Gamified inoculation interventions do not improve discrimination between true and fake news: reanalyzing existing research with receiver operating characteristic analysis
Gamified inoculation interventions do not improve discrimination between true and fake news: reanalyzing existing research with receiver operating characteristic analysis
 
  Gamified inoculation interventions designed to improve detection of online misinformation are becoming increasingly prevalent. Two of the most notable interventions of this kind are Bad News and Go Viral!. To assess their efficacy, prior research has typically used pre-post designs in which participants rated the reliability or manipulativeness of true and fake news items before and after playing these games, while most of the time also including a control group who played an irrelevant game (Tetris) or did nothing at all. Mean ratings were then compared between pre-tests and post-tests and/or between the control and experimental conditions. Critically, these prior studies have not separated response bias effects (overall tendency to respond “true” or “fake”) from discrimination (ability to distinguish between true and fake news, commonly dubbed discernment). We reanalyzed the results from five prior studies using receiver operating characteristic (ROC) curves, a method common to signal detection theory (SDT) that allows for discrimination to be measured free from response bias. Across the studies, when comparable true and fake news items were used, Bad News and Go Viral! did not improve discrimination, but rather elicited more “false” responses to all news items (more conservative responding). These novel findings suggest that the current gamified inoculation interventions designed to improve fake news detection are not as effective as previously thought and may even be counterproductive. They also demonstrate the usefulness of ROC analysis, a largely unexploited method in this setting, for assessing the effectiveness of any intervention designed to improve fake news detection.
  online misinformation, gamification, fake news games, receiver operating characteristics, signal detection theory
  
  
  2411–2437
  
    
      Modirrousta-Galian, Ariana
      
        5b7bbe48-7221-47e6-bc12-7c8940eb3247
      
     
  
    
      Higham, Philip A.
      
        4093b28f-7d58-4d18-89d4-021792e418e7
      
     
  
  
   
  
  
    
    
  
    
      2023
    
    
  
  
    
      Modirrousta-Galian, Ariana
      
        5b7bbe48-7221-47e6-bc12-7c8940eb3247
      
     
  
    
      Higham, Philip A.
      
        4093b28f-7d58-4d18-89d4-021792e418e7
      
     
  
       
    
 
  
    
      
  
  
  
  
  
  
    Modirrousta-Galian, Ariana and Higham, Philip A.
  
  
  
  
   
    (2023)
  
  
    
    Gamified inoculation interventions do not improve discrimination between true and fake news: reanalyzing existing research with receiver operating characteristic analysis.
  
  
  
  
    Journal of Experimental Psychology: General, 152 (9), .
  
   (doi:10.31234/osf.io/4bgkd). 
  
  
   
  
  
  
  
  
   
  
    
    
      
        
          Abstract
          Gamified inoculation interventions designed to improve detection of online misinformation are becoming increasingly prevalent. Two of the most notable interventions of this kind are Bad News and Go Viral!. To assess their efficacy, prior research has typically used pre-post designs in which participants rated the reliability or manipulativeness of true and fake news items before and after playing these games, while most of the time also including a control group who played an irrelevant game (Tetris) or did nothing at all. Mean ratings were then compared between pre-tests and post-tests and/or between the control and experimental conditions. Critically, these prior studies have not separated response bias effects (overall tendency to respond “true” or “fake”) from discrimination (ability to distinguish between true and fake news, commonly dubbed discernment). We reanalyzed the results from five prior studies using receiver operating characteristic (ROC) curves, a method common to signal detection theory (SDT) that allows for discrimination to be measured free from response bias. Across the studies, when comparable true and fake news items were used, Bad News and Go Viral! did not improve discrimination, but rather elicited more “false” responses to all news items (more conservative responding). These novel findings suggest that the current gamified inoculation interventions designed to improve fake news detection are not as effective as previously thought and may even be counterproductive. They also demonstrate the usefulness of ROC analysis, a largely unexploited method in this setting, for assessing the effectiveness of any intervention designed to improve fake news detection.
         
      
      
        
          
            
  
    Text
 Accepted_Manuscript_Prepublication_Copy_
     - Accepted Manuscript
   
  
    
      Restricted to Repository staff only
    
  
  
 
          
            
              Request a copy
            
           
            
           
        
          
            
  
    Text
 2023 JEPG Accepted Manuscript
     - Accepted Manuscript
   
  
  
    
  
 
          
            
          
            
           
            
           
        
        
       
    
   
  
  
  More information
  
    
      Accepted/In Press date: 25 January 2023
 
    
      Published date: 2023
 
    
  
  
    
  
    
  
    
  
    
  
    
     
    
  
    
     
        Keywords:
        online misinformation, gamification, fake news games, receiver operating characteristics, signal detection theory
      
    
  
    
  
    
  
  
        Identifiers
        Local EPrints ID: 475360
        URI: http://eprints.soton.ac.uk/id/eprint/475360
        
          
        
        
        
          ISSN: 0096-3445
        
        
          PURE UUID: 38a1feb7-b7a2-4e16-bf39-405e0e3b1f58
        
  
    
        
          
            
              
            
          
        
    
        
          
            
              
            
          
        
    
  
  Catalogue record
  Date deposited: 16 Mar 2023 17:35
  Last modified: 18 Mar 2024 04:03
  Export record
  
  
   Altmetrics
   
   
  
 
 
  
    
    
      Contributors
      
          
          Author:
          
            
              
              
                Ariana Modirrousta-Galian
              
              
                 
              
            
            
          
         
      
        
      
      
      
    
  
   
  
    Download statistics
    
      Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
      
      View more statistics