The challenge of accurately classifying events in a stream, despite years of research, still demands attention, especially when it involves understanding complex events. An example of such complexity is identifying tweets written by individuals that may suggest the presence of a disease. This issue was addressed at the eRisk 2024 Lab during CLEF 2024, where the task focused on analyzing evidence to detect early signs of anorexia. The objective of the proposed solution is to determine, as early as possible from a sequence of events (messages), whether an individual is sufering from anorexia. To achieve this goal, we introduce a novel architecture that merges language models with recommender systems, facilitating the fast classification of new messages. Our model demonstrates good performance, with an average F1 score up to 0.68 and a recall rate of 0.97 on a real-life imbalanced dataset.
Social interactions and personal conversations have largely shifted to the digital world due to the limitless availability of the Internet. While the mode of communication changes, the emotions involved remain the same, making social media platforms an important source of information for mental health studies. Text analysis ofers a plethora of use cases for detecting mental health issues such as depression, anxiety or eating disorders. As these conditions become more prevalent and have a serious impact on individuals’ health, there is a growing need for new detection methods.
Constructing text classification models to analyze a stream of messages presents a significant challenge, particularly when the objective is to identify specific phenomena, such as depression or anorexia, early on, with both high precision and recall, thereby achieving a high F1 score. This task becomes even more complex when the messages are composed by humans using unique online communication language (lingo). Such content can contain crucial information about the user, potentially revealing health issues or situations where intervention is necessary.
In the eRisk 2024 Lab [
Our research leverages the latest advancements in Natural Language Processing (NLP) along with a recommendation system architecture. By analyzing the behavior of models that suggest content based on user similarities, we assess whether the recommended content originates from individuals experiencing this condition. This approach enables us to issue early warnings for potential risks of exhibiting eating disorder symptoms.
Throughout the evolution of natural language processing (NLP), studies have highlighted its efectiveness
in such areas as healthcare or psychology, i.e. in spotting symptoms and aiding in early detection,
which can shorten the time to diagnosis [
The method used to distinguish between various message types is known as document classification.
This traditional task involves, for example, assigning a tag or class to a document that appears within
a stream of messages or is part of a collection. From a diferent angle, a recommendation system
undertakes a similar task by attempting to predict whether a document aligns with user preferences,
efectively performing a type of classification on a set of messages. Traditionally, the recommendation
systems have been designed in two variants: content-based and collaborative recommendation systems.
The former relies on aligning the characteristics of user profiles with the attributes of items, aiming to
recommend content that is similar, and becomes more efective as it gathers more information about
the user [
The application of Deep Learning techniques has become increasingly evident in the field of recommender systems, particularly those that process textual or visual inputs and where contextual details are significant [ 18, 19]. Additionally, models originally designed for language processing, such as BERT, have been adapted to improve recommendation generation, surpassing the performance of existing top models [20]. Despite the widespread use of NLP techniques to enhance the efectiveness of recommendation systems, there appears to be a lack of research on employing these systems directly for the classification of mental health disorders.
We propose a model designed for the early identification of potential mental health issues, utilizing recommender systems enhanced by natural language processing (NLP) techniques. This model is built upon three foundational elements: document embeddings, user embeddings, and a recommendation engine. The initial two components employ language models to transform textual data into numerical representations. Our approach incorporates the Sentence Transformer architecture, creating document embeddings that are well-suited for similarity assessments. This process facilitates the generation of user profiles based on the transformed data. By converting these textual and user representations into a format analogous to the classic recommendation scenario involving items and users, we can interpret a user’s posting history as a matrix of interactions. In the final stage, we deploy a hybrid recommendation system that leverages both document and user embeddings to tailor recommendations to individual user profiles. The system evaluates recommended posts in relation to their authors, where a higher proportion of contributions from users experiencing mental health challenges is indicative of a positive detection.
A key component of the model is the representation of text, which influences both the final document and the representation of the user. Among various NLP techniques suitable for this task, Sentence Transformers have been shown to produce high-quality embeddings that are ideal for similarity comparisons [21]. This approach not only eliminates the need for prior preprocessing but also accommodates raw text and supports multi-lingual datasets. It generates a fixed-sized numerical vector as output, which is applicable irrespective of the text’s length. We decided to use all-mpnet-base-v2 model, which is listed on top of the ranking1 published by authors of the SBERT framework.
The size of the document embeddings is determined by the chosen model; in our case, they are 768-dimensional vectors. The high dimensionality complicates comparison using conventional methods. Although there are methods to handle sparse data, we recommend employing dimensionality reduction techniques to decrease the time required for generating recommendations. This strategy is advocated for e.g., Topic Modeling [22] and has been shown to efectively preserve the contextual information of texts in a more generalized form. For this purpose, we have utilized the UMAP model [23], which can be trained in a supervised manner. Providing binary labels for the training embeddings enhances the model’s ability to distinguish between text representations from two distinct user groups.
To address the challenge of comparing authors and overcoming the issue of user cold start, we create user embeddings. This process falls under the scope of Author Profiling in the field of NLP. While our methodology primarily generates these embeddings from a history of documents authored by the users, it is possible to enrich them with supplementary data such as age, geographical location, profession, etc. For every user, we compute the average of their document embeddings, resulting in a fixed-size vector that distinctly characterizes authors based on their written works.
Document and user embeddings, along with an interaction matrix, constitute the input for our
recommender system. To leverage the features of texts and their authors, and to accommodate profiles of new
users, we opted for a hybrid approach - LightFM [
For every new user, we apply our model to generate recommendations of posts written by authors from the training dataset. From the top 20 documents, we measure the share of those coming from authors with anorexia. The positive classification is given for the users with scores higher than the predefined threshold of 50%. Additionally, to prevent too early classification based on limited information, we have set our threshold to 100% for the first five iterations. Meaning that the model delays triggering the alert, unless all recommended posts come from authors struggling with anorexia. According to the eRisk 2024 task constraints, positive classification also stops further processing of the user’s stream of writings.
Our research involved analyzing a dataset aimed at the Early Detection of Mental Health Disorders [24]. This dataset comprises 823,850 social media posts along with their titles from 1,287 participants. Each 1https://www.sbert.net/docs/pretrained_models.html entry is timestamped, which facilitates sequential analysis, a point elaborated upon in discussions about the online learning component of our study. A notable aspect of this dataset is the uneven distribution between users dealing with anorexia (134) and the rest of the participants (1153). In our model, we leveraged language models for text representation, which allowed us to bypass traditional preprocessing steps. We combined the title and body of each post into a single text string for analysis.
In the experiment we perform, our model receives continuous updates with new posts from each user, mimicking the natural pattern of social media activity. The model is not retrained; instead, it updates the user profile for more precise recommendations. Once an alert for a user is activated, the model ceases to analyze subsequent posts from that individual.
Our findings indicate that posts from users with an eating disorder are more likely "to be recommended" to others facing similar challenges, aiding in early detection. We evaluated the efectiveness of our model by the proportion of recommendations that come from users with eating disorders, using a numerical score to determine when a positive classification is warranted.
The model was evaluated by us on a test dataset, as well as by eRisk Lab authors using a separate evaluation dataset, that was not accessible to us before the evaluation process. The model exhibits similar results on both datasets, as presented in Table 1, demonstrating its strong generalization capability. It has achieved very low ERDE loss (ranging between 0.02 and 0.07), indicating its ability of triggering an alert with minimal delay and proving that small number of social media posts is enough to perform accurate classification. The model’s high recall score (0.85-0.97) indicates its proficiency in correctly identifying most positive cases. However, the lower precision (0.45-0.56) suggests that the model mistakenly identifies some negative samples as positive, an issue that could potentially be mitigated by increasing the classification threshold.
We have implemented a mental health disorder detection model that achieves robust F1 score of 0.68, high recall rate of 0.97, while maintaining a low ERDE loss ranging between 0.02 and 0.07. This model leverages advancements in the Natural Language Processing and combines them with recommender system architecture to detect similarities between users with and without anorexia.
This research contributes to the broader domain of tasks focused on early detection and intervention for mental health disorders through digital platforms. We suggest expanding already existing use cases for recommender systems with NLP methods for proactive health management, addressing the urgent need for early identification of eating disorders. The overall good results prove the feasibility of employing recommender systems in the task of mental health issues detection. Its simplicity and flexibility shows a potential in applying it in real-life application and enhancing the social impact of recommender systems. Its modular design ensures ongoing development in parallel with the advancements of NLP and emergence of more powerful language models. The promising results indicate the importance of continuous improvement and adaptation of these models to keep pace with the evolving nature of online communication and mental health challenges.
Future work should focus on improving the model’s precision score, to eliminate false negative predictions. We propose investigating the optimal delay before triggering an alert, as too short posts might initially mislead the model. A similar approach to detect an adequate amount of data enough for prediction was previously suggested [25], by calculating the combined size of the text taken into analysis, instead of only relying on number of posts. Moreover, our suggested technique for creating user embeddings rely solely on the averaged document embeddings. More time-aware methods could be considered to reflect the transition of mental state between the posts, especially as post creation timestamps are available in the dataset. [18] S. Zhang, L. Yao, A. Sun, Y. Tay, Deep learning based recommender system, Acm Computing
Surveys 52 (2019) 1–38. doi:10.1145/3285029. [19] S. Jeong, Y. Kim, Deep learning-based context-aware recommender system considering contextual features, Applied Sciences 12 (2021) 45. doi:10.3390/app12010045. [20] F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, P. Jiang, Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer, in: Proceedings of the 28th ACM international conference on information and knowledge management, 2019, pp. 1441–1450. [21] N. Reimers, I. Gurevych, Sentence-bert: Sentence embeddings using siamese bert-networks, arXiv preprint arXiv:1908.10084 (2019). [22] M. Grootendorst, Bertopic: Neural topic modeling with a class-based tf-idf procedure, arXiv preprint arXiv:2203.05794 (2022). [23] L. McInnes, J. Healy, J. Melville, Umap: Uniform manifold approximation and projection for dimension reduction, arXiv preprint arXiv:1802.03426 (2018). [24] F. Crestani, D. E. Losada, J. Parapar, Early risk prediction of mental health disorders, Early Detection of Mental Health Disorders by Social Media Monitoring (2022) 1–6. doi:10.1007/ 978-3-031-04431-1_1. [25] D. Ramírez-Cifuentes, M. Mayans, A. Freire, Early risk detection of anorexia on social media, in: Internet Science: 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24–26, 2018, Proceedings 5, Springer, 2018, pp. 3–14.