Sourcing and Automation of Political News and Information over Social Media in the United States, 2016-2018
Bibliographic record
Abstract
Social media is an important source of news and information in the United States. But during the 2016 US presidential election, social media platforms emerged as a breeding ground for influence campaigns, conspiracy, and alternative media. Anecdotally, the nature of political news and information evolved over time, but political communication researchers have yet to develop a comprehensive, grounded, internally consistent typology of the types of sources shared. Rather than chasing a definition of what is popularly known as “fake news,” we produce a grounded typology of what users actually shared and apply rigorous coding and content analysis to define the phenomenon. To understand what social media users are sharing, we analyzed large volumes of political conversations that took place on Twitter during the 2016 presidential campaign and the 2018 State of the Union address in the United States. We developed the concept of “junk news,” which refers to sources that deliberately publish misleading, deceptive, or incorrect information packaged as real news. First, we found a 1:1 ratio of junk news to professionally produced news and information shared by users during the US election in 2016, a ratio that had improved by the State of the Union address in 2018. Second, we discovered that amplifier accounts drove a consistently higher proportion of political communication during the presidential election but accounted for only marginal quantities of traffic during the State of the Union address. Finally, we found that some of the most important units of analysis for general political theory—parties, the state, and policy experts—generated only a fraction of the political communication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".