Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Dataset for Research on Modelling Depression Severity in Online Forum Data
مجموعة بيانات للبحث عن خطورة الاكتئاب النمذجة في بيانات المنتدى عبر الإنترنت
541
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يستخدم الأشخاص من المنتديات عبر الإنترنت إما أن نبحث عن معلومات أو للمساهمة به. بسبب شعبيتها المتنامية، تم إنشاء بعض المنتديات عبر الإنترنت خصيصا لتوفير الدعم والمساعدة والآراء للأشخاص الذين يعانون من مرض عقلي. الاكتئاب هو واحد من الأمراض النفسية الأكثر شيوعا في جميع أنحاء العالم. يتواصل الأشخاص المزيد من المنتديات عبر الإنترنت للعثور على إجابات لأمراضهم النفسية. ومع ذلك، لا توجد آلية لقياس شدة الاكتئاب في كل وظيفة وإعطاء أهمية أعلى لأولئك الذين يشخصون بالاكتئاب بشدة. على الرغم من أن العديد من الأبحاث التي تستند إلى بيانات المنتدى عبر الإنترنت وتحديد الاكتئاب قد أجريت، نادرا ما يتم استكشاف شدة الاكتئاب. بالإضافة إلى ذلك، فإن غياب مجموعات البيانات سوف تنموي تطوير إجراءات تشخيصية جديدة للممارسين. من هذه الدراسة، نقدم مجموعة بيانات لدعم البحوث حول تقييم شدة الاكتئاب. النهج الحسابي لقياس عملية تلقائية، خطورة محددة من الاكتئاب هنا نهج جديد تماما. ومع ذلك، هناك حاجة إلى ذلك، هناك حاجة إلى هذا القياس الموضعي للاكتئاب في مشاركات المنتدى عبر الإنترنت لضمان موازين القياس المستخدمة في بحثنا يجتمع مع القواعد المتوقعة للبحث العلمي.
People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.
References used
https://aclanthology.org/
rate research
Read More
In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabiliti
es of various forms of bullying and offensive content, including insult, trolling, profanity, sarcasm, threat, porn and exclusion. We recruited a pool of 17 annotators to perform fine-grained annotation on the dataset with each tweet annotated by three annotators. All our annotators are high school educated and frequent users of social media. Inter-rater agreement for the dataset as measured by Krippendorff's Alpha is 0.67. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results.
Online abuse and offensive language on social media have become widespread problems in today's digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of ta
rgeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.
This study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. The dataset comprises dialog triplets from the Wikipedia Talk pages, and edit
ing actions on the corresponding articles being discussed. We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor. However, they do not predict editorial behavior. On the other hand, feedback invoking evidentiality and criticism, and references to Wikipedia's community norms, is more likely to persuade the contributor to perform edits but is less likely to lead to a positive emotion. We developed baseline classifiers trained on pre-trained RoBERTa features that can predict editorial change with an F1 score of .54, as compared to an F1 score of .66 for predicting emotional change. A diagnostic analysis of persisting errors is also provided. We conclude with possible applications and recommendations for future work. The dataset is publicly available for the research community at https://github.com/kj2013/WikiTalkEdit/.
Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents. Annotating CDCR data is an arduous and expensive process, explaining why existing corpor
a are small and lack domain coverage. To overcome this bottleneck, we automatically extract event coreference data from hyperlinks in online news: When referring to a significant real-world event, writers often add a hyperlink to another article covering this event. We demonstrate that collecting hyperlinks which point to the same article(s) produces extensive and high-quality CDCR data and create a corpus of 2M documents and 2.7M silver-standard event mentions called HyperCoref. We evaluate a state-of-the-art system on three CDCR corpora and find that models trained on small subsets of HyperCoref are highly competitive, with performance similar to models trained on gold-standard data. With our work, we free CDCR research from depending on costly human-annotated training data and open up possibilities for research beyond English CDCR, as our data extraction approach can be easily adapted to other languages.
Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems. As a result, they do not contain any of
the richness and variety of natural-occurring utterances, where humans ask about data they need or are curious about. In this work, we release SEDE, a dataset with 12,023 pairs of utterances and SQL queries collected from real usage on the Stack Exchange website. We show that these pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset, propose an evaluation metric based on comparison of partial query clauses that is more suitable for real-world queries, and conduct experiments with strong baselines, showing a large gap between the performance on SEDE compared to other common datasets.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
