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.
Sentiment Analysis for Fake News Detection
2021137 citationsMiguel Á. Alonso, David Vilares et al.Electronicsprofile →
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 David Vilares'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 David Vilares with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Vilares more than expected).
This network shows the impact of papers produced by David Vilares. 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 David Vilares. The network helps show where David Vilares may publish in the future.
Co-authorship network of co-authors of David Vilares
This figure shows the co-authorship network connecting the top 25 collaborators of David Vilares.
A scholar is included among the top collaborators of David Vilares 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 David Vilares. David Vilares is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gómez‐Rodríguez, Carlos, et al.. (2017). How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Sentiment Analysis.. arXiv (Cornell University).1 indexed citations
Alonso, Miguel Á. & David Vilares. (2016). A review on political analysis and social media. Procesamiento del lenguaje natural. 56(56). 13–24.11 indexed citations
12.
Vilares, David, Miguel Á. Alonso, & Carlos Gómez‐Rodríguez. (2016). EN-ES-CS: An English-Spanish Code-Switching Twitter Corpus for Multilingual Sentiment Analysis.. Language Resources and Evaluation. 4149–4153.22 indexed citations
Vilares, David, et al.. (2014). LyS at CLEF RepLab 2014: Creating the State of the Art in Author Influence Ranking and Reputation Classification on Twitter. CLEF (Working Notes). 1468–1478.9 indexed citations
17.
Vilares, David, Miguel Á. Alonso, & Carlos Gómez‐Rodríguez. (2013). Clasificación de polaridad en textos con opiniones en español mediante análisis sintáctico de dependencias. Procesamiento del lenguaje natural. 50(50). 13–20.4 indexed citations
18.
Vilares, David, Miguel Á. Alonso, & Carlos Gómez‐Rodríguez. (2013). Una aproximación supervisada para la minería de opiniones sobre tuits en español en base a conocimiento lingüístico. Procesamiento del lenguaje natural. 51(51). 127–134.2 indexed citations
19.
Vilares, Jesús, Miguel Á. Alonso, & David Vilares. (2013). Prototipado rápido de un sistema de normalización de tuits: una aproximación léxica. 39–43.3 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.