Abstract

This paper 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 paper 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.

Publication Type:	Article
Additional Information:	This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Publisher Keywords:	content moderation; diversity index; partisanship; misinformation; web archive
Subjects:	H Social Sciences > HN Social history and conditions. Social problems. Social reform Z Bibliography. Library Science. Information Resources > ZA Information resources
Departments:	School of Policy & Global Affairs > Sociology & Criminology

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