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Sentiment and emotion findings distinguished victim- and perpetrator-related responses and showed shared expressions such as caring, love, and gratitude.",{"@graph":14,"@context":77},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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data source and sample size were used to analyze public attitudes?","Question",{"text":63,"@type":64},"The study used comments from 21 victim YouTube videos, 8 perpetrator videos, and 19 general family financial abuse videos, totaling 3,796 comments.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How did sentiment analysis differ across victim, perpetrator, and general cases?",{"text":68,"@type":64},"Negative comments (about 40%) were more associated with the perpetrator, while positive comments (nearly 60%) were more associated with the victim.",{"name":70,"@type":61,"acceptedAnswer":71},"What key emotions were unique to perpetrator or victim cases?",{"text":72,"@type":64},"Disapproval, surprise, disgust, and confusion appeared only for the perpetrator, while anger and fear appeared only for the victim.",{"name":74,"@type":61,"acceptedAnswer":75},"What additional insights did human-coded analysis provide beyond NLP?",{"text":76,"@type":64},"The human-coded subsample identified sarcasm and microaggressions targeting the perpetrator’s race and gender, which NLP did not capture for background-related victim-perpetrator attitudes.","https://schema.org",{"og:url":32,"og:type":79,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":81,"canonical":32},"index,follow",{"doc_id":83,"site_id":7},427731,1790912519,{"code":4,"msg":86,"data":87},"success",[88,92,96,100,105,110,115,119,123,126,130],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Story & 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FINANCIAL EXPLOITATION: ANALYSIS OF SOCIAL MEDIA DATA  \nTina Kilaberia, Weicheng Zeng, and Ruopeng An, New York University, New York, New York, United States  \nUnderreporting of privacy-laden family financial exploitation of older adults and Federal underinvestment in response to elder mistreatment can adversely influence older people’s help-seeking, health, and mental health outcomes. This study compared public  \nInnovation in Aging, 2025, Vol. 9, No. S2 661  \nattitudes toward family financial exploitation by focusing on one victim (21 YouTube videos), one perpetrator (8), and a case of family financial abuse generally (19), totaling 3,796 comments included in the analysis. The victims and perpetrators in the videos were not related. Three levels of analysis were conducted:  \nsentiment and emotion analyses using advanced natural language processing (NLP) methods and a human-coded subsample.  \nSentiment analysis showed that among the three cases, a greater share of negative (about 40%) and positive (nearly 60%) comments was attributed to the perpetrator and victim, respectively.  \nEmotion analysis found no shared emotions between the victim and perpetrator cases. Disapproval, surprise, disgust, and confusion were expressed solely concerning the perpetrator, whereas anger and fear were expressed solely concerning the victim.  \nCaring, love, and gratitude were shared emotions across the victim and the general cases. Sadness, admiration, curiosity, annoyance, and approval were five emotions shared across all three cases. Human-coded sub-sample analysis showed expressions of sarcasm, and microaggressions directed at the perpetrator’s race and gender. Although NLP methods did not capture victim-perpetrator background-related attitudes in relation to family financial abuse, they delineated relatively clear-cut attitudes toward victim and perpetrator status, reflecting a public concern toward elder family financial exploitation. Public attitudes may be harnessed for stakeholder engagement to increase reporting and investment in elder mistreatment.","cbCaieiKMgwSqHj7","https://ap.wps.com/l/cbCaieiKMgwSqHj7","pdf",60051,"English","# Abstract\n## Study Design and Dataset\n## Sentiment Analysis\n## Emotion Analysis\n## Human-Coded Subsample Insights\n## Implications for Reporting and Investment","[{\"question\":\"What data source and sample size were used to analyze public attitudes?\",\"answer\":\"The study used comments from 21 victim YouTube videos, 8 perpetrator videos, and 19 general family financial abuse videos, totaling 3,796 comments.\"},{\"question\":\"How did sentiment analysis differ across victim, perpetrator, and general cases?\",\"answer\":\"Negative comments (about 40%) were more associated with the perpetrator, while positive comments (nearly 60%) were more associated with the victim.\"},{\"question\":\"What key emotions were unique to perpetrator or victim cases?\",\"answer\":\"Disapproval, surprise, disgust, and confusion appeared only for the perpetrator, while anger and fear appeared only for the victim.\"},{\"question\":\"What additional insights did human-coded analysis provide beyond NLP?\",\"answer\":\"The human-coded subsample identified sarcasm and microaggressions targeting the perpetrator’s race and gender, which NLP did not capture for background-related victim-perpetrator attitudes.\"}]","PUBLIC ATTITUDE TOWARD ELDER FAMILY FINANCIAL EXPLOITATION - Analysis of Social Media Data | PDF",1790644703]