Recommender systems may accelerate knowledge discovery in many fields. However, their users may be competitors guarding their ideas before publication or for other reasons. We describe a simulation experiment to assess user privacy against targeted attacks, modeling recommendations based on co‐access data. The analysis uses an unusually long (14 years) set of anonymized historical data on user‐item accesses. We introduce the notions of “visibility” and “discoverability.” We find, based on historical data, that the majority of the actions of arXiv users would be potentially “visible” under targeted attack. However, “discoverability,” which incorporates the difficulty of actually seeing a “visible” effect, is very much lower for nearly all users. We consider the effect of changes to the settings of the recommender algorithm on the visibility and discoverability of user actions and propose mitigation strategies that reduce both measures of risk.

We present the results of running 4 different papers through the automated filtering system used by the open access preprint server “arXiv” to classify papers and implement quality control barriers. The exercise was carried out in order to assess whether these highly sophisticated, state‐of‐the‐art filters can distinguish between papers that are controversial or have gone past their “sell‐by date,” and otherwise normal papers. We conclude that not even the arXiv filters, which are otherwise successful in filtering fringe‐topic papers, can fully acquire “Domain‐Specific Discrimination” and thus distinguish technical papers that are taken seriously by an expert community from those that are not. Finally, we discuss the implications this has for citizen and policy‐maker engagement with the Primary Source Knowledge of a technical domain.

Abstract We continue investigation of the effect of position in announcements of newly received articles, a single day artifact, with citations received over the course of ensuing years. Earlier work focused on the “visibility” effect for positions near the beginnings of announcements, and on the “self‐promotion” effect associated with authors intentionally aiming for these positions, with both found correlated to a later enhanced citation rate. Here we consider a “reverse‐visibility” effect for positions near the ends of announcements, and on a “procrastination” effect associated with submissions made within the 20 minute period just before the daily deadline. For two large subcommunities of theoretical high‐energy physics, we find a clear “reverse‐visibility” effect, in which articles near the ends of the lists receive a boost in both short‐term readership and long‐term citations, almost comparable in size to the “visibility” effect documented earlier. For one of those subcommunities, we find an additional “procrastination” effect, in which last position articles submitted shortly before the deadline have an even higher citation rate than those that land more accidentally in that position. We consider and eliminate geographic effects as responsible for the above, and speculate on other possible causes, including “oblivious” and “nightowl” effects.

Abstract arXiv.org mediates contact with the literature for entire scholarly communities, providing both archival access and daily email and web announcements of new materials. We confirm and extend a surprising correlation between article position in these initial announcements and later citation impact, due primarily to intentional “self‐promotion” by authors. There is, however, also a pure “visibility” effect: the subset of articles accidentally in early positions fared measurably better in the long‐term citation record. Articles in astrophysics (astro‐ph) and two large subcommunities of theoretical high energy physics (hep‐th and hep‐ph) announced in position 1, for example, respectively received median numbers of citations 83%, 50%, and 100% higher than those lower down, while the subsets there accidentally had 44%, 38%, and 71% visibility boosts. We also consider the positional effects on early readership. The median numbers of early full text downloads for astro‐ph, hep‐th, and hep‐ph articles announced in position 1 were 82%, 61%, and 58% higher than for lower positions, respectively, and those there accidentally had medians visibility‐boosted by 53%, 44%, and 46%. Finally, we correlate a variety of readership features with long‐term citations, using machine learning methods, and conclude with some observations on impact metrics and the dangers of recommender mechanisms.

We describe a large-scale application of methods for finding plagiarism in research document collections. The methods are applied to a collection of 284,834 documents collected by arXiv.org over a 14 year period, covering a few different research disciplines. The methodology efficiently detects a variety of problematic author behaviors, and heuristics are developed to reduce the number of false positives. The methods are also efficient enough to implement as a real-time submission screen for a collection many times larger.