| Challenge: | Identifying changes in individuals’ behaviour and mood via shared content is gaining importance given the global increase in mental health disorders and the limited access to support services. |
| Approach: | They propose a task of identifying moments of change in individuals on the basis of their shared content online. |
| Outcome: | The proposed task is based on 500 manually annotated user timelines and shows that it performs best through context aware sequential modelling. |
Similar Papers
Creation and evaluation of timelines for longitudinal user posts (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for segmenting user posts into timelines improve quality and cost of manual annotation. |
| Approach: | They propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user’s behaviour based on their online posting activity. |
| Outcome: | The proposed framework is able to evaluate two different social media datasets and compares with existing models. |
Exciting Mood Changes: A Time-aware Hierarchical Transformer for Change Detection Modelling (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing work on temporally sensitive tasks focuses on predicting mood changes . however, there is little attention given to the importance of longitudinal language modelling . |
| Approach: | They propose a Hawkes process-inspired transformation layer to model the influence of time on users’ posts, capturing both their immediate and historical dynamics. |
| Outcome: | The proposed model outperforms existing models on two existing datasets and shows clear performance gains. |
Sequential Path Signature Networks for Personalised Longitudinal Language Modeling (2023.findings-acl)
Copied to clipboard
| Challenge: | Current work on low-dimensional static user representations or more importantly on dynamic user representation is limited. |
| Approach: | They propose to integrate path signatures from rough path theory into neural sequential models by integrating contextual neural representations and recursive neural networks. |
| Outcome: | The proposed model outperforms state-of-the-art models on macro-average F1 score on two available datasets and outperformed previous models which only have access to historical posts. |
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing models that model fine-grained opinion shifts of social media users are lacking . lack of publicly available datasets for this task presents a major challenge . |
| Approach: | They propose an annotated social media opinion dataset that provides a model for subtle opinion fluctuations and fine-grained stances. |
| Outcome: | The proposed dataset is comparable to the annotations of experts and non-experts. |
Short-Term Meaning Shift: A Distributional Exploration (N19-1)
Copied to clipboard
| Challenge: | a new study examines the phenomenon of short-term meaning shift in online communities . the authors use distributional representations to explore the phenomenon . |
| Approach: | They propose to use distributional representations to explore short-term meaning shift in online communities. |
| Outcome: | The proposed model has problems distinguishing meaning shift from referential phenomena, and measures contextual variability to remedy this. |
MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations (2024.acl-short)
Copied to clipboard
| Challenge: | a new problem setting is designed to detect critical moments in conversations . a human-annotated multi-modal dataset is used to classify and detect turning points . |
| Approach: | They propose a problem setting focusing on turning points in conversations as TPs . they propose MTPC, MTPD, & MTPR tasks to classify and detect turning points . |
| Outcome: | The proposed model achieves an F1-score of 0.88 in classification and 0.61 in detection . it uses state-of-the-art vision-language models to construct a narrative from the videos . |
Measuring and Modeling Language Change (N19-5)
Copied to clipboard
| Challenge: | This tutorial will help researchers answer questions fundamental to the social sciences and humanities . |
| Approach: | This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data . |
| Outcome: | The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars. |
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers (2023.emnlp-main)
Copied to clipboard
| Challenge: | valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. |
| Approach: | They study the relationship between tweet emotion dynamics and mental health disorders by using a user-disclosed diagnosis. |
| Outcome: | The results show that the measures varied by the user's self-disclosed diagnosis. |
Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
| Approach: | They collect first dataset of textual posts by same users before and after being diagnosed with depression and build multiple predictive models based on Transformers and BERT. |
| Outcome: | The proposed model can be used to detect depression and suicidal thoughts in users who are not diagnosed with depression or suicide. |
Exploring Word Usage Change with Continuously Evolving Embeddings (2021.acl-demo)
Copied to clipboard
| Challenge: | a new method to track word usage changes is proposed for text datasets that are collected over a longer period of time. |
| Approach: | They propose a way to track word usage changes via continuously evolving embeddings . they demonstrate an interactive web app that can explore semantic shifts with interactive plots a text . |
| Outcome: | The proposed method can be used to analyze word usage changes with interactive plots. |