[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122275-en":3,"doc-seo-122275-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122275,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram","This study uses artificial intelligence, specifically machine learning, to examine the emotional impact generated by mental health disclosures on social media, focusing on Instagram influencer/celebrity content. A newly created, emotion-labeled corpus categorizes responses such as love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness. Machine learning models are trained to detect these emotions from the prior corpus, producing strong results: Random Forest achieves low computational load with moderate performance, while deep learning and BERT reach high accuracy levels.","Submitted 14 February 2024 Accepted 19 July 2024  \nPublished 19 September 2024 Corresponding author  \nNoemi Merayo, [noemer@tel.uva.es](noemer@tel.uva.es)[ ](noemer@tel.uva.es)Academic editor  \nXiangjie Kong  \nAdditional Information and Declarations can be found on page 21  \nDOI 10.7717/peerj-cs.2251  \nCopyright 2024 Merayo et al.  \nDistributed under  \nCreative Commons CC-BY 4.0  \nMachine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram  \nNoemi Merayo 1, Alba Ayuso-Lanchares2 and Clara GonzálezSanguino3  \n1 Signal Theory, Communications and Telematic Engineering Department, High School of Telecommunications Engineering, Universidad de Valladolid, Valladolid, Valladolid, Spain  \n2 Department of Pedagogy, Faculty of Medicine, Universidad de Valladolid, Valladolid, Valladolid, Spain  \n3 Department of Psychology, Education and Social Work Faculty, Universidad de Valladolid, Valladolid, Valladolid, Spain  \nABSTRACT  \nBackground: 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. Methods: This research involves creating a new corpus 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 corpus.  \nResults: 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 86–90%, 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 indifferent cultures and communities.  \nDiscussion: 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  \nHow to cite this article Merayo N, Ayuso-Lanchares A, González-Sanguino C. 2024. Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram. PeerJ Comput. Sci. 10:e2251 DOI 10.7717/peerj-cs.2251  \nhighly innovative and socially relevant given the importance of the phenomenon in societies. In fact, the proposed algorithms’ high accuracy (86–90%) 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","cbCaifoSj555Cv4e","https://ap.wps.com/l/cbCaifoSj555Cv4e","pdf",1525967,1,26,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Emotion-labeled corpus\n## Machine learning and emotion detection\n# Results\n# Discussion","[{\"question\":\"What is the main goal of this research on Instagram content?\",\"answer\":\"To examine, via artificial intelligence, the emotional impact generated by disclosures about mental health in Instagram content created by influencers/celebrities.\"},{\"question\":\"How are emotions represented in the study’s dataset?\",\"answer\":\"A new corpus is labeled with responses to mental health posts and categorized into emotions including love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness.\"},{\"question\":\"Which algorithms are evaluated, and what performance is reported?\",\"answer\":\"Random Forest, deep learning models, and BERT are used. BERT reaches about 86–90% accuracy, deep learning achieves around 72%, and Random Forest shows decent performance with lower computational load.\"}]","Machine learning and natural language processing to assess the emotional impact of influencers’ mental health content on Instagram | PDF",1785809779,66,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-natural-language-processing-to-assess-the-emotional-impact-of-influencers-mental-health-content-on-instagram","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-natural-language-processing-to-assess-the-emotional-impact-of-influencers-mental-health-content-on-instagram/122275/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this research on Instagram content?","Question",{"text":75,"@type":76},"To examine, via artificial intelligence, the emotional impact generated by disclosures about mental health in Instagram content created by influencers/celebrities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are emotions represented in the study’s dataset?",{"text":80,"@type":76},"A new corpus is labeled with responses to mental health posts and categorized into emotions including love/admiration, anger/contempt/mockery, gratitude, identification/empathy, and sadness.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithms are evaluated, and what performance is reported?",{"text":84,"@type":76},"Random Forest, deep learning models, and BERT are used. 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