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.
Processing Social Media Messages in Mass Emergency
2015478 citationsMuhammad Imran, Carlos Castillo et al.profile →
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
2019402 citationsAlexandra Olteanu, Carlos Castillo et al.Frontiers in Big Dataprofile →
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 Fernando Díaz'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 Fernando Díaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Díaz more than expected).
This network shows the impact of papers produced by Fernando Díaz. 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 Fernando Díaz. The network helps show where Fernando Díaz may publish in the future.
Co-authorship network of co-authors of Fernando Díaz
This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Díaz.
A scholar is included among the top collaborators of Fernando Díaz 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 Fernando Díaz. Fernando Díaz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Denton, Emily, et al.. (2021). Artsheets for Art Datasets. Neural Information Processing Systems.1 indexed citations
5.
Baym, Nancy K., et al.. (2021). Making Sense of Metrics in the Music Industries. International journal of communication. 15. 24.4 indexed citations
6.
Díaz, Fernando. (2021). On Evaluating Session-Based Recommendation with Implicit Feedback.. Conference on Recommender Systems.1 indexed citations
7.
Olteanu, Alexandra, Carlos Castillo, Fernando Díaz, & Emre Kıcıman. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data. 2. 13–13.402 indexed citations breakdown →
8.
Collins‐Thompson, Kevyn, et al.. (2017). Overview of the TREC 2014 Web Track. Text REtrieval Conference.12 indexed citations
9.
Bird, Sarah, Solon Barocas, Kate Crawford, Fernando Díaz, & Hanna Wallach. (2016). Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI. SSRN Electronic Journal. 4.13 indexed citations
10.
Díaz, Fernando, et al.. (2014). Overview of the NTCIR-11 Cooking Recipe Search Task.. NTCIR.7 indexed citations
11.
Aslam, Javed A., et al.. (2014). TREC 2014 Temporal Summarization Track Overview. Text REtrieval Conference.37 indexed citations
12.
Aslam, Javed A., et al.. (2013). TREC 2013 Temporal Summarization.. Text REtrieval Conference.32 indexed citations
13.
Imran, Muhammad, Shady Elbassuoni, Carlos Castillo, Fernando Díaz, & Patrick Meier. (2013). Extracting Information Nuggets from Disaster- Related Messages in Social Media. International Conference on Information Systems for Crisis Response and Management. 791–801.256 indexed citations
14.
Collins‐Thompson, Kevyn, Craig Macdonald, Paul N. Bennett, Fernando Díaz, & Ellen M. Voorhees. (2013). TREC 2013 Web Track Overview. Text REtrieval Conference.36 indexed citations
15.
Bai, Jing, Fernando Díaz, Yi Chang, Zhaohui Zheng, & Keke Chen. (2010). Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking. Journal of Bioresource Management. 18–26.3 indexed citations
16.
Díaz, Fernando & Donald Metzler. (2007). Pseudo-aligned multilingual corpora. International Joint Conference on Artificial Intelligence. 2727–2732.13 indexed citations
17.
Metzler, Donald, Fernando Díaz, Trevor Strohman, & W. Bruce Croft. (2005). UMass Robust 2005: Using Mixtures of Relevance Models for Query Expansion. Defense Technical Information Center (DTIC).10 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.