Deep Learning for Depression Detection of Twitter Users (2024)

@inproceedings{husseini-orabi-etal-2018-deep, title = "Deep Learning for Depression Detection of {T}witter Users", author = "Husseini Orabi, Ahmed and Buddhitha, Prasadith and Husseini Orabi, Mahmoud and Inkpen, Diana", editor = "Loveys, Kate and Niederhoffer, Kate and Prud{'}hommeaux, Emily and Resnik, Rebecca and Resnik, Philip", booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic", month = jun, year = "2018", address = "New Orleans, LA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-0609", doi = "10.18653/v1/W18-0609", pages = "88--97", abstract = "Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people{'}s life. This close relationship between social media platforms and their users has made these platforms to reflect the users{'} personal life with different limitations. In such an environment, researchers are presented with a wealth of information regarding one{'}s life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.",}

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%0 Conference Proceedings%T Deep Learning for Depression Detection of Twitter Users%A Husseini Orabi, Ahmed%A Buddhitha, Prasadith%A Husseini Orabi, Mahmoud%A Inkpen, Diana%Y Loveys, Kate%Y Niederhoffer, Kate%Y Prud’hommeaux, Emily%Y Resnik, Rebecca%Y Resnik, Philip%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic%D 2018%8 June%I Association for Computational Linguistics%C New Orleans, LA%F husseini-orabi-etal-2018-deep%X Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people’s life. This close relationship between social media platforms and their users has made these platforms to reflect the users’ personal life with different limitations. In such an environment, researchers are presented with a wealth of information regarding one’s life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.%R 10.18653/v1/W18-0609%U https://aclanthology.org/W18-0609%U https://doi.org/10.18653/v1/W18-0609%P 88-97

Markdown (Informal)

[Deep Learning for Depression Detection of Twitter Users](https://aclanthology.org/W18-0609) (Husseini Orabi et al., CLPsych 2018)

• Deep Learning for Depression Detection of Twitter Users (Husseini Orabi et al., CLPsych 2018)

ACL

• Ahmed Husseini Orabi, Prasadith Buddhitha, Mahmoud Husseini Orabi, and Diana Inkpen. 2018. Deep Learning for Depression Detection of Twitter Users. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 88–97, New Orleans, LA. Association for Computational Linguistics.