NarrationDep: Narratives on Social Media For Automatic Depression Detection

• URL: http://arxiv.org/abs/2407.17174v1
• Date: Wed, 24 Jul 2024 11:24:25 GMT
• Title: NarrationDep: Narratives on Social Media For Automatic Depression Detection
• Authors: Hamad Zogan, Imran Razzak, Shoaib Jameel, Guandong Xu,
• Abstract summary: We have developed a novel model called textttNarrationDep, which focuses on detecting narratives associated with depression. textttNarrationDep is a deep learning framework that jointly models individual user tweet representations and clusters of users' tweets.
• Score: 24.11420537250414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media posts provide valuable insight into the narrative of users and their intentions, including providing an opportunity to automatically model whether a social media user is depressed or not. The challenge lies in faithfully modelling user narratives from their online social media posts, which could potentially be useful in several different applications. We have developed a novel and effective model called \texttt{NarrationDep}, which focuses on detecting narratives associated with depression. By analyzing a user's tweets, \texttt{NarrationDep} accurately identifies crucial narratives. \texttt{NarrationDep} is a deep learning framework that jointly models individual user tweet representations and clusters of users' tweets. As a result, \texttt{NarrationDep} is characterized by a novel two-layer deep learning model: the first layer models using social media text posts, and the second layer learns semantic representations of tweets associated with a cluster. To faithfully model these cluster representations, the second layer incorporates a novel component that hierarchically learns from users' posts. The results demonstrate that our framework outperforms other comparative models including recently developed models on a variety of datasets.

Related papers

• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks [9.350629400940493]
  We crawl a large-scale dataset of the 2020 US presidential election and automatically label all users by manually tagged hashtags. We propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks.
  arXiv  Detail & Related papers  (2023-01-20T19:20:10Z)
• Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters [66.17039929803933]
  We propose a novel transfer learning framework which updates only $0.3%$ of model parameters to learn style specific attributes for response generation. We learn style specific attributes from the PERSONALITY-CAPTIONS dataset.
  arXiv  Detail & Related papers  (2022-10-07T00:09:22Z)
• Improved Topic modeling in Twitter through Community Pooling [0.0]
  Twitter posts are short and often less coherent than other text documents. We propose a new pooling scheme for topic modeling in Twitter, which groups tweets whose authors belong to the same community. Results show that our Community polling method outperformed other methods on the majority of metrics in two heterogeneous datasets.
  arXiv  Detail & Related papers  (2021-12-20T17:05:32Z)
• Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets [56.018551958004814]
  This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources. Large-scale datasets with noisy image-text pairs provide a sub-optimal source of supervision. We propose to leverage and separate semantics and descriptive style through the incorporation of a style token and keywords extracted through a retrieval component.
  arXiv  Detail & Related papers  (2021-11-24T19:00:05Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• 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)
• Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model [29.89122455417348]
  This paper suggests a joint embedding model, incorporating users' social and textual information to learn contextualized user representations. We apply our model to tweets related to two lifestyle activities, Yoga' and Keto diet' and use it to analyze users' activity type and motivation.
  arXiv  Detail & Related papers  (2021-04-07T15:29:36Z)
• Cue Me In: Content-Inducing Approaches to Interactive Story Generation [74.09575609958743]
  We focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories.
  arXiv  Detail & Related papers  (2020-10-20T00:36:15Z)

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.