ArticleJournal of gambling studies2026
Detecting Gambling Addiction: How Text Mining of Customer-Operator Online Chats Reveals Behavioral Risks.
Article in Journal of gambling studies, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gambling disorder is a growing public health concern, exacerbated by the rapid expansion of online gambling. Prevention is key to supporting individuals from developing or exacerbating risky conducts. This study presents a multidisciplinary project across psychology and computational linguistics. The project aimed at developing a predictive system for detecting linguistic markers of problematic gambling behaviours in customer-operator chats from Sisal, a major Italian gambling company. The objective is to support staff in promptly recognizing at-risk individuals and providing timely assistance. The research was conducted in two phases. First, a qualitative analysis of Italian conversations on the online support centre chat identified linguistic and discursive markers of risk. These insights informed the training of a supervised binary classification model. In the second phase, the system was refined through dataset expansion, manual coding carried out by psychologists, and the introduction of a four-level risk classification. Markers were grouped into macro-categories and weighted according to emotional intensity and frequency before being integrated into the predictive model. Findings showed that combining linguistic analysis with psychological expertise can effectively enhance the early detection of problematic gambling behaviours. The research highlighted the potential of text mining as a tool for prevention and person-centred interventions in gambling disorder.
Indexed as
Identifiers
42053734What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.