Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The diffusion of misinformation on social media: Temporal pattern, message, and source
2018258 citationsJieun Shin, Lian Jian et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Kevin Driscoll
Since
Specialization
Citations
This map shows the geographic impact of Kevin Driscoll's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kevin Driscoll with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Driscoll more than expected).
This network shows the impact of papers produced by Kevin Driscoll. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kevin Driscoll. The network helps show where Kevin Driscoll may publish in the future.
Co-authorship network of co-authors of Kevin Driscoll
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Driscoll.
A scholar is included among the top collaborators of Kevin Driscoll based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Kevin Driscoll. Kevin Driscoll is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Gupta, Gopal, et al.. (2021). Formalizing Informal Logic and Natural Language Deductivism.. International Conference on Lightning Protection.1 indexed citations
2.
Shin, Jieun, Lian Jian, Kevin Driscoll, & François Bar. (2018). The Diffusion of Misinformation on Social Media: Temporal Pattern, Message, and Source. SSRN Electronic Journal.2 indexed citations
Driscoll, Kevin, et al.. (2018). Data Network Evaluation Criteria Handbook. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University).3 indexed citations
8.
Shin, Jieun, Lian Jian, Kevin Driscoll, & François Bar. (2016). Political Rumoring on Twitter during the 2012 US Presidential Election: Rumor Diffusion and Correction. SSRN Electronic Journal.1 indexed citations
Driscoll, Kevin & Shawn Walker. (2014). Big Data, Big Questions| Working Within a Black Box: Transparency in the Collection and Production of Big Twitter Data. International journal of communication. 8. 20.65 indexed citations
12.
Driscoll, Kevin & Shawn Walker. (2014). Working within a black box: Transparency in the collection and production of big twitter data. International journal of communication. 8(1). 1745–1764.79 indexed citations
13.
Hall, Brendan & Kevin Driscoll. (2014). Distributed System Design Checklist. NASA Technical Reports Server (NASA). 26. 403–19.2 indexed citations
14.
Driscoll, Kevin, Mike Ananny, François Bar, et al.. (2013). Big Bird, Binders, and Bayonets: Humor and live-tweeting during the 2012 U.S. Presidential Debates. AoIR Selected Papers of Internet Research. 3.4 indexed citations
Driscoll, Kevin, et al.. (2013). Application Agreement and Integration Services. NASA Technical Reports Server (NASA).4 indexed citations
17.
Hall, Brendan, et al.. (2013). Investigating Actuation Force Fight with Asynchronous and Synchronous Redundancy Management Techniques. NASA Technical Reports Server (NASA).1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.