Anne-Sophie Ribba

Morgane Batzenschlager

Clotilde Rabat

Thierry Buchou

Sylvie Moog

Saadi Khochbin

Ekaterina Flin

Laurence Lafanechère

François Lanza
email: francois.lanza@efs.sante.fr

Karin Sadoul
email: karin.sadoul@univ-grenoble-alpes.fr

Marginal band microtubules are acetylated by αTAT1

Keywords: Tubulin, microtubules, acetylation, post-translational modification, acetyltransferase

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Introduction

Platelets are discoid cell fragments patrolling in the circulation to check vessel integrity. In case of vessel injury they undergo activation, change their shape from a disc to a sphere and extend filopodia before spreading on the injured vessel surface. Other incoming platelets get recruited and aggregate to form a hemostatic plug, which they then retract to re-establish unhindered blood flow in the damaged vessel. All these events are highly dependent on the reorganization of the cytoskeleton, especially of actin fibers and microtubules. These reorganizations have to be coordinated in a precise and efficient way to avoid bleeding complications and therefore depend on diverse regulatory mechanisms including reversible post-translational modifications. One of these modifications is the acetylation of marginal band microtubules at the residue lysine 40 of the α-tubulin subunit [START_REF] Patel-Hett | Visualization of microtubule growth in living platelets reveals a dynamic marginal band with multiple microtubules[END_REF]. We have shown previously that microtubule motor protein actions are implicated in the reorganization of marginal band microtubules during the activation process [START_REF] Diagouraga | Motor-driven marginal band coiling promotes cell shape change during platelet activation[END_REF] and it is known that microtubule acetylation can influence the kinetics of motor actions [START_REF] Dompierre | Histone deacetylase 6 inhibition compensates for the transport deficit in Huntington's disease by increasing tubulin acetylation[END_REF][START_REF] Reed | Microtubule acetylation promotes kinesin-1 binding and transport[END_REF]. We have also demonstrated that deacetylation rapidly follows platelet activation and is mediated by the deacetylase HDAC6. Platelets of mice deficient in HDAC6 have hyper-acetylated marginal bands and tubulin acetylation is maintained throughout all steps after platelet activation [START_REF] Sadoul | HDAC6 controls the kinetics of platelet activation[END_REF]. Furthermore, there is a positive correlation between the kinetics of platelet activation/spreading and the higher acetylation state. We therefore decided to search for the acetyltransferase able to acetylate marginal band microtubules and identified the major tubulin acetyltransferase αTAT1 [START_REF] Akella | MEC-17 is an alpha-tubulin acetyltransferase[END_REF][START_REF] Kalebic | alphaTAT1 is the major alpha-tubulin acetyltransferase in mice[END_REF] as the responsible enzyme. We, thus, characterized platelets and megakaryocytes of mice deficient in αTAT1 and examined the functional consequences of αTAT1 deficiency for platelet production and physiology.

Methods

Reagents and antibodies

The following antibodies and reagents were used: mouse monoclonal anti-acetylated tubulin (clone 6-11B-1, Sigma, T6793); mouse monoclonal anti--tubulin (clone B-5-1-2, Sigma, T5168); rabbit monoclonal anti--tubulin (clone EP1332Y, Millipore, 04-1117); mouse monoclonal anti-actin (MP Biomedicals, 691001); rat monoclonal anti-integrin β3 (LucA5, Emfret, M030-0); Alexa Fluor 546 goat anti-rat IgG (Invitrogen, A11081); Alexa Fluor 488 goat anti-mouse IgG (Invitrogen, A11029); Alexa Fluor 546 goat antirabbit IgG (Invitrogen, A11035); HRP goat anti-mouse IgG (Cell Signaling, 5127); phalloidin-rhodamine (Sigma, P1951); poly-D-lysine (Sigma, P7280); thrombin (Sigma, T4648); pluronic F-127 (Sigma, P2443), Mowiol 4-88 (Calbiochem, 475904)

Mouse breeding

Mice were bred in the High Technology Animal Facility (PHTA) of the Grenoble Alpes University. Mouse experiments were performed in accordance with the institutional guidelines of the European Community and approved by an Ad Hoc committee under the registration number 315_UHTA-U1209-KS-01. All investigators directly involved in the project have an official animal-handling authorization obtained after 2 weeks of intensive training and a final formal evaluation. HDAC6KO and αTAT1KO mice (for KO strategy see [START_REF] Zhang | Mice lacking histone deacetylase 6 have hyperacetylated tubulin but are viable and develop normally[END_REF] and [START_REF] Kalebic | alphaTAT1 is the major alpha-tubulin acetyltransferase in mice[END_REF], respectively) were backcrossed regularly to C57BL/6 mice and used for experiments between 7 and 10 months of age.

Preparation of mouse platelet rich plasma (PRP)

Blood was collected into 100 μL 3.2% Na-citrate from anesthetized mice by cardiac puncture. Na-citrate volumes were adjusted according to recovered blood volumes. Blood with identical genotypes was pooled, centrifuged for 15 min, 200 g, no break, at RT to obtain the PRP. Platelets were counted and PRP was adjusted with the corresponding plasma to obtain an equal platelet concentration for all genotypes.

Resting and spread platelets

PRPs were diluted in PBS/1mM CaCl2 to a concentration of 2.5x10 6 platelets/mL. For the platelet spreading assay, 400 μL of these suspensions were added into wells of a 24-well plate containing coverslips and centrifuged for 3 min, 600g, at RT to allow simultaneous contact of all platelets with the glass surface for synchronised spreading. The plate was then placed into a cell culture incubator for 15 min. Platelets were then fixed in 4% isotonic formalin. For fixation of resting platelets, 400 μL of the suspension was added into wells of a 24-well plate containing coverslips (coated with 20 μg/mL poly-D-lysine) and 800 μL isotonic formalin. After 10 min of fixation, plates were centrifuged for 5 min, 600g, at RT.

Clot retraction assay

Platelet concentration was adjusted to 2.5x10 8 platelets/mL in 50% plasma/50% PBS and a small amount of erythrocytes was added for color contrast. Clots of 200 μL were allowed to form by addition of thrombin (final concentration 0.5U/mL) in glass tubes coated with 2% pluronic and containing a plastic loop. After 15-20 min at 37°C clots were removed and transferred into new tubes containing 200 μL of PBS. The extruded serum volume was quantified and expressed as % of the initial volume. Progression of clot retraction was followed by taking photos at different time points.

Platelet counts

Platelet counts and mean platelet volumes were determined in a Scil Vet ABC Counter (Scil Animal care company, Altorf, France).

MK culture

Bone marrow cells were flushed from femurs and tibias and passed successively through 21-, 23-and 25-gauge needles. Cells were then pelleted at 1200 rpm for 7 min and resuspended at 1×10 8 cells/mL in PBS supplemented with 5% (v/v) rat serum and 2 mM EDTA to perform a negative lineage antigen selection (Lin selection, Stem Cell Technologies). The Lin negative population was adjusted to 5×10 6 cells/mL in DMEM containing 2 mM L-glutamine, penicillin/streptomycin, 10% FBS and 50 ng/mL thrombopoietin with addition of 50 U/mL hirudin. Cultures were usually performed in 12-well tissue culture plates with 700 μL of cell suspension per well and incubated at 37°C under a 5% CO2 atmosphere for up to 5 days.

Immunofluorescence

Fixed platelets on coverslips were permeabilized with PBS/0.2% Triton-X-100 for 15 min at RT and then incubated with blocking buffer (3% BSA, 10% goat serum in PBS) for 1 h at RT. Coverslips were then incubated for 2 h with primary antibodies diluted in blocking buffer, then washed 2x PBS and 1x PBS/0.2% Triton-X-100 and incubated for 2 h at RT with secondary antibodies diluted in blocking buffer. After 3 washing steps coverslips were mounted on glass slides using Mowiol. Fluorescent images were acquired using an upright microscope (Olympus BX41) equipped with a color camera DP70 using a 100x oil immersion objective.

Western blot

To prepare platelet lysates, PRP was diluted in PBS and centrifuged for 2 min at 9800g, the platelet pellet was lysed in 2x Laemmli sample buffer and heated at 95°C for 5 min. Proteins of platelet lysates were separated by 10% SDS-PAGE (2x10 6 platelets/10μL Laemmli sample buffer/lane) and transferred onto PVDF membranes for chemiluminescence detection of specific proteins using the indicated antibodies. Signals were quantified using the Bio-Rad ChemiDoc imaging system and the Image Lab analysis software.

Statistical analysis

P-values for differences in the clot retraction assay have been calculated using the Wilcoxon matched pairs test and p-values for platelet counts and volumes were determined using an unpaired, two-tailed t-test.

Results

Although several enzymes have been described as having tubulin acetylation activity, αTAT1 appears to be the major tubulin acetyltransferase [START_REF] Sadoul | The growing landscape of tubulin acetylation: lysine 40 and many more[END_REF]. We therefore hypothesized that αTAT1 could also be the major tubulin acetyltransferase in platelets. To test this hypothesis, we took advantage of the availability of a mouse line deficient in αTAT1 [START_REF] Kalebic | alphaTAT1 is the major alpha-tubulin acetyltransferase in mice[END_REF]. In order to compare the tubulin acetylation level in resting platelets from wild type (WT), HDAC6KO and αTAT1KO mice, platelet rich plasma (PRP) was prepared from mice of each genotype. Platelet lysates were then analyzed by Western blot for the presence of α-tubulin acetylated on lysine 40. In accordance with our previous publication, we observed tubulin acetylation in WT platelets and a much higher acetylation level in HDAC6 deficient platelets, indicating that the enzyme responsible of tubulin acetylation is active in resting platelets [START_REF] Sadoul | HDAC6 controls the kinetics of platelet activation[END_REF]. We expected complete absence of tubulin acetylation in αTAT1 -/-platelets. However, quite a strong residual signal for acetylated tubulin was detected in the αTAT1 deficient platelets. We performed four independent experiments for the quantification of tubulin acetylation levels and obtained the following ratios: T -/- /WT=0.64±0.1, H -/-/WT=9.5±1.9, mean±sem (Figure 1A). In order to see whether the signal of acetylated tubulin is enhanced in WT and αTAT1 -/-platelets after deacetylase inhibition, PRP was incubated with TSA (trichostatin A; inhibitor of type I and II deacetylases including HDAC6) and nicotinamide (inhibitor of type III deacetylases). As shown in supplementary figure 1A, an enhanced signal for acetylated tubulin is observed only in WT platelets. The fact that the signal is not enhanced in αTAT1 -/- platelets suggests that the enzymes could be regulated themselves by their acetylation level as it has been shown for certain acetyltransferases and deacetylases [START_REF] Sadoul | The growing landscape of tubulin acetylation: lysine 40 and many more[END_REF]. We then decided to use immunofluorescence to evaluate tubulin acetylation levels of the marginal band in resting platelets. While HDAC6 deficient platelets have hyperacetylated marginal bands compared to WT platelets, αTAT1KO platelets show a complete absence of marginal band acetylation. However, a faint, diffuse signal is visible in the center of resting platelets (Figure 1B). In spread platelets the hyperacetylated state of microtubules persists in HDAC6 -/-platelets, as shown previously [START_REF] Sadoul | HDAC6 controls the kinetics of platelet activation[END_REF], and a faint signal is also visible in the spread αTAT1 -/-platelets (Figure 1B). We have previously reported that the higher acetylation level of HDAC6 -/-platelets correlates with faster activation/spreading kinetics. Accordingly, we wondered whether αTAT1 -/-platelets would show a retardation of their spreading capacity. We decided to use again a spreading assay to compare the speed of activation and spreading between the different genotypes. HDAC6 deficient platelets spread faster than WT platelets confirming our previously published observations, while αTAT1 deficient platelets behave similar to WT platelets under these conditions (Figure 1C). We also used a clot retraction assay to compare the functional efficiency of the three different platelet populations. Clot retraction reflects the capacity of platelets to activate and to perform a "three-dimensional spreading" within the fibrin network as well as their ability to subsequently reorganize their cytoskeleton to contract the fibrin network. While HDAC6 deficient platelets behave similar to WT platelets, we observe a slight retardation of clot retraction by αTAT1KO platelets (Figure 1D, see also supplementary figure 1B for a typical example of clot retraction kinetics).

Figure 1: Comparison of platelets from WT, αTAT1 and HDAC6 deficient mice A) Western blot of platelet lysates from WT, αTAT1 -/-(T -/-) and HDAC6 -/-(H -/-) mice probed for acetylated α-tubulin and actin as a loading control (2x10 6 platelets/lane). For quantification acetylated tubulin signals were adjusted according to actin loading controls and expressed as fold difference over WT platelets. Ratio of T -/

-/WT=0.64±0.1 and H -/- /WT=9.5±1.9; mean±sem of four independent experiments. B) Resting platelets and platelets spread for 1h from WT, αTAT1 -/-and HDAC6 -/-mice were fixed and stained for acetylated α-tubulin and total α-tubulin (scale bar 10 m).

C) Platelets were allowed to spread on glass coverslips for 15 min, before fixation and tubulin/β3-integrin staining. After determination of the surface area occupied by each platelet, the percentage of platelets in different size categories was plotted (values are mean±sem of 6 independent experiments). D) Clot formation is induced by addition of thrombin to PRP from WT, αTAT1 and HDAC6 deficient mice and the extruded serum volume after 20 min of clot retraction is quantified and expressed as % of the initial volume. Each experiment is performed in triplicates and

boxplots (min, max, median, Q1, Q3) are calculated from 12 independent experiments using 8 different PRPs. Values were tested for significance using the Wilcoxon matched pairs test.

It has recently been shown that Chinese hamster ovary cells expressing a mutant, partially active integrin receptor (αIIbβ3D723H) recapitulate certain steps of megakaryocyte differentiation. They form proplatelet-like extensions when cultured on fibrinogen and tubulin acetylation appears to be essential for the formation of these protrusions [START_REF] Van Dijk | Microtubule polyglutamylation and acetylation drive microtubule dynamics critical for platelet formation[END_REF]. This suggests that platelet production might be influenced by tubulin acetylation levels. Our previously unpublished observations comparing platelet counts of WT and HDAC6KO mice did not show a difference between the two genotypes (1240±64 x10 3 /μL and 1246±79 x10 3 /μL, mean±sem; 9 and 8 animals, respectively), indicating that tubulin hyper-acetylation has no detectable consequence on platelet production. To test whether the absence of microtubule acetylation might influence platelet production in αTAT1KO mice, we investigated primary megakaryocytes, which form proplatelet extensions after a 4-day differentiation period in vitro. The percentage of differentiating megakaryocytes with proplatelet-like extensions within the total population is similar for the two genotypes (Figure 2B). However, maturing WT megakaryocytes display a different microtubule network compared to αTAT1KO megakaryocytes. WT cells have a higher number of circular elements, while KO cells have more loosely arranged proplatelet-like extensions (Figure 2A). Yet, these in vitro observations did not translate into major defects in platelet biogenesis since platelet counts and mean platelet volumes were not or only slightly different between WT and KO mice (Figure 2C,D). Since there is a dynamic crosstalk between the actin and the microtubule part of the cytoskeleton, we investigated whether the microtubule acetylation level might have an impact on the actin cytoskeleton. However, as shown in the supplementary figure 2, no major differences can be visualized between the different genotypes.

Discussion

Our results indicate that αTAT1 is the acetyltransferase responsible for the acetylation of marginal band microtubules in platelets. However, a residual acetylation level of lysine 40 of the α-tubulin subunit is detected in αTAT1 deficient platelets. This is in agreement with an earlier study characterizing αTAT1KO mice, which show residual tubulin acetylation in different tissues [START_REF] Kim | Mice lacking alpha-tubulin acetyltransferase 1 are viable but display alpha-tubulin acetylation deficiency and dentate gyrus distortion[END_REF]. It has been reported that under certain conditions the mouse monoclonal anti acetylated tubulin antibody, clone 6-11B-1, recognizes deacetylated tubulin by immunofluorescence but not by Western blot [START_REF] Soppina | Luminal localization of alphatubulin K40 acetylation by cryo-EM analysis of fab-labeled microtubules[END_REF]. Thus, the possibility that this residual signal detected by Western blot is due to an unspecific recognition of unacetylated tubulin is very unlikely. An additional tubulin acetyltransferase must therefore be expressed in platelets, as well as other tissues. The relatively faint and diffuse acetylated tubulin staining present in the center of αTAT1KO platelets may be due to α-tubulin subunits acetylated by this other acetyltransferase, but clearly this other enzyme is unable to acetylate marginal band microtubules. Further studies will be necessary to identify the enzyme(s) involved. Potential candidates might be GCN5, NAT10 or the ARD1-NAT1 complex [START_REF] Sadoul | The growing landscape of tubulin acetylation: lysine 40 and many more[END_REF]. The acetyltransferase p300, which has been shown to play an important role in platelets [START_REF] Aslan | Lysine acetyltransfer supports platelet function[END_REF], could also directly or indirectly influence tubulin acetylation levels. The functional characterization of αTAT1 deficient platelets revealed only minor consequences of deficient microtubule acetylation. The fact that no significant difference in the spreading capacity was detected between platelets from WT and αTAT1KO mice but a delay in clot retraction may suggest that the absence of tubulin acetylation is less important for early activation events than for subsequent clot retraction. Several publications have shown a role of microtubules in clot retraction [START_REF] Chao | Similarities between platelet contraction and cellular motility during mitosis: role of platelet microtubules in clot retraction[END_REF][START_REF] Cohen | Ultrastructure of clots during isometric contraction[END_REF][START_REF] Jen | Platelet microtubules in clot structure formation and contractile force generation: investigation of a controversy[END_REF]. Microtubules could be important for the repeated, successive extensions and retractions of filopodia, which bind to fibrin strands to pull them together, thereby retracting the clot [START_REF] Kim | Quantitative structural mechanobiology of platelet-driven blood clot contraction[END_REF]. Extension of filopodia might depend on motor driven sliding of microtubules, as it is the case for the extension of proplatelets by megakaryocytes [START_REF] Patel | Differential roles of microtubule assembly and sliding in proplatelet formation by megakaryocytes[END_REF]. This, in turn, may be less efficient in αTAT1KO platelets, since microtubule motor actions are less productive on non-acetylated microtubules [START_REF] Dompierre | Histone deacetylase 6 inhibition compensates for the transport deficit in Huntington's disease by increasing tubulin acetylation[END_REF][START_REF] Reed | Microtubule acetylation promotes kinesin-1 binding and transport[END_REF]. Alternatively, the effect may be due to deficient acetylation of an additional substrate of αTAT1, such as cortactin [START_REF] Castro-Castro | ATAT1/MEC-17 acetyltransferase and HDAC6 deacetylase control a balance of acetylation of alpha-tubulin and cortactin and regulate MT1-MMP trafficking and breast tumor cell invasion[END_REF]. An altered insideout or outside-in signaling can also not be ruled out. Our results show that the tubulin acetylation level has no impact on platelet production in vivo, which is surprising in view of our observation that αTAT1 deficient megakaryocytes differentiated in vitro have an altered microtubule network. This may suggest that compensation mechanisms have been established in the mutant mice. The absence of a strong functional phenotype of both, HDAC6KO and αTAT1KO platelets, may point to a role of acetylation in the fine-tuning of microtubule reorganizations during platelet activation, spreading and retraction. Alternatively, tubulin acetylation might become important under shear stress conditions, when forces are exerted on microtubules. Indeed, it has been shown that resistance of microtubules to mechanical breakage is conferred by tubulin acetylation [START_REF] Xu | Microtubules acquire resistance from mechanical breakage through intralumenal acetylation[END_REF].

supplementary Figure 1: A) Platelets of WT, αTAT1 -/-(T -/-) and HDAC6 -/-(H -/-) mice were treated or not with TSA (0.1 μg/mL) and nicotinamide (5 mM) for 20 min. Tubulin acetylation levels in platelet lysates were determined by Western blot using an antibody against acetylated α-tubulin and antibodies against tubulin as well as actin as loading controls (2x10 6 platelets/lane). B) Thrombin (final concentration 0.5 U/mL) was added to a glass tube containing 200 μL PRP to induce clot formation. After 20 min clots are transferred into new glass tubes containing 200 μL PBS and contraction was allowed to continue for 50 min. The extruded serum volume after 20 min of contraction was measured to quantify clot retraction (see figure 1D). supplementary Figure 2: A) The actin cytoskeleton in platelets of WT, αTAT1 -/-and HDAC6 -/-mice after 1h of spreading stained with phalloidin-rhodamine (scale bar, 10 μm). B) The actin and tubulin cytoskeleton of megakaryocytes from WT and αTAT1 -/-mice differentiated for 4d in culture (scale bar, 10 μm).

Figure 2 :

 2 Figure 2: Platelet production in WT and αTAT1KO mice A) Megakaryocytes from WT and αTAT1 -/-mice differentiated for 4d in culture have been stained for a-tubulin, three representative examples are shown for each genotype (scale bar, 25 μm). B) Percentage of megakaryocytes with proplatelet-like extensions in the total population of Lin negative selected bone marrow cultures from WT and αTAT1 -/-mice after 4d of differentiation. C) Platelet counts in blood from WT and αTAT1 -/-mice (WT: 1354±36 x10 3 /μL, N=18 αTAT1 -/-: 1267±60 x10 3 /μL; N=16; mean±sem; p=0.276 Unpaired t-test, Two-tailed). D) Determination of platelet volumes from WT and αTAT1 -/-mice (WT: 4.83±0.03 fL, N=18; αTAT1 -/-: 4.94±0.04 fL; N=16; mean±sem; p=0.0226 Unpaired t-test, Two-tailed).

Acknowledgements

We are grateful to Paul Heppenstall for providing the αTAT1KO mice and Patrick Matthias for the HDAC6KO mice. We thank Thérèse Rossini for platelet count determination.

Funding

This work was supported by the Fondation Recherche Médicale (FRM, grant number DEI20151234416) and the University Grenoble Alpes (UGA, grant number AGIR-POLE FRAG15CS08) as well as by funds from the Association de Recherche et Développement en Médecine et Santé Publique (ARMESA to M.B.).

Disclosure statement

The authors have no declaration of interest to disclose.

Author contributions

A.-S.R., M.B., C.R., T.B., S.M., K.S. performed experiments. F.L. and K.S. designed experiments and wrote the paper. S.K. and L.L. financially contributed to the study. All authors interpreted results and reviewed the manuscript.


Jérôme Delobelle
email: jerome.delobelle@u-paris.frelena.cabrio

Amaury Delamaire
email: amaury.delamaire@storyzy.com

Elena Cabrio

Ramón Ruti
email: ramon.ruti@storyzy.com

Serena Villata
email: serena.villata@unice.fr

Sifting the Arguments in Fake News to Boost a Disinformation Analysis Tool

Keywords: Argument mining, Stance Detection, Fake news

The problem of disinformation spread on the Web is receiving an increasing attention, given the potential danger fake news represents for our society. Several approaches have been proposed in the literature to fight fake news, depending on the media such fake news are concerned with, i.e., text, images, or videos. Considering textual fake news, many open problems arise to go beyond simple keywords extraction based approaches. In this paper, we present a concrete application scenario where a fake news detection system is empowered with an argument mining model, to highlight and aid the analysis of the arguments put forward to support or oppose a given target topic in articles containing fake information.

Introduction

The phenomenon of disinformation, produced and transmitted on the Web via social media platforms, websites, and forums is not new, but it has taken on an unprecedented scale since 2016 with the campaign for the American presidential election, the Brexit campaign, and in 2017 with the French presidential campaign. These days the health emergency associated with Covid-19 has exacerbated this problem considerably. If the phenomenon of disinformation has a very long history in the form of rumor, it has taken, in the digital age, first the name of "fake news" then that of "fake information". A fake news deals both with the dissemination of information without certainty whether it is false or true, but also with the intention pursued by the author of the content to mislead the audience.

Given the potential danger fake news represent for the users, several approaches have been developed to address the automatic detection of fake news online (e.g., see [START_REF] Zhou | Fake news: Fundamental theories, detection strategies and challenges[END_REF]). The proposed methods may vary depending on the type of media the fake news is spread upon, i.e., text, images, or videos. Among those, we find algorithms that automatically identify fake news by recognising keywords in the text [START_REF] Oshikawa | A survey on natural language processing for fake news detection[END_REF], by detecting the reuse of misleading images using their chronology, and by detecting the angle of the face, its expression, the lighting and other important information to verify the authenticity of videos about people [START_REF] Tolosana | Deepfakes and beyond: A survey of face manipulation and fake detection[END_REF]. In general, the task of fake news detection aims at easing the work of human analysts that have to investigate the ways fake news spread on the Web to then find a way to limit this phenomenon. Therefore, providing automatic (or semi-automatic) tools to make the analysis of fake news more effective to analysts is a main open challenge.

In this paper, we address this issue by proposing an argumentation-based disinformation analysis tool to support analysts to investigate the diffusion of fake news according to several criteria. More precisely, we propose to extend a disinformation analysis tool with a stance detection module [START_REF] Küc | Stance detection: A survey[END_REF] relying on pretrained language models, i.e., BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF], with the aim of obtaining a more effective analysis tool both for users and analysts. To evaluate the stance detection module in the disinformation context, we propose to annotate a new resource of fake news articles, where arguments are classified as being InFavor or Against towards a target topic. Our new annotated data set contains sentences about three topics currently attracting a lot of fake news around them, i.e., public health demands vaccination, white helmets provide essential services, and the risible impact of Covid-19. This data set collects 86 articles containing nearly 3000 sentences.

Although argumentation is an area of research in Artificial Intelligence that has received an increasing attention in recent years, few links have been made to bridge it with the detection of fake news. Among these works, Sethi [START_REF] Sethi | Spotting fake news: A social argumentation framework for scrutinizing alternative facts[END_REF] propose a prototypical social argumentation framework to verify the validity of proposed alternative facts to help in limiting the propagation of fake news. The debate about the veracity of a given fact is represented through a basic argumentative structure (claims, evidences and sources connected by a support relation and an attack relation), and it is crowdsourced and mediated by expert moderators in a virtual community. However, the argumentative part is limited to the formalisation of the debate, and no empirical evaluation is addressed. This approach does not deal with natural language arguments. More recently, Kotonya and Toni [START_REF] Kotonya | Gradual argumentation evaluation for stance aggregation in automated fake news detection[END_REF] introduce a new method for veracity prediction based on a form of argumentative aggregation. More precisely, they use stance label predictions for relation-based argument mining to generate a bipolar argumentation framework [START_REF] Cayrol | On the acceptability of arguments in bipolar argumentation frameworks[END_REF] which is a triple composed of a set of arguments, an attack and a support relation between arguments. Each argument is then evaluated with the DF-QuAD gradual semantics [START_REF] Rago | Discontinuity-free decision support with quantitative argumentation debates[END_REF] in order to assess the veracity of the news against some evidence. However, their method applies to Twitter conversations, and more precisely, to the RumourEval dataset 3 , where they consider each tweet in the conversation as a potential argument which discusses/disagrees/agrees with another tweet. They do not propose any concrete integration of their approach to a fake news detection system, as it is the case for our stance detection module.

In this paper, we do not present a new approach to detect fake news online. We propose an argumentation-empowered disinformation analysis tool to support analysts in a better understanding of the fake news content and structure, to conceive more effective solutions to fight its spread. To the best of our knowledge, this is the first concrete tool for fake news classification based on a stance detection module to aid the analysis of fake news and their diffusion.

The paper is organised as follows. Section 2 introduces the fake news detection module and its main features. In Section 3, we describe the manually annotated data set of fake news we created, and we report on the performances of the stance detection module on such dataset. Section 4 describes the resulting Disinformation Analysis Tool which combines the automated analysis of disinformation spread with the stance detection module. Conclusions end the paper, discussing directions for future work.

Fake News Detection

The disinformation analysis system we propose to extend in this work is the Storyzy4 Disinformation Analysis tool (DAT), that automatically classifies information sources according to their reliability. In the following, we describe the main building blocks composing such system:

1. Manual annotation of information sources according to their reliability. Information sources are tagged by information experts and fact-checkers according to a restricted set of categories. More precisely, each source is annotated as belonging to one of these three categories: trusted, fake news or satire. For those sources that have been annotated as fake news, at least one more additional subcategory (e.g., hate, conspiracy, propaganda) is added. 2. Numeric representation of information sources and training. Information sources are turned into multidimensional vectors in order to build language models -one model for each information reliability category. Several language models can be used such as bag-of-words or ngrams, though ngrams are not easily scalable due to the size of the models. Different weighting functions are also available, such as tf-idf or frequency. 3. Collection of new sources. New information sources are gathered on a regular basis through diverse heuristics. These will feed the classifier and Storyzy's database. 4. Classification of new sources. New information sources spotted in step 3 are vectorised and classified according to the language models built in step 2. The classification is performed through probability computations which represent the chances of a new information source to belong to an information category. 5. Verification of newly classified information sources. The result of step 4 is analysed by human experts and added to the models from step 2. Thus the pipeline is circular and feeds itself with little human supervision. Human validation of newly classified information sources is based on several external criteria beyond classification probabilities. These criteria are mainly based on the analysis of information sources pointing at or pointed by the considered information source. The combination of multiple criteria aims at reducing the workload of human experts by selecting the most relevant information sources.

It is worth noting that newly constructed language models are evaluated and compared to the previous ones in order to avoid regressions. While model evaluation is automated, the final decision of modifying a current model is left to human supervision. Human validations are always necessary in the delicate field of disinformation detection, but their load is kept to the minimum. A full automation of such a pipeline is not possible yet with satisfying results. This is due to the complexity of the taskeven for humans -and the multiple engineering issues inherent to dynamic information monitoring.

The accuracy of Storyzy classification system is comparable to the human upper bound for this task with a score of 90% on a benchmark of more than 5500 English websites (where 1/4 are fake instances). 5 As the fake news detection system of Storyzy mainly relies on the assessment of the reliability of the news sources, we propose to extend such system with an argument mining module which instead focuses on the content of the news itself, with the aim to provide analysts with the arguments present in such articles and their stance toward the targeted topic, so that they can be supported in their decision to mark a news as being fake or not. oppose a given target topic. Furthermore, an argument may presuppose some domain knowledge, (or the application of commonsense reasoning), but it must be unambiguous in its orientation to the target topic. A target topic, in turn, is some matter of controversy for which there is an obvious polarity to the possible outcomes -that is, a question of being either in favor or against the use or adoption of something, the commitment to some course of action, etc. Thus, given a target topic, it is possible to label a sentence in one of the following three categories:

(Argument-InFavor) A sentence expressing evidence or reasoning that can be used to support a target topic. (Argument-Against) A sentence expressing evidence or reasoning that can be used to oppose a target topic. (Neither) This category includes all other sentences. In other words, it can be a nonsubject-related argument or just a non-argumentative sentence that may or may not be related to the target topic (e.g., a definition).

Following this definition, in this work we address the task of topic-dependent, sentence-level stance detection. We cast it as a three-way classification task: given a sentence and a topic, the algorithm should classify it as either an Argument-InFavor, an Argument-Against or Neither. Given that the use of contextualized word embeddings is the approach offering the best results for this task [START_REF] Reimers | Classification and clustering of arguments with contextualized word embeddings[END_REF], we opted for such method. Among existing approaches, BERT (Bidirectional Encoder Representations from Transformers) [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF] is a Transformer-approach, pre-trained on large corpora and open-sourced.

As input to the network, we concatenate the sentence and the topic (separated by the [SEP]-token), as follows: [CLS] The researchers say the yearly flu shot targets protein "heads" that attack the body and make people feel sick. [SEP] Public health demands vaccinations.

We add a softmax layer to the output of the first token from BERT and fine-tune the entire bert-base-uncased model (BERT base ) and the bert-large-uncased model (BERT large ) with an Adam optimizer for three epochs with a batch size of 16, a maximum sequence length of 128 and a learning rate of 2e-5. To train our module, we use the UKP Sentential Argument Mining Corpus [START_REF] Stab | Crosstopic argument mining from heterogeneous sources[END_REF], containing 400 documents with 25.492 sentences on eight controversial topics (abortion, cloning, death penalty, gun control, marijuana legalisation, minimum wage, nuclear energy and school uniforms), annotated with the same three labels: Argument-InFavor/Argument-Against/Neither.

Given that the purpose of the proposed stance detection module is to be integrated into a disinformation analysis tool, to better evaluate its performances in the targeted context, we have collected and annotated a sample of fake news, and we have annotated them with the stance labels described before. The following section describes the dataset construction.

Test Set Creation

We randomly selected a set of articles identified as containing fake information on a given target topic by the Storyzy fake news detection system (Section 2). In total, we collected 86 articles containing nearly 3000 sentences (after a pre-processing phase aimed at removing very short and useless sentences) and equitably covering three current controversial topics: White Helmets provide essential services, Public health demands vaccinations and The impact of Covid-19 is risible. 6 For the annotation phase, we followed the same annotation scheme and protocol used to annotate the UKP Corpus [START_REF] Stab | Crosstopic argument mining from heterogeneous sources[END_REF]. Our expert annotators were three researchers in the area of stance detection and fake news detection, whose goal was to assign to each sentence one of the three stance labels, i.e., Argument-InFavor, Argument-Against and Neither. We would like to recall that our goal is not to annotate only arguments which contain false information about a given target topic, but rather to annotate all arguments (i.e., those which contain false information or not) related to a given topic in the article. Table 1 provides some examples of argumentative sentences and the assigned stance labels. Interannotator agreement (IAA) among the three annotators calculated on 100 arguments is 0.71 (Fleiss'κ).

Target topic Argument Stance

Public health demands vaccinations

But the reality is that vaccines are loaded with chemicals that destroy immunity, damage the cellular system, and in some cases even result in sterilization.

Argument-Against

Public health demands vaccinations

According to the GreenMedinfo website, the push for the flu vaccine is primarily economic and political rather than based on solid medical evidence.

Argument-Against

White Helmets provide essential services

In Syria we are seeing the unprecedented use of children by the white helmets as propaganda tools to promote a humanitarian war to kill more children.

Argument-Against

The impact of Covid-19 is risible

Covid-19 is not serious because the pandemic can be dramatically slowed, or stopped, with the immediate widespread use of high doses of vitamin C.

Argument-InFavor

Public health demands vaccinations

Vaccines are one of the biggest public health victories in human history.

Argument-InFavor

White Helmets provide essential services

They [White Helmets] are working very hard in a very dangerous situation, doing something few others could do. . .

Argument-InFavor

Public health demands vaccinations

Georgia State is now looking to move their tests of the nanoparticle vaccine on to ferrets, who have a similar respiratory system to humans.

Neither

The impact of Covid-19 is risible PM Sanchez has announced the government will hold meetings via video conference after fellow minister Irene Montero tested positive for the virus.

Neither

Table 1. Examples of argumentative sentences found in fake news. 6 The dataset of fake news annotated with stance labels is available at https://github.com/jeris90/annotationFN.

Table 2 provides statistics on the size and the class distribution of our data set. A first observation is that the proportion of topic-related arguments in our dataset is generally lower than the proportion observed in the UKP corpus. Indeed, our corpus (resp. the UKP corpus) contains 82.6% (resp. 56.3%) of sentences with the label "Neither", 13.1% (resp. 24.3%) for the label "Argument-Against", and 4.3% (resp. 19.4%) for the label "Argument-InFavor". There are several explanations for this difference. First of all, while the data in the UKP corpus generally comes from sources that focus on the debate on a given topic (e.g., https://www.procon.org), the articles containing fake news are rarely completely focused on a single topic, thus increasing the number of sentences with the label "Neither". A second observation is the very low number of arguments in favor of the target topic. Overall, in fake news, arguments in favor of the target topic are often arguments from "certified" articles or web sites which are attacked (and therefore discredited) with the goal of destabilising the society.

Results and Error Analysis

In addition to the two BERT models (i.e., BERT base and BERT large ), we also compared our results with a majority baseline (i.e., the prediction is the most common label in the training dataset). Table 3 reports on the results returned by these three stance detection models on our fake news test set. The fine-tuned BERT base performs 0.44 and 0.04 better in F 1 score than the majority baseline and BERT large , respectively. Best results are obtained for both classes (i.e., Argument-InFavor and Argument-Against) with BERT base . Error Analysis. Table 4 provides the confusion matrix of BERT base . The reasons for some misclassifications are firstly due to the lack of word knowledge that can bias the perception of the polarity of a given argument (argument in favor or against). This is the case, for example, for elements with a positive connotation such as prices/rewards (e.g., the sentence "The Nobel Peace Prize must go to the White Helmets." is labelled Neither while this is an argument in favor of White Helmets), or for elements with a negative connotation such as terrorists (e.g., the sentence "The well-known organization White Helmets once again stepped up its activities in Syria, enlisting, by tradition, the support of one of the largest terrorist groups." is labelled Neither while it is an argument denouncing the relations of White Helmets with terrorists whilst they are supposed to be a humanitarian organisation). A second problem is related to the ambiguity These elements provide insightful information to aid the analyst in her task. For instance, it is possible to identify some recurrent networks of specific information sources allowing the analyst to better understand how some information emerged and began to spread across the internet. This then makes it possible to automatically generate (when possible) the propagation path of an information, a task that has been done manually until now. The interaction between the Disinformation Analysis Tool and the stance detection module is done via an API. The latter requests the following three elements:

1. the plain text of the article; 2. the target topic for which we wish to extract the arguments (this information is available thanks to the set of target topics associated with each article by the DAT); 7 An example may be found on the website https://www.newsguardtech.com/ covid-19-myths/ with some of the most popular fake news about Covid-19.

3. and the entire URL pointing to the website where the text was extracted (optional).

After checking the received information, the text is split into sentences, converted to the BERT format and the stance detection module is run to assign a label ("Neither", "Argument-InFavor" or "Argument-Against") to each of these sentences. The data is then returned to the DAT in JSON format where each item is associated with a sentence. More precisely, each item contains:

the plain text of the sentence; its label; the target topic used by the model; and its source (if available).

These elements are then directly integrated into the Disinformation Analysis Tool on the page containing the information about this article in the form of a table containing the sentences that have been labelled as Argument-InFavor or Argument-Against by our module. We decided not to display the sentences being labelled as Neither, as they are not of help for the analysis. Figure 1 shows a screenshot of the Storyzy Disinformation Analysis Tool empowered with the stance detection module.

The URL of this article is at the top of the figure. The chronological graph of this article is schematised, showing the links (i.e., citations) of this article to other articles and vice versa. The nodes represent the different sources involved. The red rectangles identify the unreliable sources, the green ones the reliable sources and the grey ones (when available) the neutral sources. An arrow between two nodes means that the articles in the top node quote the articles represented in the bottom node. In other words, the articles at the top of the graph are the most recent ones, while those at the bottom are the oldest. This allows us to easily follow the spread of information related to this article and its chronological evolution.

Finally, at the bottom of the page, we find the list of the sentences identified as arguments by our module for the same article. In this case, our module has identified five arguments related to the target topic "Public health demands vaccinations". Of these arguments, seven were labelled "Argument-InFavor" (e.g.,"Dr. Lei Deng and Dr. Baozhong Wang say that a new approach of using double-layered protein nanoparticles to target the influenza virus produced a longer lasting immunity to the flu in mice.") and one was labelled "Argument-Against" (i.e., "The researchers say the yearly flu shot targets protein heads that attack the body and make people feel sick.").

The list of arguments associated with each article returned by our model is ready to be used by fact-checkers and journalists who are interested in articles containing fake information on a specific target topic. The newly proposed tool offers indeed the possibility to, for instance, find "popular" articles, thanks to the chronological graph. This graph supports discovering the article at the origin of a fake news, but also providing a (total or partial) overview of the fake news propagation and thus knowing which article(s) played an important role in the propagation of this information. Having a list of arguments in favor or against the target topic of these articles allows the user to quickly highlight and analyse the kind of argument used by the author of the article to potentially convince the reader.

Conclusions

In this paper, we present an approach to sift the arguments in fake news to boost a disinformation analysis tool, with the final goal of supporting fact-checkers and data analysts in detecting fake news online and in identifying the precise fake information spread through the article. As a side contribution, we have introduced a new annotated resource for stance detection from articles containing fake news.

Several improvements regarding the practical use of this list of arguments are considered for future work. First, establishing statistics related to arguments in fake news. Indeed, the number of fake news can potentially vary from one target topic to another and can be extremely high for highly controversial topics such as adoption, vaccination, etc. The Disinformation Analysis Tool being able to provide the list of articles identified as coming from an unsafe source for a given topic, it would be interesting to make an overall assessment of the arguments extracted from these articles. This can, for example, take the form of a list of the X most commonly used arguments in fake news for a given topic. In order to create such list, it is necessary to be able to compute clusters of arguments according to different criteria, the main one being the degree of similarity between these arguments.

Second, we are currently investigating how the arguments and their stance can improve the automatic detection of articles containing false information. Indeed, we are interested to see whether argument-related criteria such as the number of arguments or the stance of the identified arguments can be used to "facilitate" the automatic detection of fake news. To go even further, it would be interesting to have a graphical representation of the arguments and their interactions within the same article. This would, for example, reveal potential inconsistencies in the article and thus reveal misleading fallacious argumentation.

Third, establishing a counter-argument process to convince those who unintentionally share this false information that some of the arguments put forward are fallacious. Counter-argumentation [START_REF] Mercier | Why do humans reason? arguments for an argumentative theory[END_REF] is a process aiming to put forward counter-arguments in order to provide evidences against an existing argument. In the case of fake news, in order to convince a person that the (fake) information is true, the author of the fake news will use different methods of persuasion via arguments. Thus, identifying these arguments and attacking them by using carefully constructed arguments from safe sources is a way to fight this phenomenon and its spread on social networks. This means that it would be necessary to classify arguments in order to understand which ones are worth for counterargumenting. Some criteria such as verifiability (verifiable vs. unverifiable) [START_REF] Park | Identifying appropriate support for propositions in online user comments[END_REF] or factuality (facts vs. opinions) [START_REF] Yu | Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences[END_REF] can be used for this purpose. Other arguments from sources deemed reliable by the tool can also be used as a counter-argument.

Finally, we plan to address a user study to evaluate the effectiveness of the user interface presenting the arguments and their stance in fake news articles.

Acknowledgements

This work benefited from the support of the project DGA RAPID CONFIRMA.

7

 7

Fig. 1 .

 1 Fig. 1. Screenshot of the Storyzy Disinformation Analysis Tool showing, for the text located in the URL at the top of the figure, its "chronological" graph and a list of arguments in favor and against the target topic of "Public health demands vaccinations" returned by the stance detection module.

Table 2 .

 2 Topics, corpus size and label distribution.

	topic	articles sentences Argument-InFavor Argument-Against Neither
	white helmets	25	998	20	110	868
	vaccination	31	942	84	152	705
	covid-19	30	1008	24	123	861
	total	86	2947	128	385	2434

http://alt.qcri.org/semeval2017/task8/

https://storyzy.com

Stance Detection for Arguments in Fake NewsArgument mining (AM)[START_REF] Lippi | Argumentation mining: State of the art and emerging trends[END_REF][START_REF] Cabrio | Five years of argument mining: a data-driven analysis[END_REF][START_REF] Lawrence | Argument mining: A survey[END_REF] is the research area aiming at extracting natural language arguments and their relations from text. The classic argument mining pipeline is composed of three main steps: first, the argument components are identified in the text; second, the boundaries of such components are defined; third, the intra-argument relations (relations among the evidences and the claims composing the same argument) and the inter-argument relations (relations among different arguments, e.g., support and attack) are predicted.In this context, we focus on the specific task of stance detection which is commonly defined as the "automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: Favor, Against, Neither"[START_REF] Küc | Stance detection: A survey[END_REF]. The main rationale for this choice can be summarised as follows: given that the intent of the producers of disinformation is to destabilise populations from a political, economical and societal point of view, the stance of the fake news arguments with respect to the positions of established authorities is an important indicator to detect them, e.g., "Covid-19 is not serious because the pandemic can be dramatically slowed, or stopped, with the immediate widespread use of high doses of vitamin C."Given our application scenario, i.e., stance detection for arguments from heterogeneous sources (newspaper articles, blogs, exchanges in online debate platforms) containing fake information and on different topics, we decided to adopt the model proposed by Stab et al.[START_REF] Stab | Crosstopic argument mining from heterogeneous sources[END_REF] which is both general and simple. They define an argument as a span of text expressing evidence or reasoning that can be used to either support or[START_REF] Küc | Stance detection: A survey[END_REF] Further details both on the system architecture and on its performance cannot be disclosed due to copyright reasons.

and subjectivity of the arguments. For example, the following argument "Therefore it is logical to assume that the White Helmets are aiding the U.S. government to achieve these aims and have been handsomely bankrolled for their efforts." is erroneously classified as Argument-InFavor by the system, while it is labelled as Argument-Against in our gold standard because the author, with this sentence, denounces the connection of White Helmets with the U.S. Government whilst they are supposed to be an independent organisation.

Predicted label

Argument-Against Argument-InFavor Neither

Integrating Stance Detection in the Disinformation Analysis Tool

Storyzy Disinformation Analysis Tool allows users to investigate disinformation diffusion according to several criteria:

-Topic models are built to identify relevant key-words for a given set of documents, -The authors of these documents are extracted and a network is built to represent their contributions to different information sources, -A chronological graph is built to identify the source of the disinformation and its current stage of diffusion, -Incoming and outcoming links are analyzed and categorized according to their reliability, allowing the system to give scores to information sources according to the nature of these links.