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Abstract

A substantial amount of research has focused on the persistence or availability of Web citations. The present study analyzes Web citation distributions. Web citations are defined as the mentions of the URLs of Web pages (Web resources) as references in academic papers. The present paper primarily focuses on the analysis of the URLs of Web citations and uses three sets of data, namely, Set 1 from the Humanities and Social Science Index in China (CSSCI, 1998-2009), Set 2 from the publications of two international computer science societies, Communications of the ACM and IEEE Computer (1995-1999), and Set 3 from the medical science database, MEDLINE, of the National Library of Medicine (1994-2006). Web citation distributions are investigated based on Web site types, Web page types, URL frequencies, URL depths, URL lengths, and year of article publication. Results show significant differences in the Web citation distributions among the three data sets. However, when the URLs of Web citations with the same hostnames are aggregated, the distributions in the three data sets are consistent with the power law (the Lotka function).

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Abstract

Online trolling has become increasingly prevalent and visible in online communities. Perceptions of and reactions to trolling behaviors varies significantly from one community to another, as trolling behaviors are contextual and vary across platforms and communities. Through an examination of seven trolling scenarios, this article intends to answer the following questions: how do trolling behaviors differ across contexts; how do perceptions of trolling differ from case to case; and what aspects of context of trolling are perceived to be important by the public? Based on focus groups and interview data, we discuss the ways in which community norms and demographics, technological features of platforms, and community boundaries are perceived to impact trolling behaviors. Two major contributions of the study include a codebook to support future analysis of trolling and formal concept analysis surrounding contextual perceptions of trolling.