A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo

• URL: http://arxiv.org/abs/2008.11708v1
• Date: Wed, 26 Aug 2020 17:53:17 GMT
• Title: A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo
• Authors: Yiding Wang, Zhenyi Wang, Chenghao Li, Yilin Zhang, Haizhou Wang
• Abstract summary: We build a large dataset on Sina Weibo (a leading OSN with the largest number of active users in the Chinese community) By analyzing the user's text, social behavior, and posted pictures, ten statistical features are concluded and proposed. A novel deep neural network classification model, i.e. FusionNet, is proposed and simultaneously trained with the above-extracted features.
• Score: 6.899536164312357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly. The online social network (OSN) provides researchers with another perspective for detecting individuals suffering from depression. However, existing studies of depression detection based on machine learning still leave relatively low classification performance, suggesting that there is significant improvement potential for improvement in their feature engineering. In this paper, we manually build a large dataset on Sina Weibo (a leading OSN with the largest number of active users in the Chinese community), namely Weibo User Depression Detection Dataset (WU3D). It includes more than 20,000 normal users and more than 10,000 depressed users, both of which are manually labeled and rechecked by professionals. By analyzing the user's text, social behavior, and posted pictures, ten statistical features are concluded and proposed. In the meantime, text-based word features are extracted using the popular pretrained model XLNet. Moreover, a novel deep neural network classification model, i.e. FusionNet (FN), is proposed and simultaneously trained with the above-extracted features, which are seen as multiple classification tasks. The experimental results show that FusionNet achieves the highest F1-Score of 0.9772 on the test dataset. Compared to existing studies, our proposed method has better classification performance and robustness for unbalanced training samples. Our work also provides a new way to detect depression on other OSN platforms.

Related papers

• Open-World Semi-Supervised Learning for Node Classification [53.07866559269709]
  Open-world semi-supervised learning (Open-world SSL) for node classification is a practical but under-explored problem in the graph community. We propose an IMbalance-Aware method named OpenIMA for Open-world semi-supervised node classification.
  arXiv  Detail & Related papers  (2024-03-18T05:12:54Z)
• A Framework for Identifying Depression on Social Media: MentalRiskES@IberLEF 2023 [0.979963710164115]
  This paper describes our participation in the MentalRiskES task at IberLEF 2023. The task involved predicting the likelihood of an individual experiencing depression based on their social media activity. The dataset consisted of conversations from 175 Telegram users, each labeled according to their evidence of suffering from the disorder.
  arXiv  Detail & Related papers  (2023-06-28T11:53:07Z)
• Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking [66.83273589348758]
  Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt to make use of graph neural networks (GNNs) for this task. New and diverse datasets have also been created to better evaluate the effectiveness of these new models.
  arXiv  Detail & Related papers  (2023-06-18T01:58:59Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN [18.32536789799511]
  Depression is a widespread mental health issue, affecting an estimated 3.8% of the global population. We propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts. The model achieved an accuracy of 0.86 and an F1 score of 0.86 and outperformed the state-of-the-art documented result (F1 score of 0.79) by other machine learning models in the literature.
  arXiv  Detail & Related papers  (2023-02-03T06:22:18Z)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• Neurochaos Feature Transformation and Classification for Imbalanced Learning [0.0]
  Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Inspired by the chaotic neuronal firing in the human brain, a novel learning algorithm namely Neurochaos Learning (NL) was recently proposed. We propose a unique combination of neurochaos based feature transformation and extraction with traditional ML algorithms.
  arXiv  Detail & Related papers  (2022-04-20T16:11:45Z)
• Novelty Detection via Contrastive Learning with Negative Data Augmentation [34.39521195691397]
  We introduce a novel generative network framework for novelty detection. Our model has significant superiority over cutting-edge novelty detectors. Our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
  arXiv  Detail & Related papers  (2021-06-18T07:26:15Z)
• DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media [12.820775223409857]
  Twitter is a popular online social media platform which allows users to share their user-generated content. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns.
  arXiv  Detail & Related papers  (2021-05-23T08:05:53Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.