The IMPED model: Detecting low-quality information in social media
By: Bastos, M., Walker, S. and Simeone, M
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Material type: 
BookPublisher: American Behavioral Scientist Description: 65(6), May, 2021: p.863-883.Subject(s): Content moderation, Diversity index, Partisanship, Misinformation, Web archive
In:
American Behavioral ScientistSummary: This article introduces a model for detecting low-quality information we refer to as the Index of Measured-diversity, Partisan-certainty, Ephemerality, and Domain (IMPED). The model purports that low-quality information is characterized by ephemerality, as opposed to quality content that is designed for permanence. The IMPED model leverages linguistic and temporal patterns in the content of social media messages and linked webpages to estimate a parametric survival model and the likelihood the content will be removed from the internet. We review the limitations of current approaches for the detection of problematic content, including misinformation and false news, which are largely based on fact checking and machine learning, and detail the requirements for a successful implementation of the IMPED model. The article concludes with a review of examples taken from the 2018 election cycle and the performance of the model in identifying low-quality information as a proxy for problematic content. – Reproduced
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Articles
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Indian Institute of Public Administration | 65(6), May, 2021: p.863-883 | Available | AR125530 |
This article introduces a model for detecting low-quality information we refer to as the Index of Measured-diversity, Partisan-certainty, Ephemerality, and Domain (IMPED). The model purports that low-quality information is characterized by ephemerality, as opposed to quality content that is designed for permanence. The IMPED model leverages linguistic and temporal patterns in the content of social media messages and linked webpages to estimate a parametric survival model and the likelihood the content will be removed from the internet. We review the limitations of current approaches for the detection of problematic content, including misinformation and false news, which are largely based on fact checking and machine learning, and detail the requirements for a successful implementation of the IMPED model. The article concludes with a review of examples taken from the 2018 election cycle and the performance of the model in identifying low-quality information as a proxy for problematic content. – Reproduced
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