canada is negligible. Similarly, we
can see the raise of components that attract less attention from the African
countries and get localized in the Northern Hemisphere: health pass (D2) and
vaccines (D4).

Figure 6: Map of countries connected throughout the online discussion. Link
weights correspond to the probability of reaching the neighboring country
(links below the average country outgoing probability are hidden for
readability). Country size is proportional to the square root of the number of
users. Color codes represent the relative component strength of C2 and C4 (the
components on topics related to hcq). Note that the components C2 and C4
localize on the European countries and Canada.

On Fig. 6, the countries’ hcq component strength is plotted on a map. Country
colors represent the cumulative strength of component C2 and C4 in the
decomposition of the first period (as in Fig. 5, left). This figure highlights
how most of the countries where the hcq components have higher strength,
localize in Europe or North America (expect Togo (TG) which is tightly
connected to Canada (CA)). In this plot link weights are proportional to the
probability of the random walker to reach other countries, as in Eq. 2. Links
are hidden if their strength is lower than the expected given the origin node
strength.

## Discussion

Debates on hcq promptly took an international dimension, fueled by the
position taken by prominent figures such as the President of the United
States, but also thanks to ngo such as the who. However, the management of the
epidemic has demonstrated that public health issues are largely country’s
affaires [38], and as such the debates that occupy citizens are often on a
local or national scale. National public health decisions will influence and
guide the local debate on related topics. Beyond national borders, countries
linked by a common history and areas of similar language use the latter to
create a flow of information.

Based on the general dynamics of exchanges around hcq on Twitter, we showed
the existence of two principal periods: one of very strong engagement between
March and July 2020, and then low-level exchanges. This largely corresponds to
the internationalised “scientific” discussion that has taken place around the
publication of results in the main journals and in particular the preliminary
results of national trials (DISCOVERY in France, SOLIDARITY by who
internationally), which are gradually helping to stabilise knowledge on the
ineffectiveness of the treatment [20, 1]. Although debates on hcq diluted
during the second period as a central theme, they were still a subject of
exchanges on Twitter. In France, this can be explained by a set of public
events related to the hcq: the vaccination challenge, communications from ihu
and Pr. Didier Raoult’s, notably on Youtube, the legal action against the
institute. In doing so, we draw up a first long chronology of the debates
concerning hcq and more generally COVID-19 on Twitter, by identifying the main
associated themes. This confirms the pre-eminence of discussions on the
political management of the crisis.

Our article also shows the importance of national debates and controversies,
beyond the volume of exchange in each country. Contrary to the impression that
discussions on hcq have been internationalised around a schedule determined by
scientific events, exchanges on Twitter are largely defined by national
circulation. The high degree of recapture of tweets on a national scale
indicates a strong nationalisation of the debates. The slightly lower
recapture of hcq-specific debates can then be explained not only by the
internationalisation of the debates during the first period but also by the
importance taken by certain actors in promoting this treatment which has
crossed national borders. However, the results indicate that the very central
and media-heavy polemics on hcq in France did not have the same impact in
other countries. Thus, while explicitly anti-vaccine positions may have
continued to promote hcq as a way to maintain alternative course of action
[39], these debates have had no reason to exist in countries where this
treatment has become part of the standard management of the disease [19].

These results on the national and international circulation around a situation
of uncertainty mixing scientific controversy and political debate make it
possible, more generally, to inform a geographic approach to digital polemics
by relocating the interactions in their national contexts, making it possible
to situate the case studies that are often focused on certain countries.
During a global pandemic involving highly differentiated management of the
disease, this makes it possible to show that topics controversial and debated
in one country are not necessarily so in another. This work open the
possibility to compare national dynamics of controversies, by completing the
exchanges in social media with a thick description of national public debates,
especially in the legacy media.

Possible biases of this work include the user geotagging based on special
keywords that appears in the user profile. With this approach we may mis-
assign users that, while linked to the country they refer to in their profile,
are located abroad. Nevertheless, strong links with the original country drive
the user, and most of the conversation happens within likewise users as Fig 3
suggests.

We limited our study to the French Twittosphere, being France one of the focal
points of the hcq discussion, however the results we pointed out in this
analysis suggest the need to deepen this study focusing on its
internationalization and to the global temporal dimension at the world scale.

## Declarations

### Availability of data and materials

The dataset analyzed during the current study (namely the tweet and retweet
IDs) are available in the DataCovHCQ repository:
https://github.com/maurofaccin/DataCovHCQ [31]. The same repository contains
the Python scripts used for the dataset analysis.

### Competing interests

The authors declare that they have no competing interests.

### Funding

Part of M.F. contribution was supported by Agence Nationale de la Recherche
(ANR) through project TRACTRUST, (ANR-20-COVI-0102) and Agence nationale de
recherches sur le sida-Maladies infectieuses émergentes (ANRS-MIE), project
MEDIACAM-ANRSCOV24.

### Authors’ contributions

All authors partecipated to the design of the work.

### Acknowledgements

All authors thank Jeremy K. Ward for fruitful discussion.

### Authors’ information

Not applicable

## Abbreviations

## References

  * [1] Gould, S., Norris, S.L.: Contested effects and chaotic policies: the 2020 story of (hydroxy) chloroquine for treating COVID-19 (2021). doi:10.1002/14651858.ED000151
  * [2] Schwartz, I.S., Boulware, D.R., Lee, T.C.: Hydroxychloroquine for COVID19: The curtains close on a comedy of errors. The Lancet Regional Health - Americas 11, 100268 (2022). doi:10.1016/j.lana.2022.100268
  * [3] Hallin, D.C., Brandt, M., Briggs, C.L.: Biomedicalization and the public sphere: Newspaper coverage ofhealth and medicine, 1960s-2000s. Social Science and Medicine 96, 121–128 (2013). doi:10.1016/j.socscimed.2013.07.030
  * [4] Gargiulo, F., Cafiero, F., Guille-Escuret, P., Seror, V., Ward, J.K.: Asymmetric participation of defenders and critics of vaccines to debates on French-speaking Twitter. Scientific Reports 10(1), 6599 (2020). doi:10.1038/s41598-020-62880-5. 1909.08311
  * [5] Tuccori, M., Convertino, I., Ferraro, S., Cappello, E., Valdiserra, G., Focosi, D., Blandizzi, C.: The Impact of the COVID-19 “Infodemic” on Drug-Utilization Behaviors: Implications for Pharmacovigilance. Drug Safety 43(8), 699–709 (2020). doi:10.1007/s40264-020-00965-w
  * [6] Rughiniş, C., Dima, L., Vasile, S.: Hydroxychloroquine and COVID-19: Lack of Efficacy and the Social Construction of Plausibility. American Journal of Therapeutics 27(6), 573–583 (2020). doi:10.1097/MJT.0000000000001294
  * [7] Mutlu, E.C., Oghaz, T., Jasser, J., Tutunculer, E., Rajabi, A., Tayebi, A., Ozmen, O., Garibay, I.: A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19. Data in Brief 33, 106401 (2020). doi:10.1016/j.dib.2020.106401. 2009.01188
  * [8] Pearson, H.: How COVID broke the evidence pipeline. Nature 593(7858), 182–185 (2021). doi:10.1038/d41586-021-01246-x
  * [9] Schultz, É., Ward, J.K.: Science under Covid-19’s magnifying glass: Lessons from the first months of the chloroquine debate in the French press. Journal of Sociology 58(1), 76–94 (2022). doi:10.1177/1440783321999453
  * [10] Marcon, A.R., Caulfield, T.: The Hydroxychloroquine Twitter War: A case study examining polarization in science communication. First Monday (2021). doi:10.5210/fm.v26i10.11707
  * [11] Origgi, G., Branch-smith, T., Trust, T.M.: Why Trust Raoult? How Social Indicators Inform the Reputations of Experts. Social Epistemology (2022)
  * [12] Krause, N.M., Freiling, I., Scheufele, D.A.: The “Infodemic” Infodemic: Toward a More Nuanced Understanding of Truth-Claims and the Need for (Not) Combatting Misinformation. Annals of the American Academy of Political and Social Science 700(1), 112–123 (2022). doi:10.1177/00027162221086263
  * [13] Ektorp, E.: Death threats after a trial on chloroquine for COVID-19. The Lancet. Infectious diseases 20(6), 661 (2020). doi:10.1016/S1473-3099(20)30383-2
  * [14] Berlivet, L., Löwy, I.: Hydroxychloroquine Controversies: Clinical Trials, Epistemology, and the Democratization of Science. Medical Anthropology Quarterly 34(4), 525–541 (2020). doi:10.1111/maq.12622
  * [15] Schultz, É., Atlani-Duault, L., Peretti-Watel, P., Ward, J.K.: Does the public know when a scientific controversy is over? Public perceptions of hydroxychloroquine in France between April 2020 and June 2021. Therapies (January) (2022). doi:10.1016/j.therap.2022.01.008
  * [16] Smyrnaios, N., Tsimboukis, P., Loubère, L.: La controverse autour de Didier Raoult et de sa proposition thérapeutique contre le Covid-19 sur Twitter : analyse de réseaux et de discours. Communiquer. Revue de communication sociale et publique 31, 1–20 (2021)
  * [17] Dubois, M., Frenod-Dunaud, A., Gargiulo, F., Guaspare-Carton, C.: Le contrôle par les pairs au temps du coronavirus. In: Chauvin, P.M., Clement, A. (eds.) Sorbonnavirus, Regards sur la Crise du Coronavirus. Sorbonne Université Presses, ??? (2021)
  * [18] Bharti, N., Sismondo, S.: Political Prescriptions: Three Pandemic Stories. Science, Technology & Human Values, 016224392211238 (2022). doi:10.1177/01622439221123831
  * [19] Desclaux, A.: Covid-19: En Afrique de l’Ouest, le vaccin n’est pas le nouveau “magic bullet” (2022)
  * [20] Axfors, C., Schmitt, A.M., Janiaud, P., van’t Hooft, J., Abd-Elsalam, S., Abdo, E.F., Abella, B.S., Akram, J., Amaravadi, R.K., Angus, D.C., Arabi, Y.M., Azhar, S., Baden, L.R., Baker, A.W., Belkhir, L., Benfield, T., Berrevoets, M.A.H., Chen, C.P., Chen, T.C., Cheng, S.H., Cheng, C.Y., Chung, W.S., Cohen, Y.Z., Cowan, L.N., Dalgard, O., de Almeida e Val, F.F., de Lacerda, M.V.G., de Melo, G.C., Derde, L., Dubee, V., Elfakir, A., Gordon, A.C., Hernandez-Cardenas, C.M., Hills, T., Hoepelman, A.I.M., Huang, Y.W., Igau, B., Jin, R., Jurado-Camacho, F., Khan, K.S., Kremsner, P.G., Kreuels, B., Kuo, C.Y., Le, T., Lin, Y.C., Lin, W.P., Lin, T.H., Lyngbakken, M.N., McArthur, C., McVerry, B.J., Meza-Meneses, P., Monteiro, W.M., Morpeth, S.C., Mourad, A., Mulligan, M.J., Murthy, S., Naggie, S., Narayanasamy, S., Nichol, A., Novack, L.A., O’Brien, S.M., Okeke, N.L., Perez, L., Perez-Padilla, R., Perrin, L., Remigio-Luna, A., Rivera-Martinez, N.E., Rockhold, F.W., Rodriguez-Llamazares, S., Rolfe, R., Rosa, R., Røsjø, H., Sampaio, V.S., Seto, T.B., Shehzad, M., Soliman, S., Stout, J.E., Thirion-Romero, I., Troxel, A.B., Tseng, T.Y., Turner, N.A., Ulrich, R.J., Walsh, S.R., Webb, S.A., Weehuizen, J.M., Velinova, M., Wong, H.L., Wrenn, R., Zampieri, F.G., Zhong, W., Moher, D., Goodman, S.N., Ioannidis, J.P.A., Hemkens, L.G.: Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials. Nature Communications 12(1), 1–13 (2021). doi:10.1038/s41467-021-22446-z
  * [21] Taccone, F.S., Hites, M., Dauby, N.: From hydroxychloroquine to ivermectin: how unproven “cures” can go viral. Clinical Microbiology and Infection 28(4), 472–474 (2022). doi:10.1016/j.cmi.2022.01.008
  * [22] Casarões, G., Magalhães, D.: The hydroxychloroquine alliance: how far-right leaders and alt-science preachers came together to promote a miracle drug. Revista de Administracao Publica 55(1), 197–214 (2021). doi:10.1590/0034-761220200556
  * [23] Osuagwu, U.L., Nwaeze, O., Ovenseri-Ogbomo, G., Oloruntoba, R., Ekpenyong, B., Mashige, K.P., Timothy, C., Ishaya, T., Langsi, R., Charwe, D., Abu, E.K., Chundung, M.A., Agho, K.E.: Opinion and uptake of chloroquine for treatment of COVID-19 during the mandatory lockdown in the sub-Saharan African region. African Journal of Primary Health Care & Family Medicine 13(1), 1–8 (2021). doi:10.4102/phcfm.v13i1.2795
  * [24] Mukankubito, I., Annan, E.A., Sougou, A., Taguembou, D., Kniazkov, S., Loua, A., Mankele, R., Nikiema, J.-B., Bisoborwa, G., Julius, O.K.M.: COVID-19 Treatment Protocols in the WHO African Region - Results of a Survey, 1–19 (2021)
  * [25] Atlani-Duault, L., Dozon, J.P., Wilson, A., Delfraissy, J.F., Moatti, J.P.: State humanitarian verticalism versus universal health coverage: A century of French international health assistance revisited. The Lancet 387(10034), 2250–2262 (2016). doi:10.1016/S0140-6736(16)00379-2
  * [26] Lapostolle, F., Vianu, I., De Stefano, C., Goix, L., Petrovic, T., Adnet, F.: COVID-19 Epidemic: Chloroquine, a French Obsession? La Presse Médicale Open 2, 100007 (2021). doi:10.1016/j.lpmope.2021.100007
  * [27] Roy, M., Moreau, N., Rousseau, C., Mercier, A., Wilson, A., Atlani-Duault, L.: Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014–2015 Ebola Epidemic. Culture, Medicine and Psychiatry 44(1), 56–79 (2020). doi:10.1007/s11013-019-09635-8
  * [28] Ooghe-Tabanou, B., Farjas, J., Mazoyer, B.: Gazouilloire, Twitter stream + search API grabber. https://github.com/medialab/gazouilloire
  * [29] Faccin, M.: maurofaccin/DataCovVac. doi:10.5281/zenodo.7870249. https://doi.org/10.5281/zenodo.7870249
  * [30] Faccin, M., Gargiulo, F., Atlani-Duault, L., Ward, J.K.: Assessing the influence of french vaccine critics during the two first years of the COVID-19 pandemic. arxiv:2202.10952 (2022). 2202.10952
  * [31] Faccin, M.: maurofaccin/DataCovHCQ. doi:10.5281/zenodo.7870286. https://doi.org/10.5281/zenodo.7870286
  * [32] Ausiello, G., Laura, L.: Directed hypergraphs: Introduction and fundamental algorithms—a survey. Theoretical Computer Science 658, 293–306 (2017). doi:10.1016/j.tcs.2016.03.016
  * [33] Faccin, M.: Measuring dynamical systems on directed hypergraphs. arxiv:2202.12810 (2022). 2202.12810
  * [34] Shashua, A., Hazan, T.: Non-negative tensor factorization with applications to statistics and computer vision. In: Proceedings of the 22nd International Conference on Machine Learning - ICML 2005. ACM Press, ??? (2005). doi:10.1145/1102351.1102451. https://doi.org/10.1145/1102351.1102451
  * [35] Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.-I.: Nonnegative Matrix and Tensor Factorizations. John Wiley & Sons, Ltd, ??? (2009). doi:10.1002/9780470747278. https://doi.org/10.1002/9780470747278
  * [36] Mørup, M.: Applications of tensor (multiway array) factorizations and decompositions in data mining. WIREs Data Mining and Knowledge Discovery 1(1), 24–40 (2011). doi:10.1002/widm.1
  * [37] Gauvin, L., Panisson, A., Cattuto, C.: Detecting the community structure and activity patterns of temporal networks: A non-negative tensor factorization approach. PLoS ONE 9(1), 86028 (2014). doi:10.1371/journal.pone.0086028
  * [38] Haldane, V., De Foo, C., Abdalla, S.M., Jung, A.S., Tan, M., Wu, S., Chua, A., Verma, M., Shrestha, P., Singh, S., Perez, T., Tan, S.M., Bartos, M., Mabuchi, S., Bonk, M., McNab, C., Werner, G.K., Panjabi, R., Nordström, A., Legido-Quigley, H.: Health systems resilience in managing the COVID-19 pandemic: lessons from 28 countries. Nature Medicine 27(6), 964–980 (2021). doi:10.1038/s41591-021-01381-y
  * [39] Bertin, P., Nera, K., Delouvée, S.: Conspiracy Beliefs, Rejection of Vaccination, and Support for hydroxychloroquine: A Conceptual Replication-Extension in the COVID-19 Pandemic Context. Frontiers in Psychology 11(September), 1–9 (2020). doi:10.3389/fpsyg.2020.565128