Uncovering information from social media hyperlinks: An investigation of twitter
Bibliographic record
Abstract
Analyzing hyperlink patterns has been a major research topic since the early days of the web. Numerous studies reported uncovering rich information and methodological advances. However, very few studies thus far examined hyperlinks in the rapidly developing sphere of social media. This paper reports a study that helps fill this gap. The study analyzed links originating from tweets to the websites of 3 types of organizations (government, education, and business). Data were collected over an 8‐month period to observe the fluctuation and reliability of the individual data set. Hyperlink data from the general web (not social media sites) were also collected and compared with social media data. The study found that the 2 types of hyperlink data correlated significantly and that analyzing the 2 together can help organizations see their relative strength or weakness in the two platforms. The study also found that both types of inlink data correlated with offline measures of organizations' performance. Twitter data from a relatively short period were fairly reliable in estimating performance measures. The timelier nature of social media data as well as the date/time stamps on tweets make this type of data potentially more valuable than that from the general web.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".