| 000 -LEADER |
|---|
| fixed length control field |
02320nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Liaquat, Umna |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Social Media Analytics for Mental Health Assessment / |
| Statement of responsibility, etc. |
Umna Liaquat |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Islamabad : |
| Name of producer, publisher, distributor, manufacturer |
SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
94p. |
| Other physical details |
Soft Copy |
| Dimensions |
30cm |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Social media has emerged as a tool for exploring the human psyche, offering exceptional<br/>access to real-time behavioral signals that are transforming the landscape of computational<br/>psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility,<br/>requiring robust modeling of emotion, behavior, and timing. Prior studies have largely<br/>focused on text-based sentiment analysis and linguistic features to classify mental health<br/>conditions; however, these approaches often neglect non-verbal markers such as circadian<br/>rhythms and affective variability. Existing models primarily depend on static textual cues,<br/>limiting their ability to capture the dynamic, multimodal nature of psychiatric expression.<br/>This study addresses these limitations by integrating temporal rhythms, emotional<br/>dynamics, and behavioral signals extracted from Reddit user histories to develop predictive<br/>models of bipolar disorder and high-risk psychological states. We propose a series of<br/>interpretable multimodal architectures employing classical machine learning (Logistic<br/>regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and<br/>transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal<br/>posting features, emotional entropy, and community-level interaction structures.<br/>Compared to benchmark studies, our models demonstrate significant improvements in both<br/>classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of<br/>fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader<br/>implications for public health by offering a foundation for real-time, ethically deployable<br/>digital mental health tools. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Supervisor : Dr. Nabeel Anwar |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="http://10.250.8.41:8080/xmlui/handle/123456789/57289">http://10.250.8.41:8080/xmlui/handle/123456789/57289</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Thesis |
