Exploring Crowdsourced Content Moderation Through Lens of Reddit during COVID-19
Bibliographic record
Abstract
In 2020, when COVID-19 struck, social media gained even more influence in people’s lives due to increased online activity. This event led to a surge of false information and cyberbullying, making content moderation harder than ever. Given this challenge, exploring opportunities to explore content moderation solutions to reduce hate speech and fake news on social media is vital. In this paper, we examine if existing content moderation systems are enough during global pandemics and, if not, where gaps may lie. Due to its intriguing Decentralized Content Management System (DCMS), we chose Reddit as the key social networking platform for our hypothesis testing. We used 1.8 million Reddit posts from COVID-19-related subreddits from January 2020 to April 2021. Our findings reveal several significant trends regarding the effect of a worldwide event on content moderation methods designed to lessen the prevalence of hazardous content and fake news. In light of these considerations, we provide the results of comprehensive research conducted with particular attention paid to the user-generated material and the DCMS of Reddit.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".