Depression detection through traditional approaches is a very time-consuming process relying on surveys and interviews. While social data contain valuable information that can be used to identify user’s psychological states. We have provided an automated approach to collect and evaluate tweets and present a novel framework to predict depression symptoms.

More details are available in our paper [1].

To have access to the full dataset, please contact me by email. [email protected]

@article{Safa_2021,
    doi = {10.1007/s11227-021-04040-8},
    url = {https://doi.org/10.1007%2Fs11227-021-04040-8},
    year = 2021,
    month = {sep},
    publisher = {Springer Science and Business Media {LLC}},
    author = {Ramin Safa and Peyman Bayat and Leila Moghtader},
    title = {Automatic detection of depression symptoms in twitter using multimodal analysis},
    journal = {The Journal of Supercomputing}
}

[1] Safa, R., Bayat, P. & Moghtader, L. Automatic detection of depression symptoms in twitter using multimodal analysis. J Supercomput (2021). https://doi.org/10.1007/s11227-021-04040-8