Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study.

BACKGROUND: Understanding a student's depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data. OBJECTIVE: The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms. METHODS: Through a countrywide study, we collected 2952 students' raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features. RESULTS: After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days' app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication. CONCLUSIONS: Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage-based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms' capability to find subtle differences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51540.

A Bengali news and public opinion dataset from YouTube.

Along with the traditional news publishing policies, news agencies now share news over the internet since people nowadays prefer reading news online. Moreover, news media maintain YouTube channels to publish visual stories. Readers comment to share their opinions below the corresponding news item. These news and comments have been a great source of information and research. However, there is a lack of research in the Bengali news context. This article presents a dataset containing 7,62,678 public comments and replies from 16,016 video news published from 2017 to 2023 from a renowned Bengali news YouTube channel. The data withholds 15 properties of news that include video URL, title, likes, views, date of publishing, hashtags, description, comment author, comment time, comment, likes in the comment, reply author, reply time, reply, and likes in the responses. To ensure privacy, the commentator's name is encoded in the dataset. The dataset is open to use for researchers at https://data.mendeley.com/datasets/3c3j3bkxvn/4. A translated file for the raw dataset is also included. This data may help scholars to identify patterns in public opinion and analyze how public opinion changes over time.

A gradient boosting classifier for purchase intention prediction of online shoppers.

Early purchase prediction plays a vital role for an e-commerce website. It enables e-shoppers to enlist consumers for product suggestions, offer discount and for many other interventions. Several work has already been done using session log for analyzing customer behavior whether he performs a purchase on the product or not. In most cases, it is difficult to find out and make a list of customers and offer them discount when their session ends. In this paper, we propose a customer's purchase intention prediction model where e-shoppers can detect customer's purpose earlier. First, we apply feature selection technique to select best features. Then the extracted features are fed to train supervised learning models. Several classifiers like support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and XGBoost classifiers have been applied along with oversampling method for balancing the dataset. The experiments were performed on a standard benchmark dataset. Experimental results show that XGBoost classifier with feature selection techniques and oversampling method has the significantly higher area under ROC curve (auROC) score and are under precision-recall curve (auPR) score which are 0.937 and 0.754 respectively. On the other hand accuracy achieved by XGBoost and Decision tree are significantly improved and they are 90.65% and 90.54% respectively. Overall performance of the gradient boosting method is significantly improved compared to other classifiers and state-of-the-art methods. In addition to this, a method for explainable analysis on the problem was outlined.

Predicting Academic Performance: Analysis of Students' Mental Health Condition from Social Media Interactions.

Social media have become an indispensable part of peoples' daily lives. Research suggests that interactions on social media partly exhibit individuals' personality, sentiment, and behavior. In this study, we examine the association between students' mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students' psychological attributes and mental health issues will be predicted from their social media interactions. Then, students' academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students' Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users' social media usage and their psychological attributes and mental health status and (ii) users' psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students' Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.

BanglaSER: A speech emotion recognition dataset for the Bangla language.

The speech emotion recognition system determines a speaker's emotional state by analyzing his/her speech audio signal. It is an essential at the same time a challenging task in human-computer interaction systems and is one of the most demanding areas of research using artificial intelligence and deep machine learning architectures. Despite being the world's seventh most widely spoken language, Bangla is still classified as one of the low-resource languages for speech emotion recognition tasks because of inadequate availability of data. There is an apparent lack of speech emotion recognition dataset to perform this type of research in Bangla language. This article presents a Bangla language-based emotional speech-audio recognition dataset to address this problem. BanglaSER is a Bangla language-based speech emotion recognition dataset. It consists of speech-audio data of 34 participating speakers from diverse age groups between 19 and 47 years, with a balanced 17 male and 17 female nonprofessional participating actors. This dataset contains 1467 Bangla speech-audio recordings of five rudimentary human emotional states, namely angry, happy, neutral, sad, and surprise. Three trials are conducted for each emotional state. Hence, the total number of recordings involves 3 statements × 3 repetitions × 4 emotional states (angry, happy, sad, and surprise) × 34 participating speakers = 1224 recordings + 3 statements × 3 repetitions × 1 emotional state (neutral) × 27 participating speakers = 243 recordings, resulting in a total number of recordings of 1467. BanglaSER dataset is created by recording speech-audios through smartphones, and laptops, having a balanced number of recordings in each category with evenly distributed participating male and female actors, and would serve as an essential training dataset for the Bangla speech emotion recognition model in terms of generalization. BanglaSER is compatible with various deep learning architectures such as Convolutional neural networks, Long short-term memory, Gated recurrent unit, Transformer, etc. The dataset is available at https://data.mendeley.com/datasets/t9h6p943xy/5 and can be used for research purposes.