Bernat Gacias
email: bernat.gacias@laas.fr

Julien Cegarra
email: julien.cegarra@univ-jfc.fr

Pierre Lopez
email: pierre.lopez@laas.fr

An interdisciplinary method for a generic vehicle routing problem decision support system

Keywords: Decision support system, work domain analysis, vehicle routing problem, constraint programming

In this paper, we propose an interdisciplinary method for the logistics of transportation. It focuses on the design of a generic architecture for the vehicle routing problem (VRP). We highlight that human factors and dynamics aspects are generally ignored in the classical approaches to solve the vehicle routing problem. In our study, a link is done between methods of operations research (specific methods to solve vehicle routing problems and constraint programming techniques) and a work domain analysis technique coming from cognitive ergonomics. The proposed architecture allows to consider and to process the constraints identified by the work domain analysis during the problem resolution. It is also well adapted to the consideration of the Human as a main actor in the decision-making process.

Introduction

Nowadays the optimization of the vehicle routing problem (VRP) has become one of the main issues for the companies involved in goods and services production. Indeed, aggressive competition forces them to guarantee a minimum level of quality of service for the customers. In transportation logistics for example, it implies an important effort to satisfy the customer demands at the right time.

This interest from the companies has been transferred to the researchers. For instance, the VRP has been widely studied by the Operations Research scientific community for about 15 years. Therefore, there exists a large number of methods to solve very efficiently the various existing variants of the problem [START_REF] Barnhart | Handbooks in operational research and management science: Transportation[END_REF][START_REF] Golden | The Vehicle Routing Problem: Latest Advances and New Challenges[END_REF][START_REF] Toth | The vehicle routing problem[END_REF]. However we consider that the traditional way to solve the VRP has two important limitations when considering the human factors and the dynamics of the contextual constraints. In order to overcome these limitations, we propose a generic architecture for a decision support system for the vehicle routing problems. An interdisciplinary approach with two components is proposed: [START_REF] Barnhart | Handbooks in operational research and management science: Transportation[END_REF] an ecological interface based on the abstraction hierarchy resulting from a work domain analysis [START_REF] Rasmussen | Cognitive Systems Engineering[END_REF][START_REF] Vicente | Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work[END_REF]; [START_REF] Bazet | La gestion des contraintes : conflit et combinaison de règles. Actes du colloque "Recherche et Ergonomie[END_REF] solving mechanisms based on Operational Research techniques, in particular Constraint Programming [START_REF] Dechter | Constraint Processing[END_REF][START_REF] Rossi | Handbook of Constraint Programming (Foundations of Artificial Intelligence)[END_REF].

Both components are described in next sections. Then, an architecture for the decision support system is proposed. A scenario illustrates how the system works for the design and the modification of the routes. Finally, testing prospects on real problems are referred.

Vehicle routing problems

The vehicle routing problem (VRP) consists in determining the routes of a fleet of vehicles for the transportation of goods or passengers according to some customer demands (delivery, pick-up...) (Figure 1). The changes of vehicles fleets in real environment spark off a large number of constraints to take into account for determining the routes. Operational research methods exist to efficiently solve the various existing variants of the problem [START_REF] Barnhart | Handbooks in operational research and management science: Transportation[END_REF][START_REF] Golden | The Vehicle Routing Problem: Latest Advances and New Challenges[END_REF][START_REF] Toth | The vehicle routing problem[END_REF], for example metaheuristics such as tabu search [START_REF] Bräysy | Tabu search heuristics for the vehicle routing problem with time windows[END_REF][START_REF] Cordeau | A unified tabu search heuristic for vehicle routing problems with time windows[END_REF] or genetic algorithms [START_REF] Berger | A new hybrid genetic algorithm for the capacitated vehicle routing problem[END_REF][START_REF] Prins | A simple and effective evolutionary algorithm for the vehicle routing problem[END_REF]. However we consider that the traditional way to solve the VRP has two important limitations. The first one is that human factors are not much considered in the modeling phase of the problem. Now, humans play a major role by carrying out specific operations and making decisions whenever perturbations occur. For example, when supervising the routes, the user of the resolution methods has enough knowledge and know-how to anticipate emergencies, vehicle breakdowns, traffic jams, driver substitutions, and so on (see [START_REF] Cegarra | A cognitive typology of scheduling situations: A contribution to laboratory and field studies[END_REF] for a discussion on human contributions).

It is important to allow the human be part of the system. We consider that the robustness of the proposed solutions may increase if the human is allowed to act on constraints. It is usually noted that experienced individuals build schedules that are robust enough to disturbances [START_REF] Cegarra | A cognitive typology of scheduling situations: A contribution to laboratory and field studies[END_REF]. For example, the experienced individuals may assign the operations to the least flexible resources with the intention of preserving the most flexible resources for later unexpected disturbances. Another example is the use of more than one hundred different "human" heuristics to tentatively anticipate problems in order to build robust schedules.

The second limitation is that these models are not ready to deal with the rapid changing situations. In the logistics domain the constraints may even change, in some extreme cases, before the end of the modeling phase. The model still has to remain valid recovering these changes.

In the litterature, we find some works with similar objectives [START_REF] Higgins | Human performance in planning and scheduling: fieldwork studies, methodologies and research issues, chapter "Architecture and Interface Aspects of Scheduling Decision Support[END_REF][START_REF] Lassila | Toward the development of mixed-initiative scheduling systems[END_REF]. In [START_REF] Higgins | Human performance in planning and scheduling: fieldwork studies, methodologies and research issues, chapter "Architecture and Interface Aspects of Scheduling Decision Support[END_REF], the author proposes a job scheduling system, based on the work domain analysis, where human factors are highly considered but operational research methods are not exploited. In [START_REF] Lassila | Toward the development of mixed-initiative scheduling systems[END_REF], the authors present a human-machine system based on constraint propagation to solve the job scheduling problem. The authors themselves admit the lack of human consideration in the problem resolution.

Constraint programming

Generalities

To integrate, interactively and incrementally, the constraints into the solving algorithms, we use Constraint programming (CP) techniques [START_REF] Dechter | Constraint Processing[END_REF][START_REF] Rossi | Handbook of Constraint Programming (Foundations of Artificial Intelligence)[END_REF]. The CP paradigm presents some interesting advantages for the problematics at hand. First, it allows us to consider separately the formal definition of the problem (CSP), analysis mechanisms (propagation techniques) and the solving itself (Figure 2). This characteristic is probably the main originality of CP; this is particularly interesting for devising flexible systems and for considering the cooperation inside the Human-Machine system. Besides, with the CP approach we have the advantage to associate a particular processing for each type of constraints following the assertion "1 constraint = 1 algorithm". This principle allows us to decompose the global system of constraints in as many sub-problems as different types of existing constraints, and in this way to specialize the constraint propagation process (filtering the inconsistent values of a variable domain).

Another advantage is that side constraints or preferences can be considered. These constraints are rather particular since they do not define hard restrictions. Provided that these side constraints might overconstrain (that is, make unsatisfiable) the problem under consideration, we can then solve a so-called "maximum Constraint Satisfaction Problem" (Max-CSP in short). In particular the CSP theory offers a formal framework to deal with weighted CSPs (wCSPs), where the objective is to minimize the total weight of unsatisfied side constraints. In the same context, CP allows us to consider techniques based on model inversion in order to determine the constraints to relax in case of unsatisfiable problems [START_REF] Bazet | La gestion des contraintes : conflit et combinaison de règles. Actes du colloque "Recherche et Ergonomie[END_REF]. After this analysis, the user could modify the constraints following the propositions of the model inversion techniques or he might choose to relax other more suitable constraints, using his knowledge of the real problem, in order to obtain a satisfiable problem.

Finally, we can reach a reactive behavior particularly adapted to the system dynamics by incrementally adding new constraints and more particularly studying the formalisms of dynamic CSPs [START_REF] Mittal | Dynamic constraint satisfaction problems[END_REF] and mixed and conditional CSPs [START_REF] Gelle | Solving mixed and conditional constraint satisfaction problems[END_REF].

Specific algorithms for the VRP

An advantage of the proposed approach is the possibility to mix CP mechanisms and other specific solving techniques. In [START_REF] Desrochers | Towards a model and algorithm management system for vehicle routing and scheduling problems[END_REF], the authors propose a methodology based on shortest path determination to associate the solving techniques to the real problem. These associations are made from the similarities between the real problem, once we have the model, and the classical problems from the literature for which the efficient methods and algorithms are well known. This idea might be easily incorporated in the proposed system. After applying CP techniques, we may consider to use other solving techniques which are very efficient to solve problems close to the real problem. The decisions made by these techniques and algorithms may be then returned to the CP solving mechanism.

Work domain analysis

We propose a work domain analysis (WDA) for the VRP. This analysis method facilitates the identification of the problem constraints. It is a first step before the development of a solving system. We believe that the model derived from this kind of analysis can be well adapted to take into account the user and more generally the human factors perspective (see for instance [START_REF] Higgins | Job Shop Scheduling: Hybrid Intelligent Human-Computer Paradigm[END_REF][START_REF] Higgins | Human performance in planning and scheduling: fieldwork studies, methodologies and research issues, chapter "Architecture and Interface Aspects of Scheduling Decision Support[END_REF]). The underlying idea is that if the model contains all constraints having an influence on the way the solution is built, it also considers constraints relating to the Human. On the other hand, if the great majority of constraints are considered in the model, normally it will be adapted to deal with the unexpected and to resist the long-term changes of the situations.

We propose an early decomposition of the domain, analysis and identification of the structural limits of the problem. We use the abstraction hierarchy proposed in [START_REF] Rasmussen | Cognitive Systems Engineering[END_REF][START_REF] Vicente | Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work[END_REF] for this analysis. The abstraction hierarchy ensures an exhaustive decomposition of the work domain necessary to take into account all the restrictions (Figure 3). The abstraction hierarchy levels represent the levels of user reasoning to solve the problem. We decide to keep the 5 levels proposed in [START_REF] Vicente | Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work[END_REF] which are all necessary for the considered problem (see Section 6).

At first level, we find the primary objectives of the work system. For the VRP, these objectives are costs minimization and to keep a satisfying quality of service. At second level, we find the criteria to be used to decide whether the work system is achieving its purposes. These criteria are fleet costs, capacity management, time constraints management (customer time service and time windows) for costs minimization and demands satisfaction, and also time constraints to reach a sufficient quality of service.

As we can see, sometimes the criteria may be contradictory. For example, a delivery delay might lead to a decrease of transportation costs, by using fewer drivers for example. We propose to offer several evaluation criteria in order to pick the best adapted solution (for example, increase customer satisfaction or decrease transportation costs).

The functions required to achieve the purposes are shown at third level. These functions point the constraints to consider in order to achieve the objectives of the system. Finally, on the two remaining levels, we find the physical objects of the vehicle routing problem (vehicles, drivers, merchandises, depots, customers, demands, and routes) and the process defining the capacities and limits of physical objects. For example, the number of drivers may be limited, and this constraint has to be considered for CP (to determine a solution satisfying the constraints) and for the human (who could, for instance, looking for additional drivers in order to relax the constraint).

The decomposition of the problem remains very generic considering studies and problem descriptions found in the litterature. A validation for different real problems is necessary. Whatever, we consider that most of the vehicle routing problems are covered for the proposed work domain analysis and only some particular problems may required other components not represented on it.

Proposed architecture

We consider the WDA as a first step for the design of a VRP decision support system (DSS). A solving system based on optimization techniques is also part of the DSS. The user could interact with the DSS through an Ecological Interface (EI) [START_REF] Vicente | Ecological interface design: Supporting operator adaptation, continuous learning, distributed, collaborative work[END_REF][START_REF] Vicente | Ecological interface design: Progress and challenges[END_REF]. The human could participate in the modeling and also in the problem solving (Figure 4). The Ecological Interface is the interface capable to represent the abstraction hierarchy as an external mental model for the resolution of the problem. The physical and the functional information are displayed for the interface in order to make constraints and complex relationships in the work environment perceptually obvious to the user. This allows more of users' cognitive resources to be devoted to higher cognitive processes such as problem solving and decision making. By reducing mental workload and supporting knowledge-based reasoning, EI aims to improve user performance and overall system reliability for both anticipated and unanticipated events in a complex system. As we can see, in the proposed architecture for the decision support system the tasks are shared between the solving mechanism and the human.

The main task for the user is to define the restrictions of the problem; mainly, he has to select which constraints are activated during the execution on line. In the case of unsatisfiable problems, this selection might be guided by the propositions resulting from the model inversion techniques. He could also express his preferences for the resolution strategy (this feature will also be taken into account for the selection of the resolution algorithms) and he could modify all problem data. Finally, the user picks the solution to be executed.

On the other hand, the tasks assigned to the solving mechanism (solving methods, propagation techniques, initial heuristics) are: to select the algorithms to be useful depending on the context; to propose and to evaluate a set of feasible solutions. In order to help the user to choose the solution, we propose a visual branching scheme of the solutions space using the criteria to judge the performance of the solutions. That way, the user has the possibility to choose a solution taking into account several objectives or preferences that were not announced in the model but can help to increase the efficiency of the solution.

The other feature that supports the user to deal with the unexpected is that the system must offer the possibility to locally modify and re-evaluate a solution once such solution (or a set of solutions) is proposed.

Illustrative example

We illustrate how the proposed system is going to work with a simple scenario of a vehicle routing problem.

We consider a 4-customer problem. Each customer C i,i=1..4 has a demand of merchandise d i,i=1..4 and a service time window to respect T W C i,i=1..4 . A fleet of 3 vehicles V j,j=1..3 is at our disposal with a fixed capacity (Q = 7 units of product) to serve the customers. The routes start and finish at the unique available depot C 0 . 3 drivers D k,k=1..3 , with their time windows T W D k,k=1..3 , are available. The drivers time windows allow us to consider workers shifts and time constraints due to work regulations.

In Table 1, we show the problem data, and customers and drivers time windows. Besides, we consider an allocation vehicle-customer (merchandise) constraint: customer C 4 has to be served by vehicle V 2 . This constraint may correspond for example to the fact that the demanded products are different, and V 2 is the only vehicle adapted to transport the merchandise of C 4 . In order to simplify the example the distances of the route network (depot and customers) are not considered.

As we have explained, the proposed system allows the user to choose the best solving strategy for the real problem. In the example, the user imposes to serve all customers. Table 1 Example data

Once the model is obtained and the user has activated the different constraints, the abstraction hierarchy allows us to act on all features of the scenario (stressed in grey in Figure 5), the solving mechanism starts the constraint propagation. We begin for the time windows constraints, drivers D Now, the user pick the best adapted solution for the problem. In the real vehicle routing problems there exist several criteria to make the choice of the solution. For that reason, we propose a visual branching scheme using the main criteria and the user preferences (number of kilometers, number of vehicles, number of delays...) as a choice of branching. Because, for example, the user could prefer to increase the total time of route but using only two vehicles (with D 1 and D 2 ). Finally, customers {C The solving mechanism propagates the times of route, and we find that C 3 must be served before C 4 by D 1 , and D 2 has to serve {C 1 , C 2 } but we don't know the direction of the route. The solutions are evaluated taking into account the preferences (if there exist) of the user for the visual branching scheme and he chooses the most suitable set of routes for the real problem.

Expected results and conclusions

In this paper, we have proposed a decision support system architecture for the vehicle routing problem. The properties of the system are well adapted to the problem topology. The main contribution of this interdisciplinary approach is to base the initial model of the problem on a work domain analysis

  Fig. 1. Vehicle routing

Fig. 2 .

 2 Fig. 2. Constraint programming

Fig. 3 .

 3 Fig. 3. Work domain analysis of the VRP using an abstraction hierarchy

Fig. 4 .

 4 Fig. 4. Decision support system

Fig. 5 .

 5 Fig. 5. Modeling the problem under study

  1 , C 2 , C 3 } are assigned to D 2 and {C 4 } to D 1 . Now, we consider an unexpected event such as driver D 2 is late. The user analyses the situation and observes that D 2 could have some problems to serve customer C 3 because he has only the first two hours of his shift to serve him. The decision of the user is to allocate customer C 3 to D 1 . That decision is integrated in the solving mechanism. The propagation mechanism starts again, but now only one kind of solution is possible; D 2 serves customers {C 1 , C 2 } and D 1 the customers {C 3 , C 4 }.

  1 , D 2 , and D 3 can serve customers C 1 , C 2 , C 3 but nobody can serve customer C 4 . The model inversion techniques identified that the problem is unsatisfiable because C 4 cannot be served. The user analyses the situation and takes the best solution to solve the conflict. In that case the user has several options, for example to ask to C 4 whether it is possible to serve him inside [0, 8] time interval, or discard to serve C 4 , or to hire a new driver. Among all of the options we suppose that the user decides to split the time window for driver D 1 (T W D 1 = [0, 4] ∪[START_REF] Desrochers | Towards a model and algorithm management system for vehicle routing and scheduling problems[END_REF][START_REF] Higgins | Human performance in planning and scheduling: fieldwork studies, methodologies and research issues, chapter "Architecture and Interface Aspects of Scheduling Decision Support[END_REF]). Then D 1 serves C 4 .From the allocation constraint V 2 -C 4 , we deduce that D 1 is necessarily allocated to V 2 . Now, we also have that C 1 cannot be served by D 1 because of the new time window for the driver. So C 1 has to be assigned to D 2 or D 3 . We are able to discard the solutions where C 1 and C 4 are at the same route, because they have to be served by different drivers.

Finally, we take into account the vehicle capacity (Q = 7). The solutions with C 2 and C 4 at the same route are eliminated. Two sets of solutions are satisfiable: either drivers D 2 and D 3 serve customers {C 1 , C 2 , C 3 } and driver D 1 serves customer {C 4 }, or drivers D 2 and D 3 serve customers {C 1 , C 2 } and driver D 1 customers {C 3 , C 4 }.

which allows the identification of the structural boundaries of the problem to supervise the constraint programming. Besides, the solving mechanisms are compatible with potential user interventions.

We have also carried out a work domain analysis (WDA) for the vehicle routing problem based on an abstraction hierarchy. It has to be the starting point for the ecological interface design suited to the problem. The human-machine interface based on the proposed WDA, the constraint programming approach and the model proposed in [START_REF] Van Wezel | Developing user-oriented scheduling algorithms[END_REF] is being developed. The validation of the interface is planned as future work.

In this context, contacts with a company involved in waste collecting have been established. We are going to propose an interface able to take into account all the problem variables just as three different problem cases: home rubbish collection, industrial waste collection and recyclable product collection. Common properties are shared by the three problem variants but each one needs different solving methods. Once again the proposed decision support system is well adapted for the problem.

Finally, we plan to compare the system with two works with similar objectives [START_REF] Higgins | Human performance in planning and scheduling: fieldwork studies, methodologies and research issues, chapter "Architecture and Interface Aspects of Scheduling Decision Support[END_REF][START_REF] Lassila | Toward the development of mixed-initiative scheduling systems[END_REF]. This theoretical and methodological comparison may point the difficulties that could be found by the researchers when interdisciplinary approaches are setting up.


Diego Vargas

Tommy Heck

Bart Smeets

Paul Van Liedekerke

Herman Ramon

Hans Van Oosterwijck

Discrete element method models of deformable cells in 2D and 3D environments to explore traction generation mechanisms

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Discrete element method models of deformable cells in 2D and 3D environments to explore traction generation mechanisms

Diego A. Vargas 1 , Tommy Heck Mechanics plays a significant role in cellular behavior; cells exert forces on their surroundings and sense mechanical properties of the extracellular matrix (ECM). Subcellular processes such as protrusion formation, cell-ECM adhesion, and actomyosin contraction are mechanically regulated through signaling pathways. To improve our understanding of the role of mechanics in cell behavior, we developed multiscale mechanical models of single cells in 2D and 3D.

Methods

Two separate models were developed that capture a mechanically active cell in either a 2D or 3D environment. The discrete element method is used to describe the actin cortex mechanics (viscoelasticity and bending rigidity) of the cell [1].

The first model describes a three-dimensional cell on a plane (Fig. 1a). Retrograde flow is modeled as actin diffusion along the cell surface; outward forces proportional to this flow are modeled along a protrusive front, behind which force sensitive discrete focal adhesions can form. Stress fibers can connect adhesions, generating the largest traction forces.

The second model describes a 2D representation of a mechanically active cell embedded in a viscoelastic ECM (Fig. 1b). The ECM is modeled by means of smoothed particle hydrodynamics, which allows for large deformations and ECM degradation [2]. Protrusions are modeled by local reduction of actin cortex stiffness and application of small outward forces, followed by adhesion formation. Curvature-dependent cortex contractility then leads to traction generation.

Results

The models comprise three sets of parameters: Cell mechanical properties, ECM mechanical properties, and traction exertion dynamics. By quantifying parameter sensitivity, we described each set's effect on the ability of cells to exert tractions. We specifically vary protrusion, adhesion, actomyosin contraction, ECM stiffness, and ECM degradation. We quantify the effect on traction polarization, morphology, and cellular traction magnitude.

Simulations are compared to experimental results obtained by means of traction force microscopy for cells interacting with a homogeneous matrix, i.e. 2D culture on polyacrylamide and 3D culture in polyethylene glycol. The models generate realistic traction distributions.

Discussion

Experimental measurements at the subcellular scale can be technically difficult to obtain. Our models allow to investigate the role of various cellular processes and ECM mechanical properties and therefore can lead to a better understanding of the role of mechanics in cell behavior.

Fig. 1 :

 1 Fig. 1: (a) Bottom view of simulated cell on a substrate: Arrows indicate traction forces, red areas on cell surface represent focal adhesions, and white lines represent stress fibers. (b) Displacement field for a contractile cell model after protrusion formation in a degradable viscoelastic ECM model.

  1 , Bart Smeets 2 , Paul Van Liedekerke 2,3 , Herman Ramon 2 , and Hans Van Oosterwyck 1,4 1 Mechanical Engineering Department, Biomechanics section, KU Leuven, Belgium 2 Division of Mechatronics, Biostatistics and Sensors (MeBioS), KU Leuven, Belgium 3 Institut National de Recherche en Informatique et en Automatique, France 4 Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Belgium

Acknowledgements

ERC (FP7/2007-2013, n° 308223), FWO-Vlaanderen (n° G.0821.13), and FWO and EU's Horizon 2020 research and innovation programme (n° 665501).


Sacha Altay

Manon Berriche

Alberto Acerbi

Misinformation on Misinformation: Conceptual and Methodological Challenges

Keywords: Misinformation, Misperceptions, Social media, Conspiracy theories, Big data, Audience research

Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research examining the use of big data in social science and reception studies, we identify six misconceptions about misinformation and highlight the conceptual and methodological challenges they raise. The first set of misconceptions concerns the prevalence and circulation of misinformation. First, scientists focus on social media because it is methodologically convenient, but misinformation is not just a social media problem. Second, the internet is not rife with misinformation or news, but with memes and entertaining content.

Third, falsehoods do not spread faster than the truth; how we define (mis)information influences our results and their practical implications. The second set of misconceptions concerns the impact and the reception of misinformation. Fourth, people do not believe everything they see on the internet: sheer volume of engagement should not be conflated with belief. Fifth, people are more likely to be uninformed than misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. Sixth, the influence of misinformation on people's behavior is overblown as misinformation often 'preaches to the choir'. To appropriately understand and fight misinformation, future research needs to address these challenges.

Introduction

Concerns about misinformation are rising the world over. Today, Americans are more concerned about misinformation than sexism, racism, terrorism or climate change [START_REF] Mitchell | Many Americans Say Made-Up News Is a Critical Problem That Needs To Be Fixed[END_REF].

Most of them (60%) think that "made-up news" had a major impact on the 2020 election [START_REF] Auxier | 64% of Americans say social media have a mostly negative effect on the way things are going in the U.S. today[END_REF]. Internet users are more afraid of fake news than online fraud and online bullying (World Risk Poll, 2020). Numerous scientific and journalistic articles claim that online misinformation is the source of many contemporary socio-political issues, while neglecting deeper factors such as the decline of trust in institutions [START_REF] Benkler | Network propaganda: Manipulation, disinformation, and radicalization in American politics[END_REF]Bennett & Livingston, 2020) or in the media [START_REF] Newman | Reuters Institute Digital News Report[END_REF]).

Yet, the scientific literature is clear. Unreliable news, including false, deceptive, low-quality or hyper partisan news, represents a minute portion of people's information diet [START_REF] Allen | Evaluating the fake news problem at the scale of the information ecosystem[END_REF][START_REF] Cordonier | How do the French inform themselves on the Internet? Analysis of online information and disinformation behaviors[END_REF]; most people do not share unreliable news [START_REF] Grinberg | Fake news on twitter during the 2016 US Presidential election[END_REF][START_REF] Guess | Less than you think: Prevalence and predictors of fake news dissemination on Facebook[END_REF]; on average people deem fake news less plausible than true news [START_REF] Acerbi | Research note: Fighting misinformation or fighting for information? Harvard Kennedy School[END_REF]; social media are not the only culprit [START_REF] Benkler | Network propaganda: Manipulation, disinformation, and radicalization in American politics[END_REF]Bennett & Livingston, 2020;[START_REF] Tsfati | Causes and consequences of mainstream media dissemination of fake news: Literature review and synthesis[END_REF]; and the influence of fake news on large socio-political events is overblown (Guess et al., 2020;[START_REF] Mercier | Not Born Yesterday: The Science of Who We Trust and What We Believe[END_REF][START_REF] Nyhan | Facts and myths about misperceptions[END_REF].

Because of this disconnect between public discourse and empirical findings, a growing body of research argues that alarmist narratives about misinformation should be understood as a "moral panic" [START_REF] Anderson | Fake News is Not a Virus: On Platforms and Their Effects[END_REF][START_REF] Carlson | Fake news as an informational moral panic: The symbolic deviancy of social media during the 2016 US presidential election[END_REF][START_REF] Jungherr | Disinformation and the Structural Transformations of the Public Arena: Addressing the Actual Challenges to Democracy[END_REF][START_REF] Mitchelstein | Minimal Effects, Maximum Panic: Social Media and Democracy in Latin America[END_REF].

In particular, these narratives can be characterized as one of the many "technopanics" [START_REF] Marwick | To catch a predator? The MySpace moral panic[END_REF] that have emerged with the rise of digital media. These panics repeat themselves cyclically [START_REF] Orben | The Sisyphean Cycle of Technology Panics[END_REF], and are fueled by a wide range of actors such as the mass media, policymakers, and experts (Cohen, 1972, p. 8). A well-known example is the broadcast of Orson Welles' radio drama The War of the Worlds, in 1938, which was quickly followed by alarmist headlines from the newspaper industry claiming that Americans suffered from mass hysteria. Some years later, psychologist Hadley Cantril gave academic credence to this idea by estimating that a million Americans truly believed in the Martian invasion [START_REF] Cantril | The Invasion from Mars[END_REF]. Yet his claim turned out to be empirically unfounded. As Brad Schwartz (2015, p. 184) explains "With the crude statistical tool of the day, there was simply no way of accurately judging how many people heard War of the Worlds, much less how many of them were frightened by it. But all the evidence-the size of the Mercury's audience, the relatively small number of protest letters, and the lack of reported damage caused by the panic-suggest that few people listened and even fewer believed. Anything else is just guesswork."

The "Myth of the War of the Worlds Panic" illustrates how academic research can fuel, and legitimize, misconceptions on misinformation. Much research has documented the role played by media and political discourse in building an alarmist narrative on misinformation [START_REF] Anderson | Fake News is Not a Virus: On Platforms and Their Effects[END_REF][START_REF] Carlson | Fake news as an informational moral panic: The symbolic deviancy of social media during the 2016 US presidential election[END_REF][START_REF] Jungherr | Disinformation and the Structural Transformations of the Public Arena: Addressing the Actual Challenges to Democracy[END_REF][START_REF] Mitchelstein | Minimal Effects, Maximum Panic: Social Media and Democracy in Latin America[END_REF]. Here, we offer a critical review of academic discourse on misinformation, which exploded after the 2016 US presidential election with the publication of more than 2,000 scientific articles [START_REF] Allen | Evaluating the fake news problem at the scale of the information ecosystem[END_REF]. We explain why overgeneralizations of scientific results and poor considerations of methodological limitations can lead to misconceptions about misinformation in the public sphere. Drawing on research questioning the use of big data in social sciences [START_REF] Boyd | Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon[END_REF][START_REF] Kitchin | Big Data, new epistemologies and paradigm shifts[END_REF][START_REF] Tufekci | Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls[END_REF] and reception studies [START_REF] Livingstone | Audiences in an Age of Datafication: Critical Questions for Media Research[END_REF], we identify six common misconceptions about misinformation (i.e. statements often found in press articles and scientific studies but not supported by empirical findings). These misconceptions can be divided into two groups: one concerning misconceptions about the prevalence and circulation of misinformation, and one concerning misconceptions about the impact and reception of misinformation. Table 1 offers an overview of the misconceptions tackled in this article. Given that definitions of misinformation vary from one study to another, we use the term in its broadest sense, i.e., as an umbrella term encompassing all forms of false or misleading information regardless of the intent behind it.

Table 1. Frequently encountered misconceptions on misinformation in column two, and misinterpretation of research results associated with these misconceptions in column three.

Misconceptions about the prevalence and circulation of misinformation

Misinformation is just a social media problem

The internet drastically reduces the cost of accessing, producing, and sharing information. Social media reduces friction even more: the cost of connecting with physically distant minds is lower than ever. The media landscape is no longer controlled by traditional gatekeepers, and misinformation, just like any other content, is easier to publish. Today's wide access to rich digital traces makes contemporary large-scale issues like misinformation easier to study, which can give

Misconceptions on Misinformation Misinterpretations of research results

Prevalence & Circulation 1. Misinformation is just a social media problem Scientists focus on social media because it is methodologically convenient. Yet, misinformation in legacy media and offline networks is understudied. Social media make visible and quantifiable social phenomena that were previously hidden or difficult to observe.

The internet is rife with misinformation

Big numbers are the rule on the internet. The misinformation problem should be evaluated at the scale of the information ecosystem, e.g. by including news consumption and news avoidance in the equation.

Falsehoods spread faster than the truth

How (mis)information is defined influences the perceived scale of the problem and the solutions to fight it. Misinformation need not be framed in terms of accuracy (true vs. false), it could also be framed in terms of harmfulness or ideological slant.

Impact

& Reception

People believe everything they see on the internet

Prevalence should not be conflated with impact or acceptance. Sharing or liking is not believing. Digital traces do not always mean what we expect them to, and often, to fully understand them, fine-grained analyses are needed.

A large number of people are misinformed

Surveys overestimate people's misperceptions, beliefs in conspiracy theories, and poorly measure rare behaviors. This is due to poor survey practices, but also the instrumental use that some participants make of these surveys (e.g., expressive responding).

Misinformation has a strong influence on people's behavior

People mostly consume misinformation they are predisposed to accept. Acceptance should not be conflated with attitude or behavioral change. If anything, people are stubborn and do not easily change their minds. Influencing people's behaviors is a daunting task.

the impression that misinformation was less prevalent in the past (compared to reliable information). Alarmist headlines about the effect of social media and new technologies are widespread, such as 'How technology disrupted the truth' [START_REF] Viner | How technology disrupted the truth[END_REF] or 'You thought fake news was bad? Deep fakes are where truth goes to die' [START_REF] Schwartz | You thought fake news was bad? Deep fakes are where truth goes to die[END_REF]. Yet, we need to be careful when comparing the big data sets that we have today with the much poorer and smaller data sets of the past. We should also resist the temptation of idealizing the past. There was no golden age in which people only believed and communicated true information [START_REF] Nyhan | Facts and myths about misperceptions[END_REF]. Conspiracy theories spread long before social media [START_REF] Allcott | Social Media and Fake News in the 2016 Election[END_REF]. Misinformation, such as rumors, is a universal feature of human societies, not a modern exception [START_REF] Acerbi | Cultural Evolution in the Digital Age[END_REF]. Before the advent of the internet, in the 70s, rumors about "sex thieves" arose from interactions between strangers in markets and spread throughout Africa [START_REF] Bonhomme | Les Voleurs de sexe. Anthropologie d'une rumeur africaine[END_REF]. In 1969, rumors about women being abducted and sold as sex slaves proliferated in the French city of Orleans [START_REF] Morin | La rumeur d'Orléans[END_REF]. One cannot assume that misinformation is more common today simply because it is more available and measured.

Social scientists focus on social media-Twitter in particular-because it is methodologically convenient [START_REF] Tufekci | Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls[END_REF]. But active social media users are not representative of the general population [START_REF] Mellon | Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users[END_REF], and most U.S. social media users (70%) say they rarely, or never, post about political or social issues (and only 9% say they often do so; [START_REF] Mcclain | 70% of U.S. social media users never or rarely post or share about political, social issues[END_REF].

Traditional media may play an important role in spreading misinformation, but because of the current focus on social media, little is known about misinformation in widely used sources of information such as television or radio. According to Google Scholar, in May 2022, only seven papers had both 'fake news' and 'television' in their title, compared to 578 with 'fake news' and 'social media' (the results are similar with misinformation, 6 vs 389). Television is a gateway to misinformation from elites, most notably from politicians [START_REF] Benkler | Network propaganda: Manipulation, disinformation, and radicalization in American politics[END_REF][START_REF] Tsfati | Causes and consequences of mainstream media dissemination of fake news: Literature review and synthesis[END_REF].

For instance, Trump's tweets in his final year of office were on cable news channels for a total of 32 hours [START_REF] Wardle | Broadcast news' role in amplifying Trump's tweets about election integrity[END_REF]. More broadly, the detrimental consequences of mainstream partisan media, where politicians routinely spread misinformation, are well documented (e.g. [START_REF] Broockman | The manifold effects of partisan media on viewers' beliefs and attitudes: A field experiment with Fox News viewers[END_REF]. If political elites or partisan media did not wait for social media to spread misinformation, now they can leverage social media features (e.g., hashtags) to frame topics of discussion in online public debate and to impose their partisan agenda based (sometimes) on outright lies.

These limitations invite more nuanced views on the role of social media in the misinformation problem and suggest that more work is needed on misinformation in legacy media and offline networks.

The internet is rife with misinformation

During the 2016 US presidential campaign, the top 20 fake news stories on Facebook accumulated nearly nine million shares, reactions, and comments, between August 1 and November 8 [START_REF] Buzzfeed | Here Are 50 Of The Biggest Fake News Hits On Facebook From[END_REF]. But what do nine million interactions mean on Facebook? If the 1.5 billion Facebook users in 2016 had commented, reacted, or shared just one piece of content per week, the nine million engagements with the top fake news stories would represent only 0.042% of all their actions during the period studied [START_REF] Watts | Don't blame the election on fake news. Blame it on the media[END_REF].

According to one estimate, in the US, between 2019 and 2020, traffic to untrustworthy websites increased by 70%, whereas traffic to trustworthy websites increased by 47% [START_REF] Majid | Covid-19 and the rise of misinformation and misunderstanding[END_REF], which led to the following headline: "Covid-19 and the rise of misinformation and misunderstanding" (ibid). Yet, traffic to trustworthy websites is one order of magnitude higher than the traffic on untrustworthy websites. In March 2020, untrustworthy websites received 30 million additional views compared to March 2019, whereas traffic to trustworthy websites increased by two billion (ibid).

These two examples highlight the need to zoom out of the big numbers surrounding misinformation and to put them in perspective with people's information practices. The average US internet user spends less than 10 minutes a day consuming news online [START_REF] Allen | Evaluating the fake news problem at the scale of the information ecosystem[END_REF]) and the average French internet user spends less than five minutes [START_REF] Cordonier | How do the French inform themselves on the Internet? Analysis of online information and disinformation behaviors[END_REF]. News consumption represents only 14% of Americans' media diet and 3% of the time French people spend on the internet (ibid). Where is misinformation in all that? It represents 0.15% of the American media diet and 0.16% of the French (ibid). In addition, misinformation consumption is heavily skewed: 61% of the French participants did not consult any unreliable sources during the 30 days of the study (ibid), and a small minority of people account for most of the misinformation consumed and shared online [START_REF] Grinberg | Fake news on twitter during the 2016 US Presidential election[END_REF].

Just as the internet is not rife with news, the internet is not rife with misinformation. When people want to inform themselves, they primarily access established news websites [START_REF] Fletcher | Measuring the reach of "fake news" and online disinformation in Europe[END_REF]Guess et al., 2020), or, more commonly, turn on the TV [START_REF] Allen | Evaluating the fake news problem at the scale of the information ecosystem[END_REF]. Misinformation receives little online attention compared to reliable news, and, in turn, reliable news receives little online attention compared to everything else that people do [START_REF] Cordonier | How do the French inform themselves on the Internet? Analysis of online information and disinformation behaviors[END_REF]. People do not use the internet primarily to be informed, but rather to connect with friends, shop, work, and more generally for entertainment (ibid). Documenting the large volume of interactions generated by misinformation is important, but it should not be taken as proof of its predominance. Big numbers are the rule on the internet, and to be properly understood, they must be contextualized within the entire information ecosystem.

That being said, it is not always possible for researchers to consider the big picture since access to social media data is restricted and often partial. As a result, many research questions remain unexplored and conclusions are sometimes uncertain, even when private companies provide access to huge amounts of data. For instance, despite its unprecedented size, the URLs dataset released by Facebook, via Social Science One's program, only includes URLs that have been publicly shared at least 100 times. This threshold of 100 public shares overestimates by a factor of four the prevalence of misinformation on Facebook [START_REF] Allen | Research note: Examining potential bias in large-scale censored data[END_REF]. Despite our limited access to social media data, methodological improvements are possible. For example, even with partial data access, it is possible to conduct cross-platform studies, which are needed to obtain accurate estimates of misinformation prevalence and to quantify the diversity of people's news diets [START_REF] Cointet | Uncovering the structure of the French media ecosystem[END_REF].

Falsehoods spread faster than the truth.

The idea that falsehoods spread faster than the truth was amplified and legitimized by an influential article published in Science which found, among others, that "it took the truth about six times as long as falsehood to reach 1,500 people" (Vosoughi et al., 2018, p.3). The study was quickly covered in prominent news outlets and led to alarmist headlines such as 'Want something to go viral? Make it fake news' [START_REF] Fox | Fake News: Lies spread faster on social media than truth does[END_REF]). Yet, Vosoughi and his colleagues did not examine the spread of true and false news online but of "contested news" that fact-checkers classified as either true or false, leaving aside a large number of uncontested news that is extremely viral. For instance, the royal wedding between Prince Harry and Meghan Markle attracted 29 million viewers in the U.S. and generated at least 6.9 million interactions on Facebook [START_REF] Berger | Here's how many Americans watched Prince Harry and Meghan Markle's royal wedding[END_REF]. Similarly, when

Lionel Messi posted for the first time a picture of him with a Paris Saint Germain Jersey on

Instagram, the post generated 22 million likes. The authors themselves publicly explained this sampling bias1 . Yet, journalists and scientists quickly overgeneralized, and nuances were lost.

Subsequent studies found conflicting results, with science-based evidence and fact-checking being more retweeted than false information during the COVID-19 pandemic [START_REF] Pulido | COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information[END_REF] and news from hyperpartisan sites not disseminating more quickly than mainstream news on Australian

Twitter [START_REF] Bruns | News diffusion on Twitter: Comparing the dissemination careers for mainstream and marginal news[END_REF]. Moreover, a cross-platform study found no difference in the spreading pattern of news from reliable and questionable sources [START_REF] Cinelli | The COVID-19 social media infodemic[END_REF]. This is not to say that the Vosoughi et al. findings on fact-checked news do not hold, but rather that how information and misinformation are defined greatly influences the conclusions we draw.

Misinterpretations of that article raise the questions: What categories should we use? Do dichotomous measures of veracity make sense? How should we define misinformation? These past few years, important efforts have been made to refine the blurred concept of "fake news" (for a review see Tandoc et al., 2018). In practice, though, researchers use the ready-made categories constructed by fact-checkers, putting under the same heading content that differs in terms of harmfulness or facticity.

How (mis)information is defined influences the perceived scale of the problem and the solutions to fight it [START_REF] Rogers | The scale of Facebook's problem depends upon how'fake news' is classified[END_REF]. For instance, in 2016, a widely circulated BuzzFeed article showed that fake news outperformed true news on Facebook in terms of engagement before the U.S.

presidential election [START_REF] Silverman | This analysis shows how viral fake election news stories outperformed real news on Facebook[END_REF]. However, when excluding hyperpartisan news (such as Fox News) from the fake news category, reliable news largely outperformed fake news [START_REF] Rogers | The scale of Facebook's problem depends upon how'fake news' is classified[END_REF]. Politically biased information that is not false could have harmful effects [START_REF] Longhi | Mapping information and identifying disinformation based on digital humanities methods: From accuracy to plasticity[END_REF], but does it belong in the misinformation category? As Rogers (2021) explains, "stricter definitions of misinformation (imposter sites, pseudo-science, conspiracy, extremism only) lessen the scale of the problem, while roomier ones (adding "hyperpartisan" and "junk" sites serving clickbait) increase it, albeit rarely to the point where it outperforms non-problematic (or more colloquially, mainstream) media" (p. 2). Of course, it would be impossible to find a unanimous typology that would put every piece of news in the "right" category, but it is important to be mindful of the limitations of the categories we use.

In the first part of this article, we have highlighted three misconceptions about the prevalence and circulation of misinformation. We encourage future research to broaden the scope of misinformation studies by (i) avoiding technological determinism and re-inscribing misinformation in a historical perspective, (ii) putting misinformation consumption into perspective with broader people's information practices, and (iii) studying subtler forms of information disorders and moving away from blatantly false information.

Misconceptions about the impact and reception of misinformation

In the second part of this article, we examine misconceptions about the impact and reception of misinformation. It is not because the numbers generated by misinformation are small compared to reliable information or non-news content that we can dismiss them. Small numbers can make a difference at a societal level, but to accurately assess their impact and how they translate into attitudes and behaviors, we need to understand what they mean.

People believe everything they see on the internet.

Media effects are increasingly measured via big data methods (Athique, 2018, p. 59) neglecting the agency of people and paying little attention to communicative context [START_REF] Livingstone | Audiences in an Age of Datafication: Critical Questions for Media Research[END_REF]. As danah boyd and Kate Crawford (2012, p. 666) put it, "why people do things, write things, or make things can be lost in the sheer volume of numbers". For instance, it is tempting to conflate prevalence with impact, but the diffusion of inaccurate information should be distinguished from its reception. People are more likely to share true and false news they perceive as more accurate (e.g. [START_REF] Altay | If this account is true, it is most enormously wonderful": Interestingness-if-true and the sharing of true and false news[END_REF], but there is also a well-documented disconnect between sharing intentions and accuracy judgments (ibid). Sharing and liking are not believing. People interact with misinformation for a variety of reasons: to socialize, to express skepticism, outrage or anger, to signal group membership, or simply to have a good laugh [START_REF] Acerbi | Cognitive attraction and online misinformation[END_REF][START_REF] Berriche | Internet Users Engage More With Phatic Posts Than With Health Misinformation On Facebook[END_REF][START_REF] Metzger | From Dark to Light: The Many Shades of Sharing Misinformation Online[END_REF][START_REF] Osmundsen | Partisan polarization is the primary psychological motivation behind political fake news sharing on Twitter[END_REF]. Engagement metrics are an imperfect proxy of information reception and are not sufficient to estimate the impact of misinformation. As Wagner and Boczkowski (2019, p. 881) note, "studies that measure exposure to fake news seem sometimes to easily infer acceptance of content due to mere consumption of it, therefore perhaps exaggerating potential negative effects […] future studies should methodologically account for critical or ironic news sharing, as well as other forms of negotiation and resignification of false content".

With the datafication of society, scientists' attention shifted from empirical audiences to the digital traces that they leave. But we should not forget the people who left these traces in the digital record, nor neglect the rich theoretical framework produced by decades of reception studies based on in situ observations of people consuming media content [START_REF] Lull | World families watch television[END_REF][START_REF] Morley | The nationwide audience[END_REF]. People are not passive receptacles of information. They are active, interpretative, and they domesticate technologies in complex and unexpected ways [START_REF] Livingstone | Audiences in an Age of Datafication: Critical Questions for Media Research[END_REF]. People are more skeptical than gullible when browsing online [START_REF] Fletcher | Measuring the reach of "fake news" and online disinformation in Europe[END_REF]. Trust in the media is low, but trust in information encountered on social media is even lower (ibid). And people deploy a variety of strategies to detect and counter misinformation, such as checking different sources or turning to fact-checks [START_REF] Wagner | The reception of fake news: The interpretations and practices that shape the consumption of perceived misinformation[END_REF]. People often disqualified as "irrational" or "gullible" in public discourse, such as 'conspiracy theorists' or 'anti-vaxxers', deploy sophisticated verification strategies to "fact-check" the news in their own way [START_REF] Tripodi | Searching for Alternative Facts[END_REF] and produce "objectivist counter-expertise" (Ylä-Anttila, 2018) by doing their "own research" [START_REF] Marwick | Constructing alternative facts: Populist expertise and the QAnon conspiracy[END_REF]. For instance, a recent in-depth analysis of around 15,000 comments, based on mixmethods, showed how 'anti-vaxxers' cite scientific studies on Facebook groups to support their positions and challenge the objectivity of mainstream media [START_REF] Berriche | En quête de sources. Preuves et mises à l'épreuve des internautes dans la controverse vaccinale sur Facebook[END_REF].

Given people's skepticism towards information encountered online and the low prevalence of misinformation in their media diet, interventions aimed at reducing the acceptance of misinformation are bound to have smaller effects than interventions increasing trust in reliable sources of information [START_REF] Acerbi | Research note: Fighting misinformation or fighting for information? Harvard Kennedy School[END_REF]. More broadly, enhancing trust in reliable sources should be a priority over fostering distrust in unreliable sources [START_REF] Altay | How Effective Are Interventions Against Misinformation[END_REF].

A large number of people are misinformed.

Headlines about the ubiquity of misbeliefs are rampant in the media and are most often based on surveys. But how well do surveys measure misbeliefs? Luskin and colleagues (2018) analyzed the design of 180 media surveys with closed-ended questions measuring belief in misinformation.

They found that more than 90% of these surveys lacked an explicit "Don't know" or "Not sure" option and used formulations encouraging guessing such as "As far as you know …," or "Would you say that …". Often, participants answer these questions by guessing the correct answer, and report holding beliefs that they did not hold before the survey [START_REF] Graham | Measuring Misperceptions?[END_REF]. Not providing, or not encouraging "Don't know" answers is known to increase guessing even more [START_REF] Luskin | Don't know" means "don't know": DK responses and the public's level of political knowledge[END_REF]. Guessing would not be a major issue if it only added noise to the data. To find out, Luskin and colleagues ( 2018) tested the impact of not providing "Don't know" answers and encouraging guessing on the prevalence of misbeliefs. They found that it overestimates the proportion of incorrect answers by nine percentage points (25 to 16), and, when considering only people who report being confident in holding a misperception, it overestimates incorrect answers by 20 percentage points (25 to 5). In short, survey items measuring misinformation overestimate the extent to which people are misinformed, eclipsing the share of those who are simply uninformed.

In the same vein, conspiratorial beliefs are notoriously difficult to measure and surveys tend to exaggerate their prevalence [START_REF] Clifford | An improved question format for measuring conspiracy beliefs[END_REF]. For instance, participants in survey experiments display a preference for positive response options (yes versus no, or agree versus disagree) which inflates agreement with statements, including conspiracy theories, by up to 50% [START_REF] Hill | Acquiescence Bias Inflates Estimates of Conspiratorial Beliefs and Political Misperceptions[END_REF][START_REF] Krosnick | Questionnaire design[END_REF]. Moreover, the absence of "Don't know" options, together with the impossibility to express one's preference for conventional explanations in comparison to conspiratorial explanations, greatly overestimate the prevalence of conspiratorial beliefs [START_REF] Clifford | An improved question format for measuring conspiracy beliefs[END_REF]. These methodological problems contributed to unsupported alarmist narratives about the prevalence of conspiracy theories, such as Qanon going mainstream (Enders et al. 2021).

Moreover, the misperceptions that surveys measure are skewed toward politically controversial and polarizing misperceptions, which are not representative of the misperceptions that people actually hold [START_REF] Nyhan | Facts and myths about misperceptions[END_REF]. This could contribute to fueling affective polarization by emphasizing differences between groups instead of similarities and inflate the prevalence of misbeliefs. When misperceptions become group markers, participants use them to signal group membership-whether they truly believe the misperceptions or not [START_REF] Bullock | Partisan bias in factual beliefs about politics[END_REF].

Responses to factual questions in survey experiments are known to be vulnerable to "partisan cheerleading" (ibid; [START_REF] Prior | You cannot be serious: The impact of accuracy incentives on partisan bias in reports of economic perceptions[END_REF], in which, instead of stating their true beliefs, participants give politically congenial responses. Quite famously, a large share of Americans believed that Donald Trump's inauguration in 2017 was more crowded than Barack Obama's in 2009, despite being presented with visual evidence to the contrary. Partisanship does not directly influence people's perceptions: misperceptions about the size of the crowds were largely driven by expressive responding and guessing. Respondents who supported President Trump "intentionally provide misinformation" to reaffirm their partisan identity (Schaffner & Luks, 2018, p. 136). The extent to which expressive responding contributes to the overestimation of other political misbeliefs is debated [START_REF] Nyhan | Facts and myths about misperceptions[END_REF], but it is probably significant.

Solutions have been proposed to overcome these flaws and measure misbeliefs more accurately, such as including confidence-in-knowledge measures (Graham, 2018) and considering only participants who firmly and confidently say they believe misinformation items as misinformed [START_REF] Luskin | Misinformation about Misinformation? Of Headlines and Survey Design[END_REF]). Yet, even when people report confidently holding misbeliefs, these misbeliefs are highly unstable across time, much more so than beliefs [START_REF] Graham | Measuring Misperceptions?[END_REF]. For instance, the responses of people saying they are 100 percent certain that climate change is not occurring have the same measurement properties as responses of people saying they are 100 percent certain the continents are not moving or that the sun goes around the Earth (ibid). A participant's response at time T does not predict their answer at time T+1. In other words, flipping a coin would give a similar response pattern.

So far, we have seen that even well-designed surveys overestimate the prevalence of misbeliefs.

A further issue is that surveys unreliably measure exposure to misinformation and the occurrence of rare events such as fake news exposure. People report being exposed to a substantial amount of misinformation and recall having been exposed to particular fake headlines [START_REF] Allcott | Social Media and Fake News in the 2016 Election[END_REF]. To estimate the reliability of these measures, [START_REF] Allcott | Social Media and Fake News in the 2016 Election[END_REF] showed participants the 14 most popular fake news during the American election campaign, together with 14 made-up "placebo fake news". 15% of participants declared having been exposed to one of the 14 "real fake news", but 14% also declared having been exposed to one of the 14 "fake news placebos".

During the pandemic, many people supposedly engaged in extremely dangerous hygiene practices to fight COVID-19 because of misinformation encountered on social media, such as drinking diluted bleach [START_REF] Islam | COVID-19-related infodemic and its impact on public health: A global social media analysis[END_REF]. This led to headlines such as "COVID-19 disinformation killed thousands of people, according to a study" (Paris Match Belgique, 2020). Yet, the study is silent regarding causality, and cannot be taken as evidence that misinformation had a causal impact on people's behavior (France info, 2020). For instance, 39% of Americans reported having engaged in at least one cleaning practice not recommended by the CDC, 4% of Americans reported drinking or gargling a household disinfectant, while another 4% reported drinking or gargling diluted bleach [START_REF] Gharpure | Knowledge and practices regarding safe household cleaning and disinfection for COVID-19 prevention-United States[END_REF]. These percentages should not be taken at face value. A replication of the survey found that these worrying responses are entirely attributable to problematic respondents who also reported 'recently having had a fatal heart attack' or 'eating concrete for its iron content' at a rate similar to that of ingesting household cleaners [START_REF] Litman | Did people really drink bleach to prevent COVID-19? A tale of problematic respondents and a guide for measuring rare events in survey data[END_REF]; reminiscent of the "lizardman's constant" by [START_REF] Alexander | LIZARDMAN'S CONSTANT IS 4%[END_REF]. The authors conclude that "Once inattentive, mischievous, and careless respondents are taken out of the analytic sample we find no evidence that people ingest cleansers to prevent Covid-19 infection" (ibid, p.1). This is not to say that COVID-19 misinformation had no harmful effects (such as creating confusion or eroding trust in reliable information), but rather that surveys using self-reported measures of rare and dangerous behaviors should be interpreted with caution.

Misinformation has a strong influence on people's behavior

Sometimes, people believe what they see on the internet and engagement metrics do translate into belief. Yet, even when misinformation is believed, it does not necessarily mean that it changed anyone's mind or behavior. First, people largely consume politically congenial misinformation [START_REF] Guess | Less than you think: Prevalence and predictors of fake news dissemination on Facebook[END_REF][START_REF] Guess | Cracking Open the News Feed: Exploring What US Facebook Users See and Share with Large-Scale Platform Data[END_REF]. That is, they consume misinformation they already agree with, or are predisposed to accept. Congenial misinformation 'preaches to the choir' and is unlikely to have drastic effects beyond reinforcing previously held beliefs. Second, even when misinformation changes people's minds and leads to the formation of new (mis)beliefs, it is not clear if these (mis)beliefs ever translate into behaviors. Attitudes are only weak predictors of behaviors. This problem is well known in public policies as the value-action gap [START_REF] Kollmuss | Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior[END_REF].

Most notoriously, people report being increasingly concerned about the environment without adjusting their behaviors accordingly [START_REF] Landry | Learned helplessness moderates the relationship between environmental concern and behavior[END_REF].

Common misbeliefs, such as conspiracy theories, are likely to be cognitively held in such a way that limits their influence on behaviors [START_REF] Mercier | Not Born Yesterday: The Science of Who We Trust and What We Believe[END_REF]Mercier & Altay, 2022). For instance, the behavioral consequences that follow from common misbeliefs are often at odds with what we would expect from people actually believing them. As Jonathan Kay (2011, p. 185) noted, "one of the great ironies of the Truth movement is that its activists typically hold their meetings in large, unsecured locations such as college auditoriums-even as they insist that government agents will stop at nothing to protect their conspiracy for world domination from discovery." Often, these misbeliefs are likely to be post-hoc rationalizations of pre-existing attitudes, such as distrust of institutions.

In the real world, it is difficult to measure how much attitude change misinformation causes, and it is a daunting task to assess its impact on people's behavior. Surveys relying on correlational data tell us little about causation. For example, belief in conspiracy theories is associated with many costly behaviors, such as COVID-19 vaccine refusal [START_REF] Uscinski | Cause and Effect: On the Antecedents and Consequences of Conspiracy Theory Beliefs[END_REF]. Does this mean that vaccine hesitancy is caused by conspiracy theories? No, it could be that both vaccine hesitancy and belief in conspiracy theories are caused by other factors, such as low trust in institutions (Mercier & Altay, 2022;[START_REF] Uscinski | Cause and Effect: On the Antecedents and Consequences of Conspiracy Theory Beliefs[END_REF]. A few ingenious studies allowed some causal inferences to be drawn. For instance, [START_REF] Kim | Identifying the effect of political rumor diffusion using variations in survey timing[END_REF] used a longitudinal survey to capture people's beliefs and behaviors both before and after the diffusion of the "Obama is a Muslim" rumor. They found that after the diffusion of the rumor more people were indeed likely to believe that Obama was a Muslim. Yet, this effect was "driven almost entirely by those predisposed to dislike Obama" (p. 307), and the diffusion of the rumor had no measurable effect on people's intention to vote for Obama. This should not come as a surprise, considering that even political campaigns and political advertising only have weak and indirect effects on voters [START_REF] Kalla | The Minimal Persuasive Effects of Campaign Contact in General Elections: Evidence from 49 Field Experiments[END_REF]. As David [START_REF] Karpf | On Digital Disinformation and Democratic Myths[END_REF] writes "Generating social media interactions is easy; mobilizing activists and persuading voters is hard."

The idea that exposure to misinformation (or information) has a strong and direct influence on people's attitudes and behaviors comes from a misleading analogy of social influence according to which ideas infect human minds like viruses infect human bodies. Americans did not vote for

Trump in 2016 because they were brainwashed. There is no such thing as "brainwashing"

(Carruthers, 2009). Information is not passed from brain to brain like a virus is passed from body to body. When humans communicate, they constantly re-interpret the messages they receive, and modify the ones they send [START_REF] Claidière | How Darwinian is cultural evolution?[END_REF]. The same tweet will create very different mental representations in each brain that reads it, and the public representations people leave behind them, in the form of digital traces, are only an imperfect proxy of their private mental representations.

The virus metaphor, all too popular during the COVID-19 pandemic -think of the "infodemic" epithet -is misleading [START_REF] Simon | Autopsy of a Metaphor: The Origins, Use, and Blind Spots of the 'Infodemic[END_REF]. It is reminiscent of outdated models of communication (e.g. "hypodermic needle model") assuming that audiences were passive and easily swayed by pretty much everything they heard or read [START_REF] Lasswell | The Theory of Political Propaganda[END_REF]. As [START_REF] Anderson | Fake News is Not a Virus: On Platforms and Their Effects[END_REF] notes "we might see the role of Facebook and other social media platforms as returning us to a pre-Katz and Lazarsfeld era, with fears that Facebook is "radicalizing the world" and that Russian bots are injecting disinformation directly in the bloodstream of the polity." These premises are at odds with what we know about human psychology and clash with decades of data from communication studies.

Conclusion

We identified six misconceptions about the prevalence and impact of misinformation and examined the conceptual and methodological challenges they raise. First, social media makes the perfect villain; but before blaming it for the misinformation problem, more work needs to be done on legacy media and offline networks. Second, the misinformation problem should be evaluated at the scale of the information ecosystem, e.g. by including news consumption and news avoidance in the equation. Third, we should be mindful of the categories that we use, like "fake" versus "true" news since they influence our results and their practical implications. Fourth, more qualitative and quantitative reception studies are needed to understand people's informational practices. Digital traces do not always mean what we expect them to, and often, to fully understand them, finegrained analyses are needed. Fifth, the quantity of misinformed people is likely to be overestimated. Surveys measuring misbeliefs should include "Don't know" or "Not sure" options and avoid wording that encourages guessing. Sixth, we should resist monocausal explanations and blaming misinformation for complex socio-economic problems. People mostly consume information they are predisposed to accept; this acceptance should not be conflated with attitude or behavioral change. More broadly, conclusions drawn from engagement metrics, online experiments, or surveys, need to be taken with a grain of salt, as they tend to overestimate the prevalence of misbeliefs, and tell us little about the causal influence of misinformation and its reception. In the lines below, we detail practical avenues for future research to overcome some of these challenges.

We need to move away from fake news and blatantly false information as their prevalence and effects are likely minimal. Instead, we should investigate subtler forms of misinformation that produce biased perceptions of reality. For instance, partisan media use true information in misleading ways by selectively reporting on some issues but not others and framing them in a politically congruent manner [START_REF] Broockman | The manifold effects of partisan media on viewers' beliefs and attitudes: A field experiment with Fox News viewers[END_REF].

We need to better interpret data collected via computational methods or self-report surveys. So far, misinformation studies have been dominated by big data methods or experimental approaches.

This research could benefit from being completed by active audience research [START_REF] Livingstone | Audiences in an Age of Datafication: Critical Questions for Media Research[END_REF].

For instance, instead of focusing on media effects where participants are often implicitly depicted as passive, it would be more fruitful to study the different ways people use the information they consume online. Similarly, digital ethnographies are needed to understand the reception of misinformation in our complex digital ecosystem.

We need to rely on more complex models of influence. Metaphors like "contagion" and "infodemic" provide an overly simplified -and incorrect -view of how influence works [START_REF] Simon | Autopsy of a Metaphor: The Origins, Use, and Blind Spots of the 'Infodemic[END_REF]. People do not change their minds when exposed to "fake news" on social media. Social media does not turn people into conspiracy theorists. Individual predispositions, such as conspiracy mentality, mediate the relationship between social media use and belief in conspiracy theories [START_REF] Uscinski | Cause and Effect: On the Antecedents and Consequences of Conspiracy Theory Beliefs[END_REF]. These mediating variables, that explain for instance why some people but not others visit untrustworthy websites, should be investigated further.

We need to ask better research questions. The misconceptions outlined above (mis)guided some research agenda, by focusing excessively on why people 'fall for fake news' or on how to make people more skeptical. Yet, most people do not fall for fake news and are skeptical of news on social media. This excessive focus on biases, common in psychology, left some pressing questions unanswered. For example, we know very little about the cognitive and social tools that people use to verify information online. Future studies should move from a perspective centered on the identification of isolated failures of individual cognition to an approach attentive to audience critical skills. Our digital environment empowered us with resources such as fact-checking websites, search engines and Wikipedia, making it easier than ever before to verify content encountered online and offline. Yet, research on the uses (and misuses) of these new information search and verification tools is drastically lacking.

The study of misconceptions does not entail a focus on misinformation. People commonly hold false beliefs and engage in detrimental behaviors because they do not trust institutions and choose not to inform themselves. Instead of focusing on the small number of people who consume news from unreliable sources, it would be more fruitful to focus on the large share of people who are overly skeptical of reliable sources and rarely consume any news [START_REF] Allen | Evaluating the fake news problem at the scale of the information ecosystem[END_REF][START_REF] Cordonier | How do the French inform themselves on the Internet? Analysis of online information and disinformation behaviors[END_REF]. Despite legitimate concerns about misinformation, people are more likely to be uninformed than misinformed [START_REF] Li | The Value of Not Knowing: Partisan Cue-Taking and Belief Updating of the Uninformed, the Ambiguous, and the Misinformed[END_REF], even during the COVID-19 pandemic [START_REF] Cushion | Research suggests UK public can spot fake news about COVID-19, but don't realise the UK's death toll is far higher than in many other countries[END_REF].

For instance, see Sinan Aral's tweet: https://twitter.com/sinanaral/status/974276513741844480

To appropriately understand and fight misinformation, it is crucial to have these conceptual and methodological blind spots in mind. Just like misinformation, misinformation on misinformation could have deleterious effects [START_REF] Altay | Why do so few people share fake news? It hurts their reputation[END_REF][START_REF] Jungherr | Disinformation and the Structural Transformations of the Public Arena: Addressing the Actual Challenges to Democracy[END_REF][START_REF] Miró-Llinares | Misinformation about fake news: A systematic critical review of empirical studies on the phenomenon and its status as a 'threat[END_REF][START_REF] Van Duyn | Priming and fake news: The effects of elite discourse on evaluations of news media[END_REF], such as diverting society's attention and resources from deeper socio-economic issues or fueling people's mistrust of the media even more (Altay & Acerbi, 2022). Misinformation is a symptom of deeper socio-political problems rather than a cause. Fighting the symptoms can help, but it should not divert us from the real causes, nor overshadow the need to fight for access to accurate, transparent, and quality information.

Competing interests

The authors declare no competing interests.