
1. At established platforms, algorithmic ranking and recommendation involve using many signals and are typically not aimed at simply maximizing short-run engagement.2. Quantifying the impacts of algorithmic ranking is quite difficult,even with access to proprietary data. This is not only becauseof the complexity of these technical systems, but due to people’scomplex and often strategic responses to changes in algorithms.3. We lack clear evidence about broader benefits or harms of algorithmic ranking. Nonetheless, simple rankings and recommendations (e.g., chronological, overall popularity) can make some formsof undesirable strategic behavior easier.4. Policy-makers can protect the ability of external researchers toprobe these systems, and they can provide clear paths for platforms to retain and share data in privacy-preserving ways.
Other Social and Behavioral Sciences, Internet Law, Social and Behavioral Sciences, Law
Other Social and Behavioral Sciences, Internet Law, Social and Behavioral Sciences, Law
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
