T. T. Kulatilake, P. L. R. S. Liyanage, G. H. K. Deemud, U. S. C. D. Silva, Disni Sriyaratna, Archchana Kugathasan
{"title":"PRODEP: Smart Social Media Procrastination and Depression Tracker","authors":"T. T. Kulatilake, P. L. R. S. Liyanage, G. H. K. Deemud, U. S. C. D. Silva, Disni Sriyaratna, Archchana Kugathasan","doi":"10.1109/SMAP56125.2022.9941896","DOIUrl":null,"url":null,"abstract":"Procrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic underachievement [47]. It is viewed within physiological research as a self-regulation failure [48]. Similar to procrastination, another severe problem which comes up within lots of people including students and teenagers is “Depression”. Depression is a massively widespread problem among people around the world as well as in Sri Lanka [49]. As a result of procrastination and depression, students has to face academic underachievement. One of the main cause of these widespread problems are Social media over-usage [50]. Therefore this paper presents a new tracker which presented as a mobile application with four main components. This research study is about identifying and tracking users’ facial emotions and eye-aspect ratio to analyze real emotions of the user via device inbuilt webcam to identify user fatigueness and procrastination. This study also analyzes user behavior in two selected social media platforms which are Facebook and Twitter and identifies the negativity and depressiveness of “Sinhala” content using Machine learning based Sentiment analysis approaches. Also as a companion, this paper introduces a chat-bot which communicates with the user in “Singlish” language. Our final products will be a complete mobile application which generates reports to the user based on the analysis done in the four components. As future work we will introduce AutoML approaches instead of traditional machine learning based approaches.","PeriodicalId":432172,"journal":{"name":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","volume":"23 23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMAP56125.2022.9941896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Procrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic underachievement [47]. It is viewed within physiological research as a self-regulation failure [48]. Similar to procrastination, another severe problem which comes up within lots of people including students and teenagers is “Depression”. Depression is a massively widespread problem among people around the world as well as in Sri Lanka [49]. As a result of procrastination and depression, students has to face academic underachievement. One of the main cause of these widespread problems are Social media over-usage [50]. Therefore this paper presents a new tracker which presented as a mobile application with four main components. This research study is about identifying and tracking users’ facial emotions and eye-aspect ratio to analyze real emotions of the user via device inbuilt webcam to identify user fatigueness and procrastination. This study also analyzes user behavior in two selected social media platforms which are Facebook and Twitter and identifies the negativity and depressiveness of “Sinhala” content using Machine learning based Sentiment analysis approaches. Also as a companion, this paper introduces a chat-bot which communicates with the user in “Singlish” language. Our final products will be a complete mobile application which generates reports to the user based on the analysis done in the four components. As future work we will introduce AutoML approaches instead of traditional machine learning based approaches.