United States

Social media data have been used in research for many years to understand users’ mental health. In this paper, using user-generated content we aim to achieve two goals: the first is detecting moments of mood change over time using timelines of users from Reddit. The second is predicting the degree of suicide risk as a user-level classification task. We used different approaches to address longitudinal modelling as well as the problem of a severely imbalanced dataset. For the first task, using BERT with undersampling techniques performed the best among models tested, including LSTM and random forests models. For the second task, extracting features related to suicide from posts’ text contributed to the overall performance improvement. Specifically, a feature representing of a number of suicide-related words in a post improved accuracy by 17%. 


NAACL 2022

Predicting Moments of Mood Changes Overtime from Imbalanced Social Media Data 

suicide

imbalanced data

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

This paper describes our systems for CLPsych’s 2022 Shared Task 1 . Subtask A involves capturing moments of change in an individual’s mood over time, while Subtask B asked us to identify the suicidality risk of a user. We explore multiple machine learning and deep learning methods for the same, taking real-life applicability into account while considering the design of the architecture. Our team, IIITH, achieved top results in different categories for both subtasks. Task A was evaluated on a post-level (using macro averaged F1) and on a window-based timeline level (using macro-averaged precision and recall). We scored a post-level F1 of 0.520 and ranked second with a timeline-level recall of 0.646. Task B was a user level task where we also came in second with a micro F1 of 0.520 and scored third place on the leaderboard with a macro F1 of 0.380.

Towards Capturing Changes in Mood and Identifying Suicidality Risk

We investigate the impact of using Multi-Task Learning (MTL) to predict mood changes over time for each individual (social media user) and also the impact it has on predicting an individual’s suicide risk severity. The presented models were developed as a part of the Computational Linguistics and Clinical Psychology (CLPsych) 2022 shared task.

Multi-Task Learning to Capture Changes in Mood Over Time

When reading stories, people can naturally identify sentences in which a new event starts, i.e. event boundaries, using their knowledge of how events typically unfold, but a computational model to detect event boundaries is not yet available. We characterize and detect sentences with expected or surprising event boundaries in an annotated corpus of short diary-like stories, using a model that combines commonsense knowledge and narrative flow features with a RoBERTa classifier. Our results show that, while commonsense and narrative features can help improve performance overall, detecting event boundaries that are more subjective remains challenging for our model. We also find that sentences marking surprising event boundaries are less likely to be causally related to the preceding sentence, but are more likely to express emotional reactions of story characters, compared to sentences with no event boundary.

Uncovering Surprising Event Boundaries in Narratives

While large language models have shown exciting progress on several NLP benchmarks, evaluating their ability for complex analogical reasoning remains under-explored. Here, we introduce a high-quality crowdsourced dataset of narratives for employing proverbs in context as a benchmark for abstract language understanding. The dataset provides fine-grained annotation of aligned spans between proverbs and narratives, and contains minimal lexical overlaps between narratives and proverbs, ensuring that models need to go beyond surfacelevel reasoning to succeed. We explore three tasks: (1) proverb recommendation and alignment prediction, (2) narrative generation for a given proverb and topic, and (3) identifying narratives with similar motifs. Our experiments show that neural language models struggle on these tasks compared to humans, and these tasks pose multiple learning challenges.

ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding

Internet forums such as Reddit offer people a platform to ask for advice when they encounter various issues at work, school or in relationships. Telling helpful comments apart from unhelpful comments to these advice-seeking posts can help people and dialogue agents to become more helpful in offering advice. We propose a dataset that contains both helpful and unhelpful comments in response to such requests. We then relate helpfulness to the closely related construct of empathy. Finally, we analyze the language features that are associated with helpful and unhelpful comments.

How to be Helpful on Online Support Forums?

Over the past decade, an extended line of research
developed a broad range of methods for reasoning
about narrative from a social perspective. This
often revolved around transforming literary text
into a character network representation. However,
there remain inconsistent traits of narrative
structure produced computationally by either neural
language technology or network theory tools. In
this paper, we propose an encoder-decoder model
with a main objective to mitigate the apparent
computational divergence. Our encoder novelty lies
in generating hundreds of location-based network
graphs to render a fine-grained narrative. We
further formalize a decoder task for detecting
character communities and analyze modularity and
membership affiliation. Through empirical
experiments, we present visualization of stages
in our computational process for four literary
fiction novels.

Multi-Graph Encoder-Decoder Model for Location-based Character Networks in Literary Narrative

Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our main goal is to understand how humans organize information to craft complex answers. We develop an ontology of six sentence-level functional roles for long-form answers, and annotate 3.9k sentences in 640 answer paragraphs. Different answer collection methods manifest in different discourse structures. We further analyze model-generated answers – finding that annotators agree less with each other when annotating model-generated answers compared to annotating human-written answers. Our annotated data enables training a strong classifier that can be used for automatic analysis. We hope our work can inspire future research on discourse-level modeling and evaluation of long-form QA systems.

How Do We Answer Complex Questions: Discourse Structure of Long-form Answers

We experiment with adapting generative language
models for the generation of long coherent
narratives in the form of theatre plays.
Since fully automatic generation of whole plays
is not currently feasible, we created an interactive
tool that allows a human user to steer the
generation somewhat while minimizing intervention.
We pursue two approaches to long-text
generation: a flat generation with summarization
of context, and a hierarchical text-to-text
two-stage approach, where a synopsis is generated
first and then used to condition generation
of the final script. Our preliminary results and
discussions with theatre professionals show improvements
over vanilla language model generation,
but also identify important limitations of
our approach.

GPT-2-based Human-in-the-loop Theatre Play Script Generation

We present a novel approach to generating
scripts by using agents with different personality
types. To manage character interaction
in the script, we employ simulated dramatic
networks. Automatic and human evaluation on
multiple criteria shows that our approach outperforms
a vanilla-GPT2-based baseline.

DialogueScript: Using Dialogue Agents to Produce a Script

Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we delineate the process of developing GisPy, an open-source tool in Python for measuring the Gist Inference Score (GIS) in text. Evaluation of GisPy on documents in three benchmarks from the news and scientific text domains demonstrates that scores generated by our tool significantly distinguish low vs. high gist documents. Our tool is publicly available to use at: https://github.com/phosseini/GisPy.

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Towards Capturing Changes in Mood and Identifying Suicidality Risk

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Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data

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Next from NAACL 2022

Towards Capturing Changes in Mood and Identifying Suicidality Risk

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Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data

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