Ireland

In this presentation I present our model for the shared task.

ACL 2022

Detecting Signs of Depression from Social Media Text

depression

logistic regression

visualization

machine learning

readability

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

Depression is a mental illness that negatively affects a person's well-being and can, if left untreated, lead to serious consequences such as suicide. Therefore, it is important to recognize the signs of depression early. In the last decade, social media has become one of the most common places to express one's feelings. Hence, there is a possibility of text processing and applying machine learning techniques to detect possible signs of depression. In this paper, we present our approaches to solving the shared task titled Detecting Signs of Depression from Social Media Text. We explore three different approaches to solve the challenge: fine-tuning BERT model, leveraging AutoML for the construction of features and classifier selection and finally, we explore latent spaces derived from the combination of textual and knowledge-based representations. We ranked 9th out of 31 teams in the competition. Our best solution, based on knowledge graph and textual representations, was 4.9% behind the best model in terms of Macro F1, and only 1.9% behind in terms of Recall.

BERT, AutoML and Knowledge-graph backed Detection of Depression

In this paper, we describe our approach for the task of homophobia and transphobia detection in English social media comments. The dataset consists of YouTube comments, and it has been released for the shared task on Homophobia/Transphobia Detection in social media comments. Given the high class imbalance, we propose a solution based on data augmentation and ensemble modeling. We fine-tuned different large language models (BERT, RoBERTa, and HateBERT) and used the weighted majority vote on their predictions.
Our proposed model obtained 0.48 and 0.94 for macro and weighted F1-score, respectively, ranking at the third position.

Ensemble Modeling for Homophobia and Transphobia Detection

Hateful and offensive content on social media platforms can have negative effects on users and can make online communities more hostile towards certain people and hamper equality, diversity and inclusion. In this paper, we describe our approach to classifying homophobia and transphobia in social media comments. We used an
ensemble of transformer-based models to build our classifier. Our model ranked 2nd for English, 8th for Tamil and 10th for Tamil-English.


Ensembled Transformers Against Homophobia and Transphobia

This paper presents our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022. The task was to create a system that, given social media posts in English, should detect the level of depression as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. We based our solution on transformer-based language models. We fine-tuned selected models: BERT, RoBERTa, XLNet, of which the best results were obtained for RoBERTa. Then, using the prepared corpus, we trained our own language model called DepRoBERTa (RoBERTa for Depression Detection). Fine-tuning of this model improved the results. The third solution was to use the ensemble averaging, which turned out to be the best solution. It achieved a macro-averaged F1-score of 0.583. The source code of prepared solution is available at https://github.com/rafalposwiata/depression-detection-lt-edi-2022.

Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language Models

Depression is a common mental disorder that severely affects the quality of life, and can lead to suicide. When diagnosed in time, however, mild, moderate, and even severe depression can be treated. This is why it is vital to detect signs of depression in time. One possibility for this is the use of text classification models on social media posts. Transformers have achieved state-of-the-art performance on a variety of similar text classification tasks. One drawback, however, is that when the dataset is imbalanced, the performance of these models may be negatively affected. Because of this, in this paper, we examine the effect of balancing a depression detection dataset using data augmentation. In particular, we use abstractive summarization techniques for data augmentation. We examine the effect of this method on the LT-EDI-ACL2022task. Our results show that when increasing the multiplicity of the minority classes to the right degree, this data augmentation method can in fact improve classification scores on the task.

Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classification

This paper describes the system submitted by our team for the homophobia/transphobia detection in social media comments shared task. A machine learning-based model has been designed and integrated with TF/IDF as a vector space model. SVM has been used to develop the proposed model and our submission reported 0.91, 0.92, and 0.88 weighted f1-score for English, Tamil, and Tamil-English datasets respectively.

Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM

The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task~i and applied it to the dataset for Task~j to iteratively integrate new silver data for Task~i. Our official submissions to the shared task obtained a macro-averaged F1_score of 0.53 for Task_hope and 0.46 for Task_antiLGBT, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.

When Hope and Hate Collide

A person feeling unimportant, useless, or unhappy may be a sign of depression. Depression is one of the most severe mental health conditions which may be unnoticed, undiagnosed and untreated in many cases. People worldwide suffer from depression and the affected person may operate poorly at work, studies, and in the community.
Knowingly or unknowingly the behavior of a person will be reflected in the comments she/he posts on social media. Social media users who are usually in depression try to seek the attention and sympathy of others by posting negative and disturbing messages or requesting help.
However, as analyzing the increasing amount of social media data manually in laborious and error-prone, automated tools have to be developed for the same.
To address the issue of detecting the sign of depression content on social media, in this paper, we - team MUCS, describe an Ensemble of Machine Learning (ML) models and a Transfer Learning (TL) model submitted to "Detecting Signs of Depression from Social Media Text-LT-EDI@ACL 2022" (DepSign-LT-EDI@ACL-2022) shared task at Association for Computational Linguistics (ACL) 2022.

Detecting Sign of Depression from Social Media Text using Supervised Learning Approach

Social media is considered as a platform where users express themselves. The rise of social media as one of humanity’s most important public communication platforms presents a potential prospect for early identification and management of mental illness. Depression is one such illness that can lead to a variety of emotional and physical problems. It is necessary to measure the level of depression from the social media text to treat them and to avoid the negative consequences. Detecting levels of depression is a challenging task since it involves the mindset of the people, which can change periodically. The aim of the DepSign-LT-EDI@ACL-2022 shared task is to classify the social media text into three levels of depression namely “Not Depressed”, “Moderately Depressed”, and “Severely Depressed”. This overview presents a description of the task, the data set, methodologies used and an analysis on the results of the submissions. The models that were submitted as a part of the shared task had used a variety of technologies from traditional machine learning algorithms to deep learning models. It could be observed from the result that the transformer based models have outperformed the other models. Among the 32 teams who had submitted their results for the shared task, the best macro F1-score of 0.583 was obtained using a transformer based model.

Findings of the Shared Task on Detecting Signs of Depression from Social Media

This paper illustrates the overview of the shared
task on automatic speech recognition in the
Tamil language. In the shared task, spontaneous
Tamil speech data gathered from elderly
and transgender people was given for recognition
and evaluation. These utterances were collected
from people when they communicated
in the public locations such as hospitals, markets,
vegetable shop, etc. The speech corpus
includes utterances of male, female, and transgender
and was split into training and testing
data. The given task was evaluated using WER
(Word Error Rate). The participants used the
transformer-based model for automatic speech
recognition. Different results using different
pre-trained transformer models are discussed
in this overview paper.

Detecting Signs of Depression from Social Media Text

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BERT, AutoML and Knowledge-graph backed Detection of Depression

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Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments