Who are the "Heroes of CRISPR"? Public science communication on Wikipedia and the challenge of micro-notability.

Wikipedia's influence in shaping public perceptions of science underscores the significance of scientists being recognized on the platform, as it can impact their careers. Although Wikipedia offers guidelines for determining when a scientist qualifies for their own article, it currently lacks guidance regarding whether a scientist should be acknowledged in articles related to the innovation processes to which they have contributed. To explore how Wikipedia addresses this issue of scientific "micro-notability," we introduce a digital method called Name Edit Analysis, enabling us to quantitatively and qualitatively trace mentions of scientists within Wikipedia's articles. We study two CRISPR-related Wikipedia articles and find dynamic negotiations of micro-notability as well as a surprising tension between Wikipedia's principle of safeguarding against self-promotion and the scholarly norm of "due credit." To reconcile this tension, we propose that Wikipedians and scientists collaborate to establish specific micro-notability guidelines that acknowledge scientific contributions while preventing excessive self-promotion.

A diachronic perspective on citation latency in Wikipedia articles on CRISPR/Cas-9: an exploratory case study.

This paper analyzes Wikipedia's representation of the Nobel Prize winning CRISPR/Cas9 technology, a method for gene editing. We propose and evaluate different heuristics to match publications from several publication corpora against Wikipedia's central article on CRISPR and against the complete Wikipedia revision history in order to retrieve further Wikipedia articles relevant to the topic and to analyze Wikipedia's referencing patterns. We explore to what extent the selection of referenced literature of Wikipedia's central article on CRISPR adheres to scientific standards and inner-scientific perspectives by assessing its overlap with (1) the Web of Science (WoS) database, (2) a WoS-based field-delineated corpus, (3) highly-cited publications within this corpus, and (4) publications referenced by field-specific reviews. We develop a diachronic perspective on citation latency and compare the delays with which publications are cited in relevant Wikipedia articles to the citation dynamics of these publications over time. Our results confirm that a combination of verbatim searches by title, DOI, and PMID is sufficient and cannot be improved significantly by more elaborate search heuristics. We show that Wikipedia references a substantial amount of publications that are recognized by experts and highly cited, but that Wikipedia also cites less visible literature, and, to a certain degree, even not strictly scientific literature. Delays in occurrence on Wikipedia compared to the publication years show (most pronounced in case of the central CRISPR article) a dependence on the dynamics of both the field and the editor's reaction to it in terms of activity.

A large dataset of scientific text reuse in Open-Access publications.

We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains 91 million cases of reused text passages found in 4.2 million unique open-access publications. Cases range from overlap of as few as eight words to near-duplicate publications and include a variety of reuse types, ranging from boilerplate text to verbatim copying to quotations and paraphrases. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. The Webis-STEREO-21 does not indicate if a reuse case is legitimate or not, as its focus is on the general study of text reuse in science, which is legitimate in the vast majority of cases. It allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications.

On Textual Analysis and Machine Learning for Cyberstalking Detection.

Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.

WORDGRAPH: Keyword-in-Context Visualization for NETSPEAK's Wildcard Search.

The WORDGRAPH helps writers in visually choosing phrases while writing a text. It checks for the commonness of phrases and allows for the retrieval of alternatives by means of wildcard queries. To support such queries, we implement a scalable retrieval engine, which returns high-quality results within milliseconds using a probabilistic retrieval strategy. The results are displayed as WORDGRAPH visualization or as a textual list. The graphical interface provides an effective means for interactive exploration of search results using filter techniques, query expansion, and navigation. Our observations indicate that, of three investigated retrieval tasks, the textual interface is sufficient for the phrase verification task, wherein both interfaces support context-sensitive word choice, and the WORDGRAPH best supports the exploration of a phrase's context or the underlying corpus. Our user study confirms these observations and shows that WORDGRAPH is generally the preferred interface over the textual result list for queries containing multiple wildcards.