Team- KEC @ LT - EDI : Detecting Signs of Depression from Social Media Text (2023)

Abstract: The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate speech, inflammatory remarks, and abusive language. The exponential growth of social media has facilitated the widespread circulation of hostile and toxic content, giving rise to an unprecedented influx of hate speech, incendiary language, and abusive rhetoric. The study utilized different techniques to represent the text data in a numerical format. Word embedding techniques aim to capture the semantic and syntactic information of the text data, which is essential in text classification tasks. The study utilized various techniques such as CNN, BERT, and N-gram to classify social media posts into depression and non-depression categories. Text classification tasks often rely on deep learning techniques such as Convolutional Neural Networks (CNN), while the BERT model, which is pre-trained, has shown exceptional performance in a range of natural language processing tasks. To assess the effectiveness of the suggested approaches, the research employed multiple metrics, including accuracy, precision, recall, and F1-score. The outcomes of the investigation indicate that the suggested techniques can identify symptoms of depression with an average accuracy rate of 56%.

## introduction the pervasive use of social media has introduced new challenges in detecting signs of depression from text shared on these platforms @xcite . researchers in nlp have utilized featurebased linear classifiers, cnn, and rnn architectures, as well as fine-tuning pre-trained language models like bert and roberta, to automatically detect signs of depression. while linear classifiers have shown competitive performance, pretrained models have achieved state-of-the-art results. however, pre-trained models may have limitations in understanding context-specific language. this project provides an overview of existing re-search, describes the task and dataset, proposes machine learning and deep learning models, presents experimental results, and concludes with potential avenues for future research.suicide is a serious public health problem; however, suicides are preventable with timely, evidence-based and often low-cost interventions 1 . the following sections of this document are structured as follows: section 2 provides a comprehensive review of existing research on the identification of signs of depression through analysis of social media text @xcite . section 3 provides a detailed explanation of the task at hand, including a description of the dataset employed in this study. our proposed machine learning and deep learning models for detecting signs of depression are presented in section 4. subsequently, section 5 outlines the conducted experiments and presents the corresponding results. a thorough discussion of these results is provided within the same section. finally, in section 6, we present our concluding remarks based on the findings and suggest potential avenues for future research in this field. ## literature survey the rapid growth of internet content and the anonymity of online platforms have made it challenging to manually identify signs of depression from social media text @xcite ). however, machine learning and nlp techniques, such as naive bayes, decision trees, random forests, svm, cnn, and rnn, have shown promise in automatically detecting signs of depression with high accuracy, even when tested on diverse datasets, including those associated with hate speech 2 . these advancements offer potential for efficient and automated detection and intervention in online contexts.results obtained from the literature review stated that bilstm + attention model performs well on depression related textual data. even though the achieved result may be satisfactory, there are certain issues with the model implemented in that research(david-william 2020).for example, guntuku s.c., yaden d.b., kern m.l., ungar l.h., eichstaedt j.c. detecting depression and mental illness on social media(guntuku-s.c. et al. 2017) focus on studies aimed at predicting mental illness using social media. first, they consider the methods used to predict depression, and then they consider four approaches that have been used in the literature: prediction based on survey responses, prediction based on self-declared mental health status, prediction based on forum membership, and prediction based on annotated posts.wang y.p., gorenstein c. assessment of depression in medical patients: a systematic review of the utility of the beck depression inventory-ii(wang.y.p. and gorenstein.c 2013) examined relevant investigations with the beck depression inventory-ii for measuring depression in medical settings to provide guidelines for practicing clinicians. the beck depression inventory-ii showed high reliability and good correlation with the measures of depression and anxiety. ## proposed classifiers the text-to-feature transformation is described, followed by the classification algorithms used for detecting signs of depression from social media text. for feature extraction, 3-gram techniques were employed, which are extensively used in nlp and text mining tasks.it capture contiguous sequences of three words, providing local word order and context information.thus when 3-gram is used it allows machine to recognize the 3 words as one entity which makes the text classification to next level. to classify the extracted features, two classifiers are proposed: cnn and bert. the proposed architecture is shown in [figure 1 ]. convolutional neural networks (cnn) are deep learning models that are effective in capturing local patterns in text data. cnns have shown promising results in various nlp tasks. we used cnn for the 3-gram feature extraction method 5 . the architecture of cnn algorithm includes a number of convolution layers, max-pooling layers, and fully connected layers. relu as an activation function is used in proposed work. this project leverages the power of bert (bidirectional encoder representations from transformers), a highly effective pre-trained language model known for capturing contextual information from text 6 . unlike traditional models, which looked at a text sequence only from one direction, the bert encoder attention mechanism works bidirectional training of transformer, which learns information from both the left and right sides of a word, allowing the model to catch a deeper sense of language context. we integrated bert into our 3-gram feature extraction method, allowing us to benefit from its capabilities in various nlp tasks, including text classification. each of the proposed classifiers takes the respective feature vectors as input and outputs the classification for each social media text. these classifiers have different specialties, and their performance metrics may vary. by utilizing the 3-gram technique along with cnn and bert classifiers, we aim to effectively detect signs of depression from social media text, providing valuable insights for mental health analysis. ## results and discussion the proposed classifiers have been implemented using scikit-learn(f.a.nazira and m.f.mridha 2021) and python, and the training and testing processes took place on the google collaboratory platform. google collaboratory provides a cloudbased jupyter notebook environment, eliminating the need for local setup. in our study, the coda lab lt-edi@ranlp 2023 dataset was utilized, specifically developed for detecting signs of depression from social media text. this dataset comprises social media messages in english. we trained various classifiers, including cnn and bert, using the extracted features from the training set. the performance of these classifiers was then evaluated on the test dataset. the combination of scikit-learn, python, and the lt-edi@ranlp 2023 dataset allowed us to detect signs of depression from social media text, contributing to the analysis and understanding of mental health indicators in online communication. ## conclusion and feature work in conclusion, this study successfully conducted experimental work to detect signs of depression from social media text using the provided dataset. 3-gram is employed as feature extraction technique to effectively capture textual information. different classifiers, including cnn, and bert, were compared and bert with 3gram has achieved a goo accuracy compared to cnn and thus proving its effectiveness in this task(vandana 2023). for future work, there are several potential areas of improvement. exploring alternative numerical or vectorial representations of the text, such as tf-idf, could potentially enhance classification performance @xcite . additionally, investigating new classifiers based on neural networks, which leverage advanced linguistic features, would be valuable. these approaches can contribute to further improving the detection of signs of depression in social media texts, enabling a deeper understanding of mental health indicators in online communication. further, the usage of a post encoder, a sentimentguided transformer and a supervised severityaware contrastive learning component may enhance the result and it can lead the classification method to a new level. unlike the proposed model the account of sentiment and semantic information of data can lead to greater accuracy. the post encoder mentalroberta and sentilare can be used to obtain the semantic as well as sentiment hidden features. our model for bagged the official rank of 12 and gave an f1 score of 0.401.