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# System prepended metadata

title: Untitled

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## Main results (for now)

 1. Volume effect: Democrats tend to produce more content than republicans
 2. Twitter algorithm systematically reduces the amount of cross-cutting content
 3. Effects are heterogeneous accross ideologies: 
   * Democrats: Both ideological cues triggered the algorithm (explicit < implicit < explicit * implicit)
   * Republican: Only implicit cues trigger the algorithm, while explicit cues do not make any difference
 4. Twitter algorithms systematically recommends democrat content
   * Popularity as a confounder (democrats posts are more popular)
   * Shawdow banning caused by claims about electoral frauds
   * Average user is liberals: so without cues, liberals content is prioritized
    
    

## To do 

  1. Update data with last week of experiment
  
  2. Evidence for each finding (graphs + models)
  
      + Replicate results with all different metrics
 
          + Average ideology: mean(abs(prob - .5))
          + Percent crosscutting content : mean(prob > .5) | mean(prob)
          + Ranking crosscutting (vorher das ranking normalisieren)
     
      + Estimate regression models (find example of factorial treatment in the literature)
 
          + Session-level (SE clustered at the bot level)
          + Bot-level 

  3. Investigate why the algorithm is biased towards liberals (popularity ; shadow banning because fraud)

  4. Get final distribution of followers/account
   

## Robutsness tests

  + Compliance Tests: 
  
      + Explicit treatment: final distribution of followers/account
      + Implicit: 
      
          + Number of interactions
          + Ideology of producer of the content interacted with

  + Placebo test: 
      + Reassign treatment randomly and rerun models (results should be non significant)

  + Bootstrap: 
      + User-Jack-Knife: Remove one bot (results should remain stable)
      + Stratified boostrap sessions: Remove 10% (?) of sessions (results should remain stable)

## Framing

  + Normative issues:
      + Users should be able to control the bias of the algorithm by stating explicit cues. 


## Paper structure



