A Qualitative Exploration of People's Experiences on Social Media
Bibliographic record
Abstract
Abstract Social media is becoming an inseparable component of our daily lives --- with the promise of providing an avenue for building connections with others worldwide. However, persuasive media coverage suggests that in reality, online social media is falling short of promises to provide a space for meaningful connections and interactions. Given that the landscape of social media is ever-changing, it is important to periodically probe into people's social media experiences to identify the challenges and nuances of how people make connections with others and experience the content that social media provides. To explore people's social media experiences, in this work, we conducted a qualitative exploratory study in which we took a two-pronged approach: (1) we created two small technology probes to elicit people’s thoughts and comments on how alternative features and functionalities could change how they use social media, and (2) conducted one-on-one creativity sessions to encourage our 16 study participants to explore how social media impact their lives and how it might transform in the future. The participants openly and enthusiastically discussed their experiences, connections, and agency on social media. Our findings suggest that the participants want features to increase expressivity, the ability to control content curation, and opportunities to make connections beyond what current social media platforms provide. We discuss the impact of privacy and anonymity in shaping social media experiences as well as the tension among agency, relevance, content curation, and echo chambers.
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 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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".