The IMPED Model: Detecting Low-Quality Information in Social Media

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Title: The IMPED Model: Detecting Low-Quality Information in Social Media
Other Titles: The IMPED Model of Information Quality
Authors: Bastos, MarcoWalker, ShawnSimeone, Michael
Permanent link: http://hdl.handle.net/10197/12548
Date: 1-May-2021
Online since: 2021-10-18T12:26:59Z
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.
Funding Details: National Science Foundation
Twitter
Type of material: Journal Article
Publisher: SAGE
Journal: American Behavioral Scientist
Volume: 65
Issue: 6
Start page: 863
End page: 883
Copyright (published version): 2021 Sage
Keywords: Content moderationDiversity indexPartisanshipMisinformationWeb archive
DOI: 10.1177/0002764221989776
Language: en
Status of Item: Peer reviewed
ISSN: 0002-7642
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Information and Communication Studies Research Collection

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