KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text

Agirrezabal, Manex; Janek, Amann,

doi:10.48550/arxiv.2204.04481

Cited by 1 publication

(1 citation statement)

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“…Lara et al [40] proposed the DeepBoSE model, which can extract the lexical sentiment information of user posts and utilize the attributes of deep learning models while retaining interpretability. Agirrezabal [41] et al integrated pre-trained BERT, RoBERTa, and XLNET models to extract text features and vote for depression detection. Kayalvizhi et al [42] proposed a standard dataset for depression detection in social media, detecting depression levels from user posts.…”

Section: A Depression Detection Of Social Media Users Based On Single...mentioning

confidence: 99%

TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection

Zhou

Liu

Shangguan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people's lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attentionbased multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model.

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Section: A Depression Detection Of Social Media Users Based On Single...mentioning

confidence: 99%