{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:04:36Z","timestamp":1760609076341,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T00:00:00Z","timestamp":1525219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The present work is a study of the detection of negative emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade a user having an affective state considered negative by an Analyzer can affect other users and generate bad repercussions. Those Analyzers that we propose are a Sentiment Analyzer, a Stress Analyzer and a novel combined Analyzer. We also want to discover what Analyzer is more suitable to predict a bad future situation, and in what context. We designed a Multi-Agent System (MAS) that uses different Analyzers to protect or advise users. This MAS uses the trained and tested Analyzers to predict future bad situations in social media, which could be triggered by the actions of a user that has an emotional state considered negative. We conducted an experimentation with different datasets of text messages from Twitter.com to examine the ability of the system to predict bad repercussions, by comparing the polarity, stress level or combined value classification of the messages that are replies to the ones of the messages that originated them.<\/jats:p>","DOI":"10.3390\/info9050107","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T03:20:27Z","timestamp":1525317627000},"page":"107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis"],"prefix":"10.3390","volume":"9","author":[{"given":"Guillem","family":"Aguado","sequence":"first","affiliation":[{"name":"Departamento de Sistemas Inform\u00e1ticos y Computaci\u00f3n, Universidad Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vicente","family":"Julian","sequence":"additional","affiliation":[{"name":"Departamento de Sistemas Inform\u00e1ticos y Computaci\u00f3n, Universidad Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4482-8793","authenticated-orcid":false,"given":"Ana","family":"Garcia-Fornes","sequence":"additional","affiliation":[{"name":"Departamento de Sistemas Inform\u00e1ticos y Computaci\u00f3n, Universidad Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11423-015-9415-4","article-title":"Developing educational materials about risks on social network sites: A design based research approach","volume":"64","author":"Vanderhoven","year":"2016","journal-title":"Educ. Technol. Res. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3916\/C43-2014-12","article-title":"Educating Teens about the Risks on Social Network Sites. An intervention study in Secondary Education","volume":"22","author":"Vanderhoven","year":"2014","journal-title":"Comunicar"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1177\/0743558411432635","article-title":"Risky Disclosures on Facebook: The Effect of Having a Bad Experience on Online Behavior","volume":"27","author":"Christofides","year":"2012","journal-title":"J. Adolesc. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.obhdp.2016.06.004","article-title":"Affect, emotion, and decision-making","volume":"136","author":"George","year":"2016","journal-title":"Organ. Behav. Hum. Dec. Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.jbtep.2016.08.012","article-title":"Decision making, cognitive distortions and emotional distress: A comparison between pathological gamblers and healthy controls","volume":"54","author":"Ciccarelli","year":"2017","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ipm.2016.06.009","article-title":"TensiStrength: Stress and relaxation magnitude detection for social media texts","volume":"53","author":"Thelwall","year":"2017","journal-title":"Inf. Process. Manag."},{"key":"ref_7","first-page":"1","article-title":"Sentiment Analysis and Opinion Mining","volume":"5","author":"Liu","year":"2012","journal-title":"Ser. Synth. Lect. Hum. Lang. 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