{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T06:00:30Z","timestamp":1787032830811,"version":"build-2736575974"},"reference-count":79,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study aims to examine, through artificial intelligence, specifically machine learning, the emotional impact generated by disclosures about mental health on social media. In contrast to previous research, which primarily focused on identifying psychopathologies, our study investigates the emotional response to mental health-related content on Instagram, particularly content created by influencers\/celebrities. This platform, especially favored by the youth, is the stage where these influencers exert significant social impact, and where their analysis holds strong relevance. Analyzing mental health with machine learning techniques on Instagram is unprecedented, as all existing research has primarily focused on Twitter.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This research involves creating a new\n                      <jats:italic>corpus<\/jats:italic>\n                      labelled with responses to mental health posts made by influencers\/celebrities on Instagram, categorized by emotions such as love\/admiration, anger\/contempt\/mockery, gratitude, identification\/empathy, and sadness. The study is complemented by modelling a set of machine learning algorithms to efficiently detect the emotions arising when faced with these mental health disclosures on Instagram, using the previous\n                      <jats:italic>corpus<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results have shown that machine learning algorithms can effectively detect such emotional responses. Traditional techniques, such as Random Forest, showed decent performance with low computational loads (around 50%), while deep learning and Bidirectional Encoder Representation from Transformers (BERT) algorithms achieved very good results. In particular, the BERT models reached accuracy levels between 86\u201390%, and the deep learning model achieved 72% accuracy. These results are satisfactory, considering that predicting emotions, especially in social networks, is challenging due to factors such as the subjectivity of emotion interpretation, the variability of emotions between individuals, and the interpretation of emotions in different cultures and communities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This cross-cutting research between mental health and artificial intelligence allows us to understand the emotional impact generated by mental health content on social networks, especially content generated by influential celebrities among young people. The application of machine learning allows us to understand the emotional reactions of society to messages related to mental health, which is highly innovative and socially relevant given the importance of the phenomenon in societies. In fact, the proposed algorithms\u2019 high accuracy (86\u201390%) in social contexts like mental health, where detecting negative emotions is crucial, presents a promising research avenue. Achieving such levels of accuracy is highly valuable due to the significant implications of false positives or false negatives in this social context.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj-cs.2251","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T03:37:02Z","timestamp":1726717022000},"page":"e2251","source":"Crossref","is-referenced-by-count":13,"title":["Machine learning and natural language processing to assess the emotional impact of influencers\u2019 mental health content on Instagram"],"prefix":"10.7717","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6920-0778","authenticated-orcid":true,"given":"Noemi","family":"Merayo","sequence":"first","affiliation":[{"name":"Signal Theory, Communications and Telematic Engineering Department, High School of Telecommunications Engineering, Universidad de Valladolid, Valladolid, Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alba","family":"Ayuso-Lanchares","sequence":"additional","affiliation":[{"name":"Department of Pedagogy, Faculty of Medicine, Universidad de Valladolid, Valladolid, Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clara","family":"Gonz\u00e1lez-Sanguino","sequence":"additional","affiliation":[{"name":"Department of Psychology, Education and Social Work Faculty, Universidad de Valladolid, Valladolid, Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"10.7717\/peerj-cs.2251\/ref-1","first-page":"265","article-title":"{TensorFlow}: a system for {Large-Scale} machine learning","author":"Abadi","year":"2016"},{"issue":"4","key":"10.7717\/peerj-cs.2251\/ref-2","doi-asserted-by":"publisher","first-page":"100066","DOI":"10.1016\/j.cmpbup.2022.100066","article-title":"Machine learning 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