Analyzing Gambling Addictions: A S panish Corpus for Understanding Pathological Behavior (2025)

Abstract: This work fosters research on the interaction between natural language use and gambling disorders. We have built a new Spanish corpus for screening standardized gambling symptoms. We employ search methods to find on-topic sentences, top-k pooling to form the assessment pools of sentences, and thorough annotation guidelines. The labeling task is challenging, given the need to identify topic relevance and explicit evidence about the symptoms. Additionally, we explore using state-of-the-art LLMs for annotation and compare different sentence search models.

## introduction compulsive gambling, or gambling disorder, consists of a powerful urge to gamble repeatedly, even when it causes significant financial, emotional, and personal harm. gambling involves risking something valuable for a potential gain @xcite . for those struggling with this disorder, it can manifest as chasing losses with ever-increasing bets, depleting savings, and accumulating debt. in recognition of the growing global problem of gambling disorders, the world health organization (who) incorporated them into the international classification of diseases (icd-11) in 2019. 1 similarly, the diagnostic and statistical manual of mental disorders (dsm-5), published in 2013, included pathological gambling as a condition warranting further investigation @xcite . despite the severity of this disorder, many individuals remain undiagnosed or receive treatment late. existing preventive tools often fall short, thereby opening a critical need for new instruments that can effectively distinguish the spectrum of gaming behaviors, from regular and healthy engagement to hazardous gaming and full-blown disorder @xcite . the vast amount of content shared on the internet and social media presents a unique opportunity for large-scale analysis of psychological traits associated with various disorders. for example, individuals struggling with gambling disorders often find online communities or forums where they connect with others, share their experiences, and seek support from professionals. this data offers a window into the minds and emotions expressed through written communication. our work analyzes online interactions to identify different publications (posts) containing gambling-related behaviors. we contribute with a new spanish dataset foot_1 designed for symptom-level gambling screening. unlike other disorders, gambling has not garnered enough attention from researchers in computational mental health. the dataset contains sentences labeled according to their relevance to multiple gambling symptoms (section 3.2). identifying symptoms at the sentence level helps to detect gambling problems more accurately. it allows models to understand subtle signs, like denial, guilt, or impulsiveness, which might be missed when looking at a whole document. these fine-grained labels potentially produce better monitoring tools and more informative output to end-users (e.g., public institutions screening for signs of gambling). candidate sentences were obtained by leveraging 18 ranking methods and applying top-k pooling on their relevance rankings. four assessors, including a trained psychologist, annotated each candidate sentence, considering its relevance to each gambling symptom and determining if the sentence offered explicit details about someone (e.g., the post's writer) exhibiting the specific symptom. additionally, we explore the ability of recent state-ofthe-art large language models (llms) to annotate the dataset. llms can efficiently process massive textual datasets, potentially reducing annotation times. we provide insights into their strengths and limitations by comparing llms' effectiveness with human assessors. for example, human assessors are susceptible to inherent biases, but llms can also inherit biases from their training data. we can summarize our contributions as follows: ## related work while psychology has traditionally dominated the area of gambling disorder research, machine learning methods powered by user-generated data have emerged as complementary tools. a pioneering study by @xcite employed k-means clustering to identify customers with similar first-month online betting behaviors. the data came from an internet betting service. other studies @xcite have also leveraged data from online gambling platforms, including lottery providers and casinos. in the literature, multiple aspects have been analyzed, including player behavior @xcite , transactions @xcite , and even communications between customers and online gambling support personnel @xcite . notably, the clef erisk workshop marked a significant advancement by leveraging social media data for pathological gambling analysis @xcite @xcite @xcite . inspired by this line of research, our research builds on the idea that studying online interactions can help us find social media posts where people discuss or engage in gambling behaviors. creating new datasets in languages other than english will help to investigate new linguistic markers indicative of gambling addictions and advance early detection and intervention strategies. ## corpus creation the construction of our gambling dataset is oriented explicitly to identifying sentences indicative of gambling-related symptoms. to that end, we leverage a well-established clinical questionnaire (apa, 2022) as our reference for identifying core symptoms within user-generated texts (see section 3.2). figure 1 depicts the whole process, consisting of three steps: i) data collection, ii) sentence retrieval, and iii) sentence labeling. ## retrieval of gambling symptoms in we also evaluated the inclusion of domain knowledge into the models @xcite . we re-ranked the st multi top-k results with a custom cross-encoder that uses a domain-adapted language model named ludobeto (appendix a.1). ## conclusion and future work this study has introduced a new language resource for fostering research on standardized gambling symptoms. the dataset includes sentences tagged based on their relevance to crucial gambling assessment criteria. we used advanced search techniques to locate relevant sentences, top-k pooling to create assessment sentence pools and annotation guidelines. furthermore, we showed that llms could act as filters to remove non-relevant sentences from the candidate pools, but they still struggle to infer psychological states from textual cues. in future work, we aim to continue expanding this corpus, exploring the potential of other models, and implementing hybrid annotation approaches combining the strengths of llms and humans.