An emotion and cognitive based analysis of mental health disorders from social media data

[EN] Mental disorders can severely affect quality of life, constitute a major predictive factor of suicide, and are usually underdiagnosed and undertreated. Early detection of signs of mental health problems is particularly important, since unattended, they can be life-threatening. This is why a deep understanding of the complex manifestations of mental disorder development is important. We present a study of mental disorders in social media, from different perspectives. We are interested in understanding whether monitoring language in social media could help with early detection of mental disorders, using computational methods. We developed deep learning models to learn linguistic markers of disorders, at different levels of the language (content, style, emotions), and further try to interpret the behavior of our models for a deeper understanding of mental disorder signs. We complement our prediction models with computational analyses grounded in theories from psychology related to cognitive styles and emotions, in order to understand to what extent it is possible to connect cognitive styles with the communication of emotions over time. The final goal is to distinguish between users diagnosed with a mental disorder and healthy users, in order to assist clinicians in diagnosing patients. We consider three different mental disorders, which we analyze separately and comparatively: depression, anorexia, and self-harm tendencies.The authors thank the EU-FEDER Comunitat Valenciana 2014- 2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Chulvi-Ferriols, MA.; Rosso, P. (2021). An emotion and cognitive based analysis of mental health disorders from social media data. Future Generation Computer Systems. 124:480-494. https://doi.org/10.1016/j.future.2021.05.032S48049412

How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants

[EN] Stereotype is a type of social bias massively present in texts that computational models use. There are stereotypes that present special difficulties because they do not rely on personal attributes. This is the case of stereotypes about immigrants, a social category that is a preferred target of hate speech and discrimination. We propose a new approach to detect stereotypes about immigrants in texts focusing not on the personal attributes assigned to the minority but in the frames, that is, the narrative scenarios, in which the group is placed in public speeches. We have proposed a fine-grained social psychology grounded taxonomy with six categories to capture the different dimensions of the stereotype (positive vs. negative) and annotated a novel StereoImmigrants dataset with sentences that Spanish politicians have stated in the Congress of Deputies. We aggregate these categories in two supracategories: one is Victims that expresses the positive stereotypes about immigrants and the other is Threat that expresses the negative stereotype. We carried out two preliminary experiments: first, to evaluate the automatic detection of stereotypes; and second, to distinguish between the two supracategories of immigrants¿ stereotypes. In these experiments, we employed state-of-the-art transformer models (monolingual and multilingual) and four classical machine learning classifiers. We achieve above 0.83 of accuracy with the BETO model in both experiments, showing that transformers can capture stereotypes about immigrants with a high level of accuracy.The work of the authors from the Universitat Politecnica de Valencia was funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The work of Paolo Rosso was done also in the framework of the research project PROMETEO/2019/121 (DeepPattern) funded by the Generalitat Valenciana.Sánchez-Junquera, J.; Chulvi-Ferriols, MA.; Rosso, P.; Ponzetto, SP. (2021). How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants. Applied Sciences. 11(8):1-27. https://doi.org/10.3390/app11083610S12711

UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet

[EN] This paper presents the contributions of the UPV-Symanto team, a collaboration between Symanto Research and the PRHLT Center, in the eRisk 2021 shared tasks on gambling addiction, self-harm detection and prediction of depression levels. We have used a variety of models and techniques, including Transformers, hierarchical attention networks with multiple linguistic features, a dedicated early alert decision mechanism, and temporal modelling of emotions. We trained the models using additional training data that we collected and annotated thanks to expert psychologists. Our emotions-over-time model obtained the best results for the depression severity task in terms of ACR (and second best according to ADODL). For the self-harm detection task, our Transformer-based model obtained the best absolute result in terms of ERDE5 and we ranked equal first in terms of speed and latency.The authors from Universitat Politècnica de València thank the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana. We would like to thank the two anonymous reviewers who helped us improve this paper.Basile, A.; Chinea-Ríos, M.; Uban, A.; Müller, T.; Rössler, L.; Yenikent, S.; Chulvi-Ferriols, MA.... (2021). UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet. CEUR. 908-927. http://hdl.handle.net/10251/19067090892

Modelos Basados en Enmascaramiento y en BERT para la Identificación de Estereotipos

[EN] Stereotypes about immigrants are a type of social bias increasingly present in the human interaction in social networks and political speeches. This challenging task is being studied by computational linguistics because of the rise of hate messages, offensive language, and discrimination that many people receive. In this work, we propose to identify stereotypes about immigrants using two different explainable approaches: a deep learning model based on Transformers; and a text masking technique that has been recognized by its capabilities to deliver good and human-understandable results. Finally, we show the suitability of the two models for the task and offer some examples of their advantages in terms of explainability[ES] Los estereotipos sobre inmigrantes son un tipo de sesgo social cada vez m¿as presente en la interacci¿on humana en redes sociales y en los discursos pol¿¿ticos. Esta desafiante tarea est¿a siendo estudiada por la ling¿u¿¿stica computacional debido al aumento de los mensajes de odio, el lenguaje ofensivo, y la discriminaci¿on que reciben muchas personas. En este trabajo, nos proponemos identificar estereotipos sobre inmigrantes utilizando dos enfoques diametralmente opuestos prestando atenci¿on a la explicabilidad de los mismos: un modelo de aprendizaje profundo basado en Transformers; y una t¿ecnica de enmascaramiento de texto que ha sido reconocida por su capacidad para ofrecer buenos resultados a la vez que comprensibles para los humanos. Finalmente, mostramos la idoneidad de los dos modelos para la tarea, y ofrecemos algunos ejemplos de sus ventajas en t¿erminos de explicabilidadThe work of the authors from the Universitat Politecnica of Valencia was funded by the Spanish Ministry of Science and Innovation under the research project MISMISFAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). Experiments were carried out on the GPU cluster at PRHLT thanks to the PROMETEO/2019/121 (DeepPattern) research project funded by the Generalitat ValencianaSánchez-Junquera, JJ.; Rosso, P.; Montes Gomez, M.; Chulvi-Ferriols, MA. (2021). Masking and BERT-based Models for Stereotype Identication. Procesamiento del Lenguaje Natural. 67:83-94. https://doi.org/10.26342/2021-67-7S83946