# Identifying Moments of Change from Longitudinal User Text arXiv: https://arxiv.org/abs/2205.05593 ยท HTML: https://mlsanity.com/paper/2205.05593/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 65 references that are in the map. - First posted on arXiv 11 May 2022. ## Builds on (most cited first, 18 of 18) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015) - [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020) - [Calibrating Deep Neural Networks using Focal Loss](https://mlsanity.com/paper/2002.09437/) (2020) - [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016) - [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020) - [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020) - [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017) - [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017) - [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018) - [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020) - [Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention](https://mlsanity.com/paper/1910.12038/) (2019) - [Multi-Task Learning for Mental Health using Social Media Text](https://mlsanity.com/paper/1712.03538/) (2017) - [The Effect of Moderation on Online Mental Health Conversations](https://mlsanity.com/paper/2005.09225/) (2020) - [Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation](https://mlsanity.com/paper/1807.07351/) (2018) - [Detecting and Explaining Crisis](https://mlsanity.com/paper/1705.09585/) (2017) ## Built on by (most cited first, 17 of 17) - [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023) - [Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media](https://mlsanity.com/paper/2401.16240/) (2024) - [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024) - [TempoFormer: A Transformer for Temporally-aware Representations in Change Detection](https://mlsanity.com/paper/2408.15689/) (2024) - [Creation and evaluation of timelines for longitudinal user posts](https://mlsanity.com/paper/2303.05891/) (2023) - [WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis](https://mlsanity.com/paper/2308.13710/) (2023) - [From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP](https://mlsanity.com/paper/2601.07988/) (2026) - [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025) - [Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling](https://mlsanity.com/paper/2312.03523/) (2023) - [Temporal Validity Change Prediction](https://mlsanity.com/paper/2401.00779/) (2024) - [ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media](https://mlsanity.com/paper/2509.17991/) (2025) - [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026) - [A Baseline for Self-state Identification and Classification in Mental Health Data: CLPsych 2025 Task](https://mlsanity.com/paper/2504.14066/) (2025) - [LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight](https://mlsanity.com/paper/2601.03645/) (2026) - [Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectories](https://mlsanity.com/paper/2604.12097/) (2026) - [LiFT: How to Enable In-Context Learning for Longitudinal Modelling](https://mlsanity.com/paper/2604.16382/) (2026) - [Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics](https://mlsanity.com/paper/2606.31464/) (2026) ## Abstract (arXiv) Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic focuses on either: (a) identifying individuals at risk or with a certain mental health condition given a batch of posts or (b) providing equivalent labels at the post level. A disadvantage of such work is the lack of a strong temporal component and the inability to make longitudinal assessments following an individual's trajectory and allowing timely interventions. Here we define a new task, that of identifying moments of change in individuals on the basis of their shared content online. The changes we consider are sudden shifts in mood (switches) or gradual mood progression (escalations). We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines (18.7K posts). We have developed a variety of baseline models drawing inspiration from related tasks and show that the best performance is obtained through context aware sequential modelling. We also introduce new metrics for capturing rare events in temporal windows.