Watching TikTok Live Streaming: A Data Collection for Public Life Study in Hutong
Bibliographic record
Abstract
The block policy caused by COVID-19 has made public life researchers cannot conduct their studies. However, in recent years, fewer public life studies have proposed new tools to reduce human job involved in data collection process. Hence, this article has proposed a new way to collect public life studies data: watching TikTok live streaming. This research aims to test the reliability of this new data collection method by comparing the contents of Hutong residents' public lives from field research and TikTok live streaming. The researcher conducted an ethnographic investigation in Hutong. In the field research process, the researcher used observation to collect data and recorded it by taking photos and taking notes. While watching TikTok, the researcher selected four anchors who often conduct live streaming in Hutongs to watch and recorded the data by taking screenshots. Data from two different collection methods was analyzed by thematic analysis separately. Results show that the data obtained by watching TikTok live streaming is more comprehensive than that obtained by field research; besides, using TikTok to collect data can sufficiently reduce the input of human jobs. The researcher thought the social function of TikTok may provide more help for data collection. Hence this study advised future research to test the reliability of conducting interviews using TikTok.
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 | 0.005 | 0.003 |
| 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".