Cross-Domain Depression Detection via Harvesting Social Media
Authors: Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, Thanassis Tiropanis, Tat-Seng Chua, Wendy Hall
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3.4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China Key Laboratory of Pervasive Computing, Ministry of Education Beijing National Research Center for Information Science and Technology 2School of Computing, National University of Singapore 3Electronics and Computer Science, University of Southampton |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | Yes | We construct DT based on 400 million crawled tweets from 2009.10 to 2012.10. Inspired by Shen et al. [2017], users are identified as depressed when self-reported sentence pattern I m diagnosed with depression is matched. ... A Twitter dataset constructed by Shen et al. [2017] is employed. |
| Dataset Splits | Yes | We take 280 samples (approximately 10% the size of DS) in DT as DTL and the remaining as DT U for testing. ... To recognize the divergent features, dataset DTL is employed for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For results reported in Table 3 and 4, we use a sigmoid activation function for DNN-FATC and the hyper-parameters are set as K = 100, W = 50, q1 = q2 = 25, l = 0.5, σ = 0.01, d = 4, δ = 2 for optimization after careful tuning. |