Replication Data for: Distorsions of Political Bias in Crowdsourced Misinformation Flagging
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Many people consume news on social media, yet the production ofnews items online has come under crossfire due to the common spreadingof misinformation. Social media platforms police their content in variousways. Primarily they rely on crowdsourced “flags”: users signal to theplatform that a specific news item might be misleading and, if they raiseenough of them, the item will be fact-checked. However, real-world datashow that the most flagged news sources are also the most popular and –supposedly – reliable ones. In this paper, we show this phenomenon canbe explained by the unreasonable assumptions current content policingstrategies make about how the online social media environment is shaped.The most realistic assumption is that confirmation bias will prevent a userfrom flagging a news item if they share the same ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["social media"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Many people consume news on social media, yet the production ofnews items online has come under crossfire due to the common spreadingof misinformation. Social media platforms police their content in variousways. Primarily they rely on crowdsourced “flags”: users signal to theplatform that a specific news item might be misleading and, if they raiseenough of them, the item will be fact-checked. However, real-world datashow that the most flagged news sources are also the most popular and –supposedly – reliable ones. In this paper, we show this phenomenon canbe explained by the unreasonable assumptions current content policingstrategies make about how the online social media environment is shaped.The most realistic assumption is that confirmation bias will prevent a userfrom flagging a news item if they share the same ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Many people consume news on social media, yet the production ofnews items online has come under crossfire due to the common spreadingof misinformation. Social media platforms police their content in variousways. Primarily they rely on crowdsourced “flags”: users signal to theplatform that a specific news item might be misleading and, if they raiseenough of them, the item will be fact-checked. However, real-world datashow that the most flagged news sources are also the most popular and –supposedly – reliable ones. In this paper, we show this phenomenon canbe explained by the unreasonable assumptions current content policingstrategies make about how the online social media environment is shaped.The most realistic assumption is that confirmation bias will prevent a userfrom flagging a news item if they share the same 
social media