Identifying Moments of Change from Longitudinal User Text
- URL: http://arxiv.org/abs/2205.05593v1
- Date: Wed, 11 May 2022 16:03:47 GMT
- Title: Identifying Moments of Change from Longitudinal User Text
- Authors: Adam Tsakalidis, Federico Nanni, Anthony Hills, Jenny Chim, Jiayu
Song, Maria Liakata
- Abstract summary: We define a new task 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.
- Score: 16.45577617206016
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- A Systematic Analysis on the Temporal Generalization of Language Models in Social Media [12.035331011654078]
This paper focuses on temporal shifts in social media and, in particular, Twitter.
We propose a unified evaluation scheme to assess the performance of language models (LMs) under temporal shift.
arXiv Detail & Related papers (2024-05-15T05:41:06Z) - Instilling Multi-round Thinking to Text-guided Image Generation [72.2032630115201]
Single-round generation often overlooks crucial details, particularly in the realm of fine-grained changes like shoes or sleeves.
We introduce a new self-supervised regularization, ie, multi-round regularization, which is compatible with existing methods.
It builds upon the observation that the modification order generally should not affect the final result.
arXiv Detail & Related papers (2024-01-16T16:19:58Z) - Learning signatures of decision making from many individuals playing the
same game [54.33783158658077]
We design a predictive framework that learns representations to encode an individual's 'behavioral style'
We apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task.
arXiv Detail & Related papers (2023-02-21T21:41:53Z) - Learning to Exploit Temporal Structure for Biomedical Vision-Language
Processing [53.89917396428747]
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities.
We explicitly account for prior images and reports when available during both training and fine-tuning.
Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
arXiv Detail & Related papers (2023-01-11T16:35:33Z) - Time-Varying Propensity Score to Bridge the Gap between the Past and Present [104.46387765330142]
We introduce a time-varying propensity score that can detect gradual shifts in the distribution of data.
We demonstrate different ways of implementing it and evaluate it on a variety of problems.
arXiv Detail & Related papers (2022-10-04T07:21:49Z) - Efficient Modelling Across Time of Human Actions and Interactions [92.39082696657874]
We argue that current fixed-sized-temporal kernels in 3 convolutional neural networks (CNNDs) can be improved to better deal with temporal variations in the input.
We study how we can better handle between classes of actions, by enhancing their feature differences over different layers of the architecture.
The proposed approaches are evaluated on several benchmark action recognition datasets and show competitive results.
arXiv Detail & Related papers (2021-10-05T15:39:11Z) - Room to Grow: Understanding Personal Characteristics Behind Self
Improvement Using Social Media [27.699640898659283]
We study the motivation-related behavior of people who persist with their intention to change.
Our experiments provide new insights into the motivation-related behavior of people who persist with their intention to change.
arXiv Detail & Related papers (2021-05-17T17:30:30Z) - Coarse Temporal Attention Network (CTA-Net) for Driver's Activity
Recognition [14.07119502083967]
Driver's activities are different since they are executed by the same subject with similar body parts movements, resulting in subtle changes.
Our model is named Coarse Temporal Attention Network (CTA-Net), in which coarse temporal branches are introduced in a trainable glimpse.
The model then uses an innovative attention mechanism to generate high-level action specific contextual information for activity recognition.
arXiv Detail & Related papers (2021-01-17T10:15:37Z) - Bursts of Activity: Temporal Patterns of Help-Seeking and Support in
Online Mental Health Forums [6.662800021628275]
We show that user activity on social media platforms follows a distinct pattern of high activity periods with interleaving periods of no activity.
We then show how studying activity during bursts can provide a personalized, medium-term analysis for a key question in online mental health communities.
Using two independent outcome metrics, moments of cognitive change and self-reported changes in mood during a burst of activity, we identify two actionable features that can improve outcomes for users.
arXiv Detail & Related papers (2020-04-21T22:39:38Z) - On Feature Normalization and Data Augmentation [55.115583969831]
Moment Exchange encourages the model to utilize the moment information also for recognition models.
We replace the moments of the learned features of one training image by those of another, and also interpolate the target labels.
As our approach is fast, operates entirely in feature space, and mixes different signals than prior methods, one can effectively combine it with existing augmentation approaches.
arXiv Detail & Related papers (2020-02-25T18:59:05Z) - Representation Learning on Variable Length and Incomplete
Wearable-Sensory Time Series [29.061466414756925]
HeartSpace encodes time series data with variable-length and missing values via the integration of a time series encoding module and a pattern aggregation network.
HeartSpace implements a Siamese-triplet network to optimize representations by jointly capturing intra- and inter-series correlations.
The empirical evaluation over two different real-world data presents significant performance gains overstate-of-the-art baselines in a variety of applications.
arXiv Detail & Related papers (2020-02-10T08:20:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.