Public Attitude Toward Elder Family Financial Exploitation: Analysis of Social Media Data

TL;DR

This study analyzed public attitudes toward elder family financial exploitation using social media comments and found distinct emotional responses to victims and perpetrators.

Contribution

The study introduces a novel approach to understanding public sentiment on elder financial abuse through social media data and NLP methods.

Findings

Sentiment analysis revealed more positive comments toward victims and negative comments toward perpetrators.

Emotion analysis identified distinct emotions for victims (anger, fear) and perpetrators (disgust, confusion).

Human-coded analysis uncovered sarcasm and microaggressions targeting the perpetrator's race and gender.

Abstract

Underreporting 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 attitudes 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: sentiment and emotion analyses using advanced natural language processing (NLP) methods and a human-coded subsample. Sentiment 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. Emotion 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. Caring, 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.