Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAutomatic Detection and Classification of Mental Illnesses from General Social Media Texts
الكشف التلقائي وتصنيف الأمراض العقلية من نصوص وسائل التواصل الاجتماعي العام
350
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تصبح الصحة العقلية أكثر اهتماما مؤخرا مؤخرا، والاكتئاب كونه مرض شائع جدا في الوقت الحاضر، ولكن أيضا اضطرابات أخرى مثل القلق أو الاضطرابات القهرية الهوس أو اضطرابات التغذية أو اضطرابات نقص الانتباه / اضطرابات نقص الانتباه / فرط النشاط. توفر كمية كبيرة من البيانات من وسائل التواصل الاجتماعي والسلف الحديث لنماذج التعلم العميق وسيلة قيمة للكشف عن الاضطرابات النفسية تلقائيا من نص عادي. في هذه المقالة، نقوم بتجربة أساليب حديثة في مجموعة بيانات الصحة العقلية SMHD من Reddit (كوهان وآخرون، 2018). مساهمتنا ثلاثة أضعاف: استخدام مجموعة بيانات تتكون من المزيد من الأمراض أكثر من معظم الدراسات، مع التركيز على النص العام بدلا من مجموعات دعم الصحة العقلية والتصنيف من قبل الوظائف بدلا من الأفراد أو المجموعات. بالنسبة للتصنيف التلقائي للأمراض، فإننا نوظف ثلاث نماذج تعليمية عميقة: بيرت روبرتا و XLNet. نحن مضاعفة خط الأساس الذي أنشأه كوهان وآخرون. (2018)، على عينة فقط من مجموعة البيانات الخاصة بهم. نحن نحسن النتائج التي حصلت عليها جيانغ وآخرون. (2020) على تصنيف ما بعد المستوى. إن الدقة التي حصلت عليها مصنف اضطراب الأكل هو أعلى نظرا للوجود الحامل للمناقشات المتعلقة بالسعرات الحرارية والوجبات الغذائية والوصفات وما إلى ذلك، في حين أن الاكتئاب كان لديه أدنى درجة F1، ربما لأن الاكتئاب أكثر صعوبة في تحديد الأفعال اللغوية.
Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. (2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts.
References used
https://aclanthology.org/
rate research
Read More
Given the current social distancing regulations across the world, social media has become the primary mode of communication for most people. This has isolated millions suffering from mental illnesses who are unable to receive assistance in person. Th
ey have increasingly turned to online platforms to express themselves and to look for guidance in dealing with their illnesses. Keeping this in mind, we propose a solution to classify mental illness posts on social media thereby enabling users to seek appropriate help. In this work, we classify five prominent kinds of mental illnesses- depression, anxiety, bipolar disorder, ADHD and PTSD by analyzing unstructured user data on Reddit. In addition, we share a new high-quality dataset1 to drive research on this topic. The dataset consists of the title and post texts from 17159 posts and 13 subreddits each associated with one of the five mental illnesses listed above or a None class indicating the absence of any mental illness. Our model is trained on Reddit data but is easily extensible to other social media platforms as well as demonstrated in our results.We believe that our work is the first multi-class model that uses a Transformer based architecture such as RoBERTa to analyze people's emotions and psychology. We also demonstrate how we stress test our model using behavioral testing. Our dataset is publicly available and we encourage researchers to utilize this to advance research in this arena. We hope that this work contributes to the public health system by automating some of the detection process and alerting relevant authorities about users that need immediate help.
Nowadays, there are a lot of advertisements hiding as normal posts or experience sharing in social media. There is little research of advertorial detection on Mandarin Chinese texts. This paper thus aimed to focus on hidden advertorial detection of o
nline posts in Taiwan Mandarin Chinese. We inspected seven contextual features based on linguistic theories in discourse level. These features can be further grouped into three schemas under the general advertorial writing structure. We further implemented these features to train a multi-task BERT model to detect advertorials. The results suggested that specific linguistic features would help extract advertorials.
The wide reach of social media platforms, such as Twitter, have enabled many users to share their thoughts, opinions and emotions on various topics online. The ability to detect these emotions automatically would allow social scientists, as well as,
businesses to better understand responses from nations and costumers. In this study we introduce a dataset of 30,000 Persian Tweets labeled with Ekman's six basic emotions (Anger, Fear, Happiness, Sadness, Hatred, and Wonder). This is the first publicly available emotion dataset in the Persian language. In this paper, we explain the data collection and labeling scheme used for the creation of this dataset. We also analyze the created dataset, showing the different features and characteristics of the data. Among other things, we investigate co-occurrence of different emotions in the dataset, and the relationship between sentiment and emotion of textual instances. The dataset is publicly available at https://github.com/nazaninsbr/Persian-Emotion-Detection.
Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions makes sarcasm detection very dif
ficult. In this work, we focus on detecting sarcasm in textual conversations, written in English, from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. We show the effectiveness and interpretability of our approach by achieving state-of-the-art results on datasets from social networking platforms, online discussion forums, and political dialogues.
Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream t
asks. In this article, we investigate whether temporal adaptation can bring additional benefits. For this purpose, we introduce a corpus of social media comments sampled over three years. It contains unlabelled data for adaptation and evaluation on an upstream masked language modelling task as well as labelled data for fine-tuning and evaluation on a downstream document classification task. We find that temporality matters for both tasks: temporal adaptation improves upstream and temporal fine-tuning downstream task performance. Time-specific models generally perform better on past than on future test sets, which matches evidence on the bursty usage of topical words. However, adapting BERT to time and domain does not improve performance on the downstream task over only adapting to domain. Token-level analysis shows that temporal adaptation captures event-driven changes in language use in the downstream task, but not those changes that are actually relevant to task performance. Based on our findings, we discuss when temporal adaptation may be more effective.
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
