This document summarises the project entitled “Web Mining, Search Technologies and Natural Language Processing for the Early Detection of Pathological Gambling”. This is a research project funded by “Dirección General de Ordenación del Juego - Ministerio de Consumo” (Government of Spain). The project started in early 2024 and will run until the end of 2024. The funding for this project was given in the context of a national call of Ministerio de Consumo ( “Convocatoria de subvenciones, durante el ejercicio 2023, para el desarrollo de actividades de investigación relacionadas con la prevención de los trastornos del juego, con los efectos derivados de dichos trastornos o con los riesgos asociados a esta actividad”).
Gambling disorders were incorporated by the World Health Organisation (WHO) into the International
Classification of Diseases ICD-11 (published in 2018, [
Patterns associated with gaming can lead to dysfunction and psychological distress for some players
and, in various countries, this problem has generated significant public health concerns [
Language is a powerful indicator of personality traits, emotions, and provides valuable clues about
mental health and disorders [
This project does not aim to develop automatic diagnostic technology. In fact, we believe that diagnostic tasks performed by medical professionals cannot be carried out by completely automated means. Here, we pursue the more realistic goal of, for example, designing methods that detect the emergence of initial signs of gaming disorders and understand the evolution of a person from the initial stages (e.g., mood changes, lack of sleep) to severe stages (e.g., pressing financial problems or suicidal thoughts). This information would be valuable, for example, for public institutions that could receive alerts about growing risks in specific population segments (e.g., to incite preventive measures). These new monitoring tools could extract and present evidence of the emergence and temporal development of gaming-related disorders that could be exploited by clinical professionals. This would enrich their current sources of evidence (usually focused on direct interaction with patients, surveys, and conventional clinical instruments).
The large volume of interactions and publications available on the Internet and social networks allows for massive analysis of psychological traits related to various disorders. It is common for individuals sufering from psychological disorders, such as those related to gaming, to interact with other individuals, express their concerns, share their experiences, and receive online help from specialised professionals. However, the analysis of online users presents challenges in several areas: filtering and searching for information (to find relevant excerpts from users that are pertinent for analysing a given psychological problem), linguistic text analysis and psycholinguistics, estimation of content quality and reputation (for example, with the purpose of recommending reputable information for people sufering from a certain disorder), and massive data processing (distributed computing methods that are scalable and operate in real-time).
The main scientific hypothesis is that natural language reveals signs of diferent psychological disorders,
particularly addictive disorders related to gambling, and that we can develop early detection technology
based on the analysis of texts published by individuals. The strong relationship between the use of
natural language and diferent psychological conditions has been demonstrated in the past [
It is feasible to have data for a project like this, and we already have experience in extracting and
analysing information online. There are public and freely available contents (open forums, social
networks, etc.) where people openly discuss their problems related to their addiction and tell others that
they have been diagnosed with a gambling addictive disorder or that they are beginning to develop it.
This includes conventional social networks, with open public groups [
The automatic analysis of texts and web pages will focus on public data made freely available online
by users or Internet platforms. The developed algorithms will be evaluated with standardised and
curated collections. We will not work with personal data, and therefore, the usual guidelines on
privacy are not applicable to our project. In any case, we will use appropriate anonymisation strategies
to remove proper names in the texts, user account identifiers, and other elements that could reveal
any information about the subjects. Moreover, the design of the experiments and, in particular, the
construction of the test collections, will carefully follow the recent recommendations on ethical aspects
in the design of natural language analysis experiments [
All activities carried out within the framework of the project will take special care to ensure that
the models and solutions do not incorporate any type of gender bias (or other types of biases). The
creation of datasets and algorithmic design will follow existing guidelines and recommendations [
The specific objectives of this project are: • O1. Develop new methods and resources that generate useful evidence for the monitoring and prevention of compulsive gambling disorder problems.
This project addresses an innovative area where there are few test collections and polished open
data. Additionally, new metrics for evaluation and early detection measures are needed. It will be
necessary to create new test collections, oriented to diferent use cases related to disorders derived
from gambling. For example, by retrieving, processing, and extracting data on the Internet related
to the diferent phases of the problem, ranging from regular and not particularly harmful gambling
behaviour to dangerous gambling phases or gambling disorder. To that end, we need to compile
on-topic evidence about diferent issues or related themes (mental health, emotional impact,
psychological efects, financial dificulties, academic, work or social problems, legal issues, etc.).
The team that leads this project has extensive experience in creating new datasets and reference
collections [
We will work on the automatic elaboration of lexicographic resources adapted to the domain of searching for signs of risk of pathological gambling. We will consider some existing multilingual analytical extraction tools, such as LinguaKit [32] or the well-known Stanford NLP Toolkit, whose linguistic modules can be improved and adapted to this project. Also, recent large language models developed by Open AI (ChatGPT, GPT4) and Google (Bard), among others, will also be used to take advantage of their advanced language capabilities which, connected with the appropriate information for this project, can automatically perform tasks such as automatic cataloguing or summary generation. • O4. Develop flexible and eficient solutions for the massive processing of data from multiple online sources, including social media, and implement real-time analysis of online content. We have experience in designing and implementing Big Data solutions for early risk detection [33]. We have also developed publicly available tools for real-time processing of social media data [34]. However, there are a number of challenges when designing Big Data solutions in the context of this project. For example, we need to process information in real-time to extract web content and analyse user-generated publications related to gambling. • O5. Define methods for analysing results, generating conclusions, and exploiting expert knowledge (for example, from psychologists, medical specialists, or communication professionals). Determine the ways in which expert knowledge can guide the identicfiation of reputable content and how to adapt communication measures or public preventive strategies to the psychological or risk profile of users.
The validation of the solutions developed under this project must be carried out by experts in relevant sectors, and the team of the project includes two professionals specialised in Psychology and Communication, respectively. Their participation will allow to inject expert knowledge that can guide the identification of relevant elements (problem phases, psychological impact, prominent themes, etc.), and to validate the results and determine efective exploitation strategies. Likewise, it will also be necessary to determine, when appropriate, timely communication strategies to, for example, configure recommendations for preemptive programs, and suggest reputable and high quality support content for people at risk. In this sense, it will be necessary to advance in understanding how diferent users react to diferent preventive or risk communication campaigns and, also, help them to identify toxic, false information or harmful recommendations (for example, commercial sites that incite them to consume and play online). To propose recommendations related to various disorders, it is necessary to study social, personality, and psychological dimensions [35].
The project has the potential to produce high-quality results at regional, national, and international levels. Our previous activity in early risk detection and monitoring signs of depression, anorexia, and other concerning disorders has been very well received internationally and further exploited by psychologists in our environment. For example, some of our Artificial Intelligence methods have led, in collaboration with psychologists from the University of A Coruña and experts from the University of Notre Dame in the USA, to suggestions for improving current monitoring tools for adolescents at risk due to problematic family situations (see [36], a collaborative work between USC, UDC, and the University of Notre Dame aimed at applying Machine Learning to predict risk in adolescents with problematic family situations). Therefore, we have experience in exploiting results with clinical professionals and are in the best position to do so with the results of this new project. On the other hand, the data and resources we have generated in the past (test collections, exploratory experimental challenges, etc.) have had a high global impact and represent a valuable resource to boost research in these areas.
Regarding this project, the new collections, resources, and experimental methods have the potential to produce a global impact and will be distributed publicly and openly so that many other teams can advance in this field. We expect to build large-scale reusable test collections that will become an international reference for studying the interactions between diferent problems and aspects of addictive gambling disorder and the use of natural language. We also hope to lay the experimental foundations (new performance metrics, new computationally eficient ways to create resources) for the early prediction of addictive gambling risks. These innovative evaluation methods have the potential to be useful not only for this project but also as a way to assess early risk prediction in other domains (for example, identifying sexual predators, cyberbullying, terrorism, etc.).
The new search methods, language technologies, and communication and recommendation models tailored to the case of addictive gambling disorders are also highly innovative developments and can lead to pioneering methods. For example, we hope to propose creative ways to automatically search for signs of disorders and their underlying problems (personal health, emotional and psychological impact, ifnancial, work, academic, social, or legal dificulties, etc.). We will investigate new search methods, based on the automatic construction of queries from expert knowledge, and these information filtering and selection strategies can have an impact beyond this project (for example, to support health-related searches in clinical repositories). The recommendation of content associated with gambling disorders and the related communication strategies will be another highly innovative area of our activity whose results have the potential to contribute beyond the scope of the project. Likewise, the large-scale processing technology aims to serve not only this project but also the international community (for example, in projects and activities requiring real-time processing or analysis on social networks). Moreover, our results have great potential to be published in high-impact international venues and disseminated to society.
In terms of social impact, the participation of experts in Psychology, as a fundamental part of the project, ensures great potential for the exploitation of the results. In this sense, we will propose use cases (individualised analysis of subjects, study of disorders in communities or population groups, etc.) that can help psychologists, educators, and teachers in their daily activity. Indeed, the project can generate new valuable knowledge (for example, providing data on the evolution of diferent dimensions associated with disorders: disafection, sleep problems, weight loss, etc.). This result is useful in itself for diferent professionals interested in addictive gambling disorders.
The project is also promising for sparking new prevention campaigns and communication strategies about gambling addictions. The team of the project incorporates an expert professor in Communication. We expect to obtain substantial evidence about the disorders, their appearance and evolution, and, thus, instigate recommendations to institutions (Ministry of Consumer Afairs and Ministry of Health, in addition to other related agencies and councils at the regional level) on public communication and preemptive policies. This is crucial, as numerous studies warn about the growth of gambling problems in our society. For example, according to 2021 data from the Spanish General Directorate for Gambling Regulation (DGOJ), we had in Spain an estimated number of 1,400,000 online gamblers; and these ifgures have been growing over the last decade. Although many of these active players do not have a pathological disorder, it is essential to analyse this population and identify the potential emergence of problems. We will work on communicating the project’s results to relevant public institutions and, in fact, we also have experience in this area.
We will also consider, when appropriate, the use of certain project results for signing exploitation agreements with third parties. This project has the potential to transfer results, and we included in the task plan the academic formalisation of problems of interest (for example, extraction, analysis, or search tools) for potential stakeholders. The team has experience in executing R&D contracts with companies. The planned approach for this project consists of, where appropriate, registering the intellectual property of the software so that universities can exploit it through contracts or exploitation agreements. This is compatible with sharing other project results with the scientific community (for example, datasets, linguistic resources, and algorithms to favour reproducibility). This is something we have been doing regularly.
Our project is aligned and relevant to the following key topics of the RCIS conference: • Information Search and Discovery: By mining publicly available web content, the project aims to discover and extract traces and evidence related to gambling disorders, contributing to the improvement of information search and discovery techniques in this domain. • Big Data & Business Analytics: Through the application of data science techniques and analytics, the project addresses the challenge of processing vast amounts of web data to identify linguistic patterns indicative of gambling disorders. • Digital Transformation: The project represents a digital transformation initiative aimed at leveraging advanced technologies to address societal issues such as gambling disorders through innovative approaches in web mining and natural language processing. • Social Computing: Understanding the way people use language and examining the evolution of language usage patterns in relation to gambling disorders aligns with the principles of social computing, ofering insights into multiple user profiles of interest and their interactions in online environments. • Health Informatics and E-Health: Given the focus on early detection of pathological gambling, the project intersects with the e-health domain, where technological advancements play a crucial role in preventing psychological disorders related to gambling in individuals.
In this paper we have presented the project entitled “Web Mining, Search Technologies and Natural Language Processing for the Early Detection of Pathological Gambling”, funded by Ministerio de Consumo, Subdirección General de Regulación del Juego (Government of Spain).
This project focuses on technologies and computational models that perform large-scale natural language analysis. The aim is to design and implement new monitoring and analytical tools that, from publicly available information on the web, mine contents and extract traces and evidence related to gambling disorders. More specifically, the main goal is to study the way people use language (and the evolution of the use of language) to reveal early signs of gambling disorders.
This research was funded by Ministerio de Consumo, Subdirección General de Regulación del Juego, from the Government of Spain, under grant number SUBV23/00002 (Project entitled “Detección Temprana de Riesgos de Aparición de Trastornos Adictivos de Juego mediante Minería Web con Modelos Avanzados de Búsqueda y Procesamiento de Lenguaje Natural ”). Website of the project.
The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G-2019/04, ED431C 2022/19) and the European Regional Development Fund, which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System. International conference of the cross-language evaluation forum for European languages, Springer, 2016, pp. 28–39. [22] D. E. Losada, F. Crestani, J. Parapar, erisk 2017: Clef lab on early risk prediction on the internet: experimental foundations, in: Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11–14, 2017, Proceedings 8, Springer, 2017, pp. 346–360. [23] D. E. Losada, F. Crestani, J. Parapar, Overview of erisk: early risk prediction on the internet, in: Experimental IR Meets Multilinguality, Multimodality, and Interaction: 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, September 10-14, 2018, Proceedings 9, Springer, 2018, pp. 343–361. [24] D. E. Losada, F. Crestani, J. Parapar, Overview of erisk 2019 early risk prediction on the internet, in: Experimental IR Meets Multilinguality, Multimodality, and Interaction: 10th International Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9–12, 2019, Proceedings 10, Springer, 2019, pp. 340–357. [25] D. E. Losada, F. Crestani, J. Parapar, Overview of erisk at clef 2020: Early risk prediction on the internet (extended overview)., CLEF (Working Notes) (2020). [26] J. Parapar, P. Martín-Rodilla, D. E. Losada, F. Crestani, Overview of erisk at clef 2021: Early risk prediction on the internet (extended overview)., CLEF (Working Notes) (2021) 864–887. [27] P. Martın-Rodilla, D. E. Losada, F. Crestani, Overview of erisk 2022: Early risk prediction on the internet, in: Experimental IR Meets Multilinguality, Multimodality, and Interaction: 13th International Conference of the CLEF Association, CLEF 2022, Bologna, Italy, September 5–8, 2022, Proceedings, volume 13390, Springer Nature, 2022, p. 233. [28] D. E. Losada, M. Herrmann, D. Elsweiler, Cost-efective identification of on-topic search queries using multi-armed bandits, in: Proceedings of the 36th Annual ACM Symposium on Applied Computing, 2021, pp. 645–654. [29] J. M. Chenlo, J. Parapar, D. E. Losada, Comments-oriented query expansion for opinion retrieval in blogs, in: Advances in Artificial Intelligence: 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Proceedings 15, Springer, 2013, pp. 32–41. [30] L. Azzopardi, R. T. Fernández, D. E. Losada, Improving sentence retrieval with an importance prior, in: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010, pp. 779–780. [31] D. E. Losada, J. Parapar, A. Barreiro, A rank fusion approach based on score distributions for prioritizing relevance assessments in information retrieval evaluation, Information Fusion 39 (2018) 56–71. [32] P. Gamallo, M. Garcia, Linguakit: uma ferramenta multilingue para a análise linguística e a extração de informação, Linguamática 9 (2017) 19–28. [33] R. Martínez-Castaño, J. C. Pichel, D. E. Losada, A big data platform for real time analysis of signs of depression in social media, International journal of environmental research and public health 17 (2020) 4752. [34] R. Martínez-Castaño, J. C. Pichel, D. E. Losada, Building python-based topologies for massive processing of social media data in real time, in: Proceedings of the 5th Spanish Conference on Information Retrieval, 2018, pp. 1–8. [35] S. E. Baumgartner, T. Hartmann, The role of health anxiety in online health information search,
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