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Purpose <p>Elder financial mistreatment by family members is a private issue that is underreported and obscured from public view. This study examined public attitudes toward this form of family mistreatment utilizing YouTube data.</p> Methods <p>Combining Natural Language Processing (NLP) methods and human annotation, three cases were examined: one victim (21 videos), one alleged perpetrator (8 videos), and an aggregated case without a person focus (19 videos). The analysis included 3,796 comments across 48 videos.</p> Results <p>In NLP sentiment analysis, a greater share of negative (about 40%) and positive (nearly 60%) comments was associated with perpetrator and victim cases, respectively. NLP emotion analysis showed no shared emotions between the victim and perpetrator cases. Disapproval, surprise, disgust, and confusion were expressed solely concerning the perpetrator’s case, whereas anger and fear were exclusively expressed concerning the victim’s case. Caring, love, and gratitude were shared across the victim’s case and the aggregated case. Sadness, admiration, curiosity, annoyance, and approval were shared across all three cases. Human-annotated analysis showed intersectional aggression directed at the perpetrator’s case.</p> Conclusions <p>NLP methods reflected public concern toward elder family financial exploitation generally, and relatively clear-cut attitudes toward victim/perpetrator cases. Intersectional aggression was missed by NLP methods. The study broadens the theoretical purview beyond victim-perpetrator and family systems to include macrosystems. Recommendations pertain to the engagement of stakeholder and public attention to combat elder family financial mistreatment.</p>

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Behind Closed Doors, in Public View: Attitudes Toward Elder Family Financial Exploitation Based on Social Media

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Tina R. Kilaberia,
  • Weicheng Zeng,
  • Ruopeng An

摘要

Purpose

Elder financial mistreatment by family members is a private issue that is underreported and obscured from public view. This study examined public attitudes toward this form of family mistreatment utilizing YouTube data.

Methods

Combining Natural Language Processing (NLP) methods and human annotation, three cases were examined: one victim (21 videos), one alleged perpetrator (8 videos), and an aggregated case without a person focus (19 videos). The analysis included 3,796 comments across 48 videos.

Results

In NLP sentiment analysis, a greater share of negative (about 40%) and positive (nearly 60%) comments was associated with perpetrator and victim cases, respectively. NLP emotion analysis showed no shared emotions between the victim and perpetrator cases. Disapproval, surprise, disgust, and confusion were expressed solely concerning the perpetrator’s case, whereas anger and fear were exclusively expressed concerning the victim’s case. Caring, love, and gratitude were shared across the victim’s case and the aggregated case. Sadness, admiration, curiosity, annoyance, and approval were shared across all three cases. Human-annotated analysis showed intersectional aggression directed at the perpetrator’s case.

Conclusions

NLP methods reflected public concern toward elder family financial exploitation generally, and relatively clear-cut attitudes toward victim/perpetrator cases. Intersectional aggression was missed by NLP methods. The study broadens the theoretical purview beyond victim-perpetrator and family systems to include macrosystems. Recommendations pertain to the engagement of stakeholder and public attention to combat elder family financial mistreatment.