
Guillermo Villar Rodríguez is a Postgraduate Researcher on Data Journalism and Data Science for CIVIC against misinformation and a Ph.D. student. Previously, he gained experience in Data Journalism during his internships at LAB RTVE and EL PAÍS and his collaborations for a branded-content webpage. After his university degrees in Journalism (with the Award Premio Extraordinario) and Audiovisual Communication and his Master (Título Propio) in Data Journalism, he moved to the Netherlands to study an official Master’s degree in Data Science and Society to learn about data-led practices on a computational level for journalism. He was selected as part of Santander-CIDOB 35 under 35 List as a acknowledgement for professionals of 35 years or less in the field of Artificial Intelligence and Algorithmic Governance.
Martín, Alejandro; Huertas-Tato, Javier; Huertas-García, Álvaro; Villar-Rodríguez, Guillermo; Camacho, David
FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference Journal Article
In: arXiv:2110.14532 [cs], 2022, (arXiv: 2110.14532).
@article{martin_facter-check_2022,
title = {FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference},
author = {Alejandro Martín and Javier Huertas-Tato and Álvaro Huertas-García and Guillermo Villar-Rodríguez and David Camacho},
url = {http://arxiv.org/abs/2110.14532},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
journal = {arXiv:2110.14532 [cs]},
abstract = {Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidences about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking that can be used for either applications designed for the general public and by fact-checking organisations. FacTeR-Check enables retrieving fact-checked information, unchecked claims verification and tracking dangerous information over social media. This architectures involves several modules developed to evaluate semantic similarity, to calculate natural language inference and to retrieve information from Online Social Networks. The union of all these components builds a semi-automated fact-checking tool able of verifying new claims, to extract related evidence, and to track the evolution of a hoax on a OSN. While individual modules are validated on related benchmarks (mainly MSTS and SICK), the complete architecture is validated using a new dataset called NLI19-SP that is publicly released with COVID-19 related hoaxes and tweets from Spanish social media. Our results show state-of-the-art performance on the individual benchmarks, as well as producing a useful analysis of the evolution over time of 61 different hoaxes.},
note = {arXiv: 2110.14532},
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pubstate = {published},
tppubtype = {article}
}
Villar-Rodríguez, Guillermo; Souto-Rico, Mónica; Martín, Alejandro
Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter Journal Article
In: Communication & Society, pp. 239–256, 2022.
@article{villar2022virality,
title = {Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter},
author = {Guillermo Villar-Rodríguez and Mónica Souto-Rico and Alejandro Martín},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Communication & Society},
pages = {239--256},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Villar-Rodríguez, Guillermo; Huertas-Tato, Javier; Martín, Alejandro; Camacho, David
A la desinformación le gusta la compañía: Representación de bulos de Twitter sobre la COVID-19 mediante embeddings Conference
XIX Conference of the Spanish Association for Artificial Intelligence, 2021.
@conference{villar2021disinfo,
title = {A la desinformación le gusta la compañía: Representación de bulos de Twitter sobre la COVID-19 mediante embeddings},
author = {Guillermo Villar-Rodríguez and Javier Huertas-Tato and Alejandro Martín and David Camacho},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {XIX Conference of the Spanish Association for Artificial Intelligence},
journal = {XIX Conference of the Spanish Association for Artificial Intelligence (pp. 523-528). 978-84-09-30514-8},
pages = {523-528},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Villar-Rodríguez, Guillermo
Time as Behavior: Binning Dwell Times as Actions between Events for the Prediction of Users’ Intent Masters Thesis
Tilburg University, 2020.
@mastersthesis{rodriguez2020timeb,
title = {Time as Behavior: Binning Dwell Times as Actions between Events for the Prediction of Users’ Intent},
author = {Guillermo Villar-Rodríguez},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
school = {Tilburg University},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}