Replication Data for: Predicting and Interpolating State-level Polls using Twitter Textual Data
<|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:'Spatially or temporally dense polling remains both difficult and expensive using existing survey methods.  In response, there have been increasing efforts to approximate various survey measures using social media, but most of these approaches remain methodologically flawed. To remedy these flaws, this paper combines 1200 state-level polls during the 2012 presidential campaign with over 100 million state-located political Tweets; models the polls as a function of the Twitter text using a new linear regularization feature-selection method; and shows via out-of-sample testing that when properly modeled, the Twitter-based measures track and to some degree predict opinion polls, and can be extended to unpolled states and potentially sub-state regions and sub-day timescales. An examination of the most predictive textual features reveals the t' . 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" : ["Elections","Machine learning","Public opinion","Social media","Text analysis"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Spatially or temporally dense polling remains both difficult and expensive using existing survey methods.  In response, there have been increasing efforts to approximate various survey measures using social media, but most of these approaches remain methodologically flawed. To remedy these flaws, this paper combines 1200 state-level polls during the 2012 presidential campaign with over 100 million state-located political Tweets; models the polls as a function of the Twitter text using a new linear regularization feature-selection method; and shows via out-of-sample testing that when properly modeled, the Twitter-based measures track and to some degree predict opinion polls, and can be extended to unpolled states and potentially sub-state regions and sub-day timescales. An examination of the most predictive textual features reveals the t' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Spatially or temporally dense polling remains both difficult and expensive using existing survey methods.  In response, there have been increasing efforts to approximate various survey measures using social media, but most of these approaches remain methodologically flawed. To remedy these flaws, this paper combines 1200 state-level polls during the 2012 presidential campaign with over 100 million state-located political Tweets; models the polls as a function of the Twitter text using a new linear regularization feature-selection method; and shows via out-of-sample testing that when properly modeled, the Twitter-based measures track and to some degree predict opinion polls, and can be extended to unpolled states and potentially sub-state regions and sub-day timescales. An examination of the most predictive textual features reveals the t
Elections; Machine learning; Public opinion; Social media; Text analysis