Social Media for Social Good or Evil: An Introduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the heyday of social media, individuals around the world held high hopes for the democratizing force of social media; however, in light of the recent public outcry of privacy violations, fake news, and Russian troll farms, much of optimism toward social media has waned in favor of skepticism, fear, and outrage. This special issue critically explores the question, “Is social media for good or evil?” While good and evil are both moral terms, the research addresses whether the benefits of using social media in society outweigh the drawbacks. To help conceptualize this topic, we examine some of the benefits (good) and drawbacks (evil) of using social media as discussed in eight papers from the 2017 International Conference on Social Media and Society. This thematic collection reflects a broad range of topics, using diverse methods, from authors around the world and highlights different ways that social media is used for good, or evil, or both. We conclude that the determination of good and evil depends on where you stand, but as researchers, we need to go a step further to understand who it is good for and who it might hurt.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it