Pew Research Center

FOR RELEASE JANUARY 10, 2014

Twitter users give Christie negative marks on bridge scandal

Methodology https://alpha.pewresearch.org/pewresearch-org/short-reads/2014/01/10/twitter-users-give-christie-negative-marks-on-bridge-scandal/ This analysis employed media research methods that combined Pew Research’s content analysis rules with computer coding software developed by Crimson Hexagon. This report is based on examinations of more than 250,000 tweets. Crimson Hexagon is a software platform that identifies statistical patterns in words used in online texts. Researchers enter key terms using […]

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Pew Research Center, January 2014, "Twitter users give Christie negative marks on bridge scandal"

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Twitter users give Christie negative marks on bridge scandal

Methodology https://alpha.pewresearch.org/pewresearch-org/short-reads/2014/01/10/twitter-users-give-christie-negative-marks-on-bridge-scandal/ This analysis employed media research methods that combined Pew Research’s content analysis rules with computer coding software developed by Crimson Hexagon. This report is based on examinations of more than 250,000 tweets. Crimson Hexagon is a software platform that identifies statistical patterns in words used in online texts. Researchers enter key terms using […]

Methodology

https://alpha.pewresearch.org/pewresearch-org/short-reads/2014/01/10/twitter-users-give-christie-negative-marks-on-bridge-scandal/

This analysis employed media research methods that combined Pew Research’s content analysis rules with computer coding software developed by Crimson Hexagon. This report is based on examinations of more than 250,000 tweets.

Crimson Hexagon is a software platform that identifies statistical patterns in words used in online texts. Researchers enter key terms using Boolean search logic so the software can identify relevant material to analyze. Pew Research draws its analysis sample from several million blogs, all public Twitter posts. Then a researcher trains the software to classify documents using examples from those collected posts. Finally, the software classifies the rest of the online content according to the patterns derived during the training.  

The time frame for the analysis was 9:00 am ET on January 8, to 7:00 pm ET on January 9, 2014.

The Boolean search used to identify tweets was: christie OR “New Jersey” OR lee OR bridge OR GW OR “George Washington” OR GWB OR Kelly OR Wildstein OR Wisniewski OR Sokolich OR NJ OR Buono OR bridgegate