Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution
Authors: Guangyao Shen, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, Wenwu Zhu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Tsinghua University; TNList 2 Department of Computer Science and Technology, Shandong University 3 School of Computing, National University of Singapore 4School of Information and Communication Engineering, Beijing University of Posts and Telecommunications |
| Pseudocode | No | The paper provides mathematical formulations and descriptions of algorithms in text, but no explicit pseudocode blocks or algorithm figures are present. |
| Open Source Code | No | The paper states: 'In addition, we release these datasets3 with features to facilitate wellness study for computer science and psychology.' with footnote '3http://depressiondetection.droppages.com/.' While datasets and features are mentioned as released, there is no explicit statement or link confirming the release of the source code for the methodology described in the paper. |
| Open Datasets | Yes | We construct benchmark datasets for online depression detection and analysis, including the well-labeled depression and non-depression datasets as well as a large-scale depression-candidate dataset. In addition, we release these datasets3 with features to facilitate wellness study for computer science and psychology. 3http://depressiondetection.droppages.com/. |
| Dataset Splits | Yes | We trained and tested these methods under 5-fold cross validation, with over 10 randomized experimental runs. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software tools and libraries like Porter Stemmer, word2vec, NLTK toolbox, LIWC, LDA model, and Naive Bayesian (Pedregosa et al., 2011 refers to scikit-learn), but it does not specify version numbers for these software components, which are required for reproducible descriptions. |
| Experiment Setup | Yes | There are three key parameters in the MDL: two regularization parameters, λ in Eqn.(3) and p in Eqn.(5), as well as an implicit parameter D. The search range for λ, p, and D are [0.001, 0.04], [10 5, 10 1], and [50, 200], respectively. ... We finally observed that MDL reached the optimal performance when λ = 0.007, p = 10 2.5, and D = 130. |