Abstract Rates of mental health concerns are rising, and an increasing number of individuals openly share their experiences on social media platforms (Hasell and Nabi, in: Emotions in the digital world: exploring affective experience and expression in online interactions, Oxford University Press, 2023). This openness creates an opportunity to study, detect, and ultimately support those at risk using data-driven methods. We focus on Anorexia Nervosa , an eating disorder characterized by persistent restriction and an intense fear of weight gain. Early, automatic identification can enable timelier assessment and intervention. We propose a transformer-based time-series model that analyzes longitudinal Reddit activity to estimate an individual’s likelihood of Anorexia. The model jointly captures temporal dynamics (how signals evolve over time) and semantic content (what the posts mean), yielding an accuracy of $$85.2\%$$ . In our experiments, this approach outperforms baselines that rely solely on semantic features, underscoring the value of modeling user trajectories rather than treating posts in isolation. We further conduct post-hoc explanation analyses to highlight the features most responsible for the model’s predictions, and we show that these attributions align with human intuition. Code for our approach is available in the repository here ( https://anonymous.4open.science/r/From-Posts-to-Patterns-Detecting-Anorexia-on-Reddit-6CB7/ ).

This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4-5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in ExIR, while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will delve into method families and their adaptations to IR, extensively covering post-hoc methods, axiomatic and probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, patent and legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on applying ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike.

Established information retrieval (IR) systems are generally reactive in that they respond to active entry of a search query by a user. Information is thus only provided to a user when they identify a need for information, and invest the effort to address this need using a search engine. As such, users end up spending more effort to satisfy their information need. Different from reactive systems, proactive information retrieval (PIR) systems seek to retrieve relevant content without the user explicitly submitting a query. A PIR system usually achieves this by using a combination of observed user activities, their contexts and user profile information firstly to automatically create search queries, and then perform search operations and present retrieved results. This new paradigm of PIR methodology thus presents opportunities for investigating a number of research questions for improving user satisfaction, such as when would be the right opportunities to deliver information to users, what should be the presentation mode of search results, establishing a conversational communication channel with the users to ask clarifying questions to refine the retrieved information, and even providing users with explanations for the retrieved information. The aim of the workshop thus is to solicit submissions from the research community targeted towards these topics, and consolidate the research directions in this domain via exchange of ideas.

A widespread use of supervised ranking models has necessitated an investigation on how consistent their outputs align with user expectations. While a match between the user expectations and system outputs can be sought at different levels of granularity, we study this alignment for search intent transformation across a pair of queries. Specifically, we propose a consistency metric, which for a given pair of queries - one reformulated from the other with at least one term in common, measures if the change in the set of the top-retrieved documents induced by this reformulation is as per a user's expectation. Our experiments led to a number of observations, such as DRMM (an early interaction based IR model) exhibits better alignment with set-level user expectations, whereas transformer-based neural models (e.g., MonoBERT) agree more consistently with the content and rank-based expectations of overlap.

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e.g., race, gender etc.). Our proposed method involves learning a multi-objective function that in addition to learning the primary objective of predicting the primary class labels from the data, also employs a clustering-based heuristic to minimize the disparities of the class label distribution with respect to the cluster memberships, with the assumption that each cluster should ideally map to a distinct combination of attribute values. Experiments demonstrate effective mitigation of cognitive biases on a benchmark dataset without the use of annotations of secondary attribute values (the zero-shot case) or with the use of a small number of attribute value annotations (the few-shot case).

The workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) seeks to encourage researchers to investigate automated methods to analyze and understand conversations, and also explore methodologies for proactively providing assistance to the communicating parties during conversations, ranging from summarizing the minutes of meetings to automatically keeping track of action items etc. The workshop will have (1) a regular research paper track, and a more focused (2) data challenge track, inviting papers on a specific task of contextualizing entities of interest from conversation dialogues.

It is often useful for an IR practitioner to analyze the similarity function of an IR model, or for a non-technical search engine user to understand why a document was shown at a certain rank, in terms of the three fundamental aspects of a similarity function, namely the a) frequency of a term in a document, b) frequency of a term in a collection and c) the length of a document. We propose a general methodology of approximating an IR model as the coefficients of a linear function of these three fundamental aspects (and an additional aspect of semantic similarity between terms for neural models), which potentially can help IR practitioners to optimize the relative importance of each aspect on specific document collection and types of queries. Our analysis shows that the coefficients, which represent the relative importance of the three fundamental aspects, are useful to compare a model's different parametric instantiations or compare across different models.

Users of current search systems actively interact with the system to complete their search task. This can encompass formulating and reformulating a series queries expressing evolving of different information needs. We believe that the next generation of search systems will see a shift towards proactive understanding of user intent based on analysis of user activities. Such a proactive search system could start recommending documents that are likely to help users accomplish their tasks without requiring them to explicitly submit queries to the system. We propose a framework to evaluate such a search system. The key idea behind our proposed metric is to aggregate a correlation measure over a search session between the expected outcome, which in this case refers to the list of documents retrieved with a true user query, and the predicted outcome, which refers to the list of documents recommended by a proactive search system. Experiments on the AOL query log data show that the ranking of two sample proactive IR systems induced by our metric conforms to the expected ranking between these systems.