Reddit machine learning

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Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. The results of each method have been discussed. The results achieved a top F1 score of 0.

Reddit machine learning

Federal government websites often end in. The site is secure. Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critical for suicide prevention. However, there are challenges associated with conventional suicide-risk screening methods. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between and The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection.

Procedia Comput. Mehrpooya A. Chancellor S.

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Whether you're a beginner or veteran in machine learning and data science, you might be interested in a place to ask questions, share projects, or join discussions on the latest developments. There are many great communities out there for this, but it can be difficult to choose which one and some may no longer be active or well-maintained. Reddit is a powerhouse for many active forums dedicated to all areas across AI, machine learning, and data science. It's a welcoming community for sharing beginner questions, projects, and resources they also have a Discord server. It's more heavily moderated than the other subreddits, but you'll be sure to find all the latest important news, research papers, and discussions here you might even bump into industry veterans like hardmaru.

Reddit machine learning

It uses a forum format for communication. The subreddit to discuss all things Machine Learning! Machine Learning. This community does not set up pairing between its members. This community does not have discounts and perks for its members. Open main menu. Community Overview.

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In this section, the findings of the review are analyzed and synthesized to provide the answers to the posed research questions, which were defined to uncover the methodology used in the domain. This means these models will only be able to classify a data point to a label if every feature already exists in the training dataset. Using training data samples, called support vectors, the algorithm constructs an optimal hyperplane that separates samples into two classes. Without known outcomes, it becomes challenging to assess the clinical validity of models built with Reddit data [ 38 ]. The highest F1 score achieved for the LR model is 0. The researchers provided the CrowdFlower platform workers with the annotation instructions. Federal government websites often end in. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. Sanh, V. It allows researchers to fine tune the pre-trained models with small, task-specific training datasets. Search Search by keyword or author Search. Iserman et al. Different datasets with emotions can be explored for sentiment analysis, and the combination of both text and images that form multimodal sentiment analysis can also be explored. Correspondence to Shilpa Gite or Biswajeet Pradhan. Some crucial algorithms used for these research topics are Word2vec, GloVe encodings , and tf-idf vectors for preprocessing of text data.

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Vendor Voice Vendor Voice. Review and manage your consent Here's an overview of our use of cookies, similar technologies and how to manage them. The authors hope that this study provides a foundation for future research exploring neural network-based models and pre-trained language models for mental stress analysis. The goal is to create a tool that would automatically and instantaneously detect if a user is exhibiting any signs of suicidality based on their online activity before engagement with providers. In addition to requiring more computational resources, the high dimensionality can lead to poor performance of the model because it might fail to find important signifying patterns in the data [ 55 ]. For suicidality detection task, true positive TP represents the number of posts that were correctly classified as suicidal. Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: the role of stress associated with neglect and negative reactions on social media. Peters, M. Other examples of suicide risk factors include mental disorder, physical illness, substance abuse, domestic violence, bullying, relationship problems, and other stressful life events. Having an accurate dataset with labeled examples is critical for the success of the ML model. Sections 4 and 5 present and discuss the results achieved by the authors. Ji, S.