A method for analysis of human temperament in contrast to social network data

Lara Mondini Martins, Cássio De Alcantara, M. Barioni, Luiz Carlos De Oliveira Júnior, E. Faria
{"title":"A method for analysis of human temperament in contrast to social network data","authors":"Lara Mondini Martins, Cássio De Alcantara, M. Barioni, Luiz Carlos De Oliveira Júnior, E. Faria","doi":"10.1145/3539637.3556994","DOIUrl":null,"url":null,"abstract":"Currently, with the growth of the use of social networks, the possibilities of studies on social relationships and interactions have grown significantly. Understanding how users express their feelings and manifest their temperaments in social networks can be a step towards anticipating psychological disorders. Instagram has billions of users and is among the most used social networks today. However, it is still little explored as a source of study for human temperament. This work aims to analyze the relationships between users’ temperament and their data collected from the social network Instagram. For the analysis of textual data, two sentiment classification strategies are proposed. The sentiment classification results were satisfactory, with accuracy above 80% in three different databases. In order to analyze the relationship between the temperaments and social network data, statistical tests are used. Each user is represented by their positive and negative captions, the use of emojis in their posts, and the number of likes in their posts. Users of the same temperament are contrasted with users of other temperaments. The results indicate that depressed users post more captions with positive sentiment than hyperthymic, angry and worried users. Anxious users have more likes than depressed, hyperthymic, angry and worried users, and finally, anxious users use more emojis in Instagram captions than depressed and angry users.","PeriodicalId":350776,"journal":{"name":"Proceedings of the Brazilian Symposium on Multimedia and the Web","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Brazilian Symposium on Multimedia and the Web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3539637.3556994","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

Currently, with the growth of the use of social networks, the possibilities of studies on social relationships and interactions have grown significantly. Understanding how users express their feelings and manifest their temperaments in social networks can be a step towards anticipating psychological disorders. Instagram has billions of users and is among the most used social networks today. However, it is still little explored as a source of study for human temperament. This work aims to analyze the relationships between users’ temperament and their data collected from the social network Instagram. For the analysis of textual data, two sentiment classification strategies are proposed. The sentiment classification results were satisfactory, with accuracy above 80% in three different databases. In order to analyze the relationship between the temperaments and social network data, statistical tests are used. Each user is represented by their positive and negative captions, the use of emojis in their posts, and the number of likes in their posts. Users of the same temperament are contrasted with users of other temperaments. The results indicate that depressed users post more captions with positive sentiment than hyperthymic, angry and worried users. Anxious users have more likes than depressed, hyperthymic, angry and worried users, and finally, anxious users use more emojis in Instagram captions than depressed and angry users.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种对比社会网络数据分析人类气质的方法
目前,随着社交网络使用的增长,研究社会关系和互动的可能性显著增加。了解用户如何在社交网络中表达他们的情感和表现他们的气质,可以成为预测心理障碍的一步。Instagram拥有数十亿用户,是当今使用最多的社交网络之一。然而,作为人类气质的研究来源,它仍然很少被探索。这项工作旨在分析用户气质与社交网络Instagram上收集的数据之间的关系。对于文本数据的分析,提出了两种情感分类策略。在三个不同的数据库中,情感分类结果令人满意,准确率在80%以上。为了分析气质与社会网络数据之间的关系,使用了统计检验。每个用户都通过他们的积极和消极的标题,在他们的帖子中使用表情符号,以及在他们的帖子中喜欢的数量来代表。具有相同气质的用户与其他气质的用户形成对比。结果表明,抑郁的用户比情绪亢进、愤怒和担忧的用户发布了更多的积极情绪的标题。焦虑的用户比抑郁、情绪亢进、愤怒和担忧的用户点赞次数更多,最后,焦虑的用户比抑郁和愤怒的用户在Instagram配文中使用更多的表情符号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evaluating Topic Modeling Pre-processing Pipelines for Portuguese Texts A Proposal to Apply SignWriting in IMSC1 Standard for the Next-Generation of Brazilian DTV Broadcasting System Once Learning for Looking and Identifying Based on YOLO-v5 Object Detection I can’t pay! Accessibility analysis of mobile banking apps Should We Translate? Evaluating Toxicity in Online Comments when Translating from Portuguese to English
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1