MISMIS: Desinformación y agresividad en los medios de comunicación social: agregando información y analizando el lenguaje

[EN] The general objectives of the project are to address and monitor misinformation (biased and fake news) and miscommunication (aggressive language and hate speech) in social media, as well as to establish a high quality methodological standard for the whole research community (i) by developing rich annotated datasets, a data repository and online evaluation services; (ii) by proposing suitable evaluation metrics; and (iii) by organizing evaluation campaigns to foster research on the above issues.[ES] Los objetivos generales del proyecto son abordar y monitorizar la desinformación (noticias sesgadas y falsas) y la mala comunicación (lenguaje agresivo y mensajes de odio) en los medios de comunicación social, así como establecer un estándar metodológico de calidad para toda la comunidad investigadora mediante: i) el desarrollo de datasets anotados, un repositorio de datos y servicios de evaluación online; ii) la propuesta de métricas de evaluación adecuadas; y iii) la organización de campañas de evaluación para fomentar la investigación sobre las cuestiones mencionadas.The MISMIS project (PGC2018-096212-B) is funded by the Spanish Ministry of Science, Innovation and Universities.Rosso, P.; Casacuberta Nolla, F.; Gonzalo, J.; Plaza, L.; Carrillo, J.; Amigó, E.; Verdejo, MF.... (2020). MISMIS: Misinformation and Miscommunication in social media: aggregating information and analysing language. Procesamiento del Lenguaje Natural. (65):101-104. https://doi.org/10.26342/2020-65-13S1011046

WordUp! at VaxxStance 2021: Combining Contextual Information with Textual and Dependency-Based Syntactic Features for Stance Detection.

In this paper we describe the participation of the WordUp! team in the VaxxStance shared task at IberLEF 2021. The goal of the competition is to determine the author's stance from tweets written both in Spanish and Basque on the topic of the Antivaxxers movement. Our approach, in the four different tracks proposed, combines the Logistic Regression classifier with diverse groups of features: stylistic, tweet-based, user-based, lexicon-based, dependency-based, and network-based. The outcomes of our experiments are in line with state-of-the-art results on other languages, proving the efficacy of combining methods derived from NLP and Network Science for detecting stance in Spanish and Basque