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.
Instagrammatics and digital methods: studying visual social media, from selfies and GIFs to memes and emoji
2016289 citationsTim Highfield, Tama LeaverCommunication Research and Practiceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Tim Highfield'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 Tim Highfield with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Highfield more than expected).
This network shows the impact of papers produced by Tim Highfield. 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 Tim Highfield. The network helps show where Tim Highfield may publish in the future.
Co-authorship network of co-authors of Tim Highfield
This figure shows the co-authorship network connecting the top 25 collaborators of Tim Highfield.
A scholar is included among the top collaborators of Tim Highfield 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 Tim Highfield. Tim Highfield is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Highfield, Tim. (2016). Social Media and Everyday Politics. QUT ePrints (Queensland University of Technology). 24(82).36 indexed citations
4.
Highfield, Tim & Tama Leaver. (2016). Instagrammatics and digital methods: studying visual social media, from selfies and GIFs to memes and emoji. Communication Research and Practice. 2(1). 47–62.289 indexed citations breakdown →
Highfield, Tim & Stefanie Duguay. (2015). "Like a monkey with a miniature cymbal": Cultural practices of repetition in visual social media. QUT ePrints (Queensland University of Technology). 5.2 indexed citations
8.
Highfield, Tim, et al.. (2015). Harbouring dissent: Greek independent and social media and the antifascist movement. QUT ePrints (Queensland University of Technology).4 indexed citations
9.
Highfield, Tim. (2015). Tweeted joke lifespans and appropriated punch lines: Practices around topical humor on social media. SHILAP Revista de lepidopterología.12 indexed citations
10.
Bruns, Axel & Tim Highfield. (2015). Social media in selected Australian federal and state election campaigns, 2010-15. QUT ePrints (Queensland University of Technology).
Enli, Gunn, Anders Olof Larsson, Bente Kalsnes, et al.. (2013). Social Media and Elections: The Use of Twitter in the 2013 Campaigns in Italy, Australia, Germany, and Norway. AoIR Selected Papers of Internet Research. 3.3 indexed citations
14.
Highfield, Tim, Stephen Harrington, & Axel Bruns. (2013). Twitter as a technology for audiencing and fandom : the #Eurovision phenomenon. QUT ePrints (Queensland University of Technology).8 indexed citations
15.
Bruns, Axel, et al.. (2013). #ausvotes Mark Two: Twitter in the 2013 Australian Federal Election. QUT ePrints (Queensland University of Technology).1 indexed citations
Bruns, Axel, et al.. (2008). Discussion paper : network and concept maps for the blogosphere. QUT ePrints (Queensland University of Technology).4 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.