Fake News on Twitter in 2016 U.S. Presidential Election: A Quantitative Approach
Bibliographic record
Abstract
The flow of misinformation and disinformation around the 2016 U.S. presidential election put the problem of “fake news” on the agenda all over the world. As a result, news organizations and companies have taken measures to reduce or eliminate the production and dissemination of fake news. Linguistic Inquiry and Word Count (LIWC) software was employed in the current study to examine 1,500 randomly selected tweets that were used to influence the 2016 U.S. presidential election. Results showed fake news are less likely to have analytical thinking. Moreover, both alt-Right troll accounts and alt-Left troll accounts posted fake news on Twitter. Lastly, Cluster analysis revealed that the fake news tweets are more likely to be retweeted and use fewer analytical thinking. APA Citation Padda, K. (2020). Fake news on Twitter in 2016 U.S. presidential election: a quantitative approach. The Journal of Intelligence, Conflict, and Warfare, 3(2), 18-45. https://journals.lib.sfu.ca/index.php/jicw/article/view/2374/1810.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".