Bibliographic record
Abstract
Social media is the group of Internet-based applications that allow for the creation and exchange of user-generated content. This typically includes blogs (e.g. Twitter), social networking sites (e.g. Facebook), content communities (e.g. YouTube), collaborative projects (e.g. Wikipedia), virtual social worlds (e.g. Second Life), and virtual game worlds (e.g. World of Warcraft) (Kaplan and Haenlein, 2010). It also includes professional networking sites (e.g. LinkedIn). There has been strong growth in the use of social media worldwide during the last decade and current user statistics from social media sites are impressive. During the second quarter of 2012, the monthly number of active users was 995 million on Facebook and 140 million on Twitter. During the same period, the monthly number of unique visitors was 800 million on YouTube and 106 million on LinkedIn. Europeans are fairly active users of social media, which is likely to be a consequence of the high level of Internet penetration in Europe; 61% compared to a world average of 33% in 2011 (Internet World Stats, 2012). Penetration is particularly high in countries in Northern and Western Europe. 26% of all monthly active users on Facebook during the second quarter of 2012 were European even though Europe contributes less than 12% of the world’s population. Early use of social media was by friends or people with similar interests as a means of connecting, communicating and interacting with each other (Correa et al., 2010). However, an increasing number of businesses, including airports, are developing a social media presence. ACI-Europe (2012) investigates airport use of social media. The report found that by 2012, 57% of airports in Europe were actively using social media; an increase from 40% in 2011. Airports use social media as a form of online communications and use it for a range of planning and management purposes. ACI-Europe (2011) provide further examples of how airports are using social media for customer service (as a virtual ‘customer service desk’), informal relationship building (to engage directly with customers), crisis handling (to communicate quickly and directly during times of crisis), corporate communications (as a tool to raise awareness), and commercial purposes (to promote products and services but also the catchment area and potential for demand). Airports also use social media for research and development (surveying customer satisfaction and/or opinions) (Halpern, 2012).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".