The Propagation of Misinformation in Social Media
A Cross-platform Analysis
Bibliographic Data
| ID | 23519636 |
|---|---|
| Editors | Richard Rogers (0000-0002-9897-6559) |
| Year | 2023 |
| Publication date | 2023-12-31 |
| Open Access | Yes |
| Type | BOOK |
| Venue | Propagation of Misinformation in Social Media (SOURCE_BOOK) |
| Publisher | Amsterdam University Press (PUBLISHER • NL) |
| DOI | 10.1515/9789048554249 |
| OpenAlex | W4366992323 |
| Open Library | OL49588415M |
| ISBN | 9789048554249 |
| Language | EN |
| Citations received | 4 |
| References cited | 64 |
The ProPagaTion of MiSinforMaTion in Social MediaAs I note in the opening chapter, the social media platforms have introduced "editorial epistemologies" for elections and the pandemic, authoring lists of authoritative sources that appear when one queries core election-related or pandemic-related keywords or making other manual interventions beyond commercial content moderation, the outsourced, low-wage work of removing offensive content.They also have had to assume the role of "accidental authorities," developing rapidly evolving source and information adjudication policy that at the same time invites backlash for heavy-handedness as well as competition from "alt-tech" platforms that moderate with a lighter touch.The extent of the platforms' editing, and particularly where it ends, is on display in each of the studies.Each of the chapters benefits from techniques developed to capture the data necessary to exhibit the current state of the misinformation problem.Some rely on platform-supplied data (Twitter, Instagram), another on a repurposed marketing data dashboard (Facebook) and others on scraping (Google Web Search, Reddit, 4chan, TikTok).There is also a study that uses one platform (4chan) to analyze another (YouTube), given the copious referencing of YouTube videos by "alternative influencers" and later to vernacular newcomers.Many of the studies also classify sources as mainstream or alternative (to varying degrees) as well as evincing a political bent, relying on (and triangulating) external classification schemes developed by journalists and other media analysts.The platform studies were undertaken twice (and on occasion three times), first in the early run-up to the U.S. presidential elections and the pandemic (March, 2020) and again after the elections and deeper into the pandemic (January, 2021 and/or March, 2021).One of the Twitter studies takes advantage of the data spanning the Capitol riots of January 6, 2021 in Washington, DC, allowing for the analysis of how the platform's subsequent purge of accounts had an impact on the quality of the sources encountered.The studies have been written up in the format of the Harvard Kennedy School Misinformation Review, where earlier versions of the Facebook and 4chan/Reddit chapters were published.It is a format that leads with the research questions and is followed by an essay summary, the implications and the findings.The methods section comes last.To us the format highlights the relevance of the work to journalists and thus also serves well the collaboration with First Draft.
Computer security · Internet privacy · Misinformation · Social media · World Wide Web · Computer Science · Misinformation and Its Impacts · Journalism · Mass media
The Hybrid Media System
Key Dimensions of Alternative News Media
Cross-Country Trends in Affective Polarization
Breaking the filter bubble
The Algorithmic Rise of the “Alt-Right”
Instagrammatics and digital methods
Programmed method
Burst of the Filter Bubble?
Exposure to opposing views on social media can increase political polarization
Kill All Normies
Cultural Marxism and the Cathedral
How Online Content Providers Moderate User‐Generated Content to Prevent Harmful Online Communication
Selling brands while staying “Authentic”
A Third Wave of Selective Exposure Research? The Challenges Posed by Hyperpartisan News on Social Media
Chinese computational propaganda
The ambivalent Internet
Helping populism win? Social media use, filter bubbles, and support for populist presidential candidates in the 2016 US election campaign
The echo chamber is overstated
What they do in the shadows
Dark Participation
Why We Can't Have Our Facts Back
Trump, Parler, and Regulating the Infosphere as Our Commons
A tool for tracking the propagation of words on Reddit
Deplatforming, demotion and folk theories of Big Tech persecution
Tracing normiefication
User unknown
Digital politics after Trump
Pol/Emics
Twitter Issue Response Hashtags as Affordances for Momentary Connectedness
Trends in the diffusion of misinformation on social media
What Is a Good Tomato? A Case of Valuing in Practice
This Is What the News Won’t Show You”
Picturing Algorithmic Surveillance
Writing’ oneself into tragedy
The intersectional internet
An anatomy of a YouTube meme
Internet memes as contested cultural capital
Fake news’ as infrastructural uncanny
Gamergate and The Fappening
Extending the Internet meme
This is why we can’t have nice things
The challenges of studying 4chan and the Alt-Right
They))) rule
Googling Politics
Twitter as arena for the authentic outsider
Deplatforming
Problem Solving in Social Interactions on the Internet
You are fake news
Film criticism, film scholarship and the video essay
History of subversive remix video before YouTube
Together they are Troy and Chase
Exposure to Political Disagreement in Social Media Versus Face-to-Face and Anonymous Online Settings
Like, Post, and Distrust? How Social Media Use Affects Trust in Government
Contestation on Reddit, Gamergate, and movement barriers
Teh Internet Is Serious Business
Corona? 5G? or both
Content moderation, AI, and the question of scale
Conspiracy theories as stigmatized knowledge
Playing Politics
Showing They Care (Or Don't)
Building Social Media Observatories for Monitoring Online Opinion Dynamics
Social Media Bullshit
Histories of Hating
| Unique citing works | 4 |
|---|---|
| Citations per year | 2 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |