The Imped Model
Detecting Low-Quality Information in Social Media
Bibliographic Data
| ID | 3755566 |
|---|---|
| Authors | M Bastos (0000-0003-0480-1078, University College Dublin, Dublin, Ireland, corresponding author), Shawn Walker (0000-0002-7052-5705, Arizona State University New College of Interdisciplinary Arts and Sciences, Phoenix, AZ, USA), Michael Simeone (0000-0002-3872-7593, Arizona State University, Tempe, AZ, USA) |
| Year | 2021 |
| Volume | 65 |
| Issue | 6 |
| Pages | 863-883 |
| Publication date | 2021-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Behavioral Scientist (JOURNAL) |
| Journal identifiers | ISSN: 0002-7642 • E-ISSN: 1552-3381 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0002764221989776 |
| OpenAlex | W3126000262 |
| Language | EN |
| Citations received | 3 |
| References cited | 40 |
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
Computer security · Data science · Machine learning · Misinformation · Proxy (statistics) · Quality (philosophy) · Social media · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Media Influence and Politics · Misinformation and Its Impacts
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Who says what to whom on twitter
Statistical Modeling
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The measurement of diversity in different types of biological collections
Rethinking Political Communication in a Time of Disrupted Public Spheres
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Digital Journalism And Tabloid Journalism
One Nation, Two Realities
Spreadable Media
Network Propaganda
Gatewatching
Twittering the News
Reuters Institute Digital News Report 2015
Social media gatekeeping
The Brexit Botnet and User-Generated Hyperpartisan News
The disinformation order
Gatekeeping Twitter
Shares, Pins, and Tweets
Misinformation and the Currency of Democratic Citizenship
The Epistemology of Fact Checking
Social Media and Fake News in the 2016 Election
Broadcasters and Hidden Influentials in Online Protest Diffusion
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2024 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |