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Apologies as signals for change? Implicit theories of personality and reactions to apologies during the #MeToo movement.

ABSTRACT: During a national reckoning against sexual violence, the public read or heard dozens of apologies offered by prominent public figures in response to allegations of sexual misconduct. This study examined people's reactions to these apologies, with a focus on whether their implicit theories of personality-their beliefs about whether personality is changeable-influenced their evaluations of the apologies and the men who issued them. Using a nationally representative sample (N = 720) and real apologies offered during the #MeToo movement, it was found that, relative to people holding more of an entity (i.e., fixed) view of personality, those holding more of an incremental (i.e., malleable) view evaluated the apologies and apologizers more favorably, held more positive general attitudes toward this recent wave of apologies for misconduct, and were more likely to indicate that redemption was possible for the accused men. These findings suggest that people who hold more of an incremental theory of personality might interpret an apology as a meaningful signal that a person is ready and willing to change their ways and work toward self-improvement.

SUBMITTER: Schumann K

PROVIDER: S-EPMC6927633 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Apologies as signals for change? Implicit theories of personality and reactions to apologies during the #MeToo movement.

Schumann Karina K

PloS one 20191223 12

During a national reckoning against sexual violence, the public read or heard dozens of apologies offered by prominent public figures in response to allegations of sexual misconduct. This study examined people's reactions to these apologies, with a focus on whether their implicit theories of personality-their beliefs about whether personality is changeable-influenced their evaluations of the apologies and the men who issued them. Using a nationally representative sample (N = 720) and real apolog ...[more]

PMID: 31869325

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Project description:RationaleImplicit theories reflect core assumptions about whether human attributes are malleable or fixed: Incremental theorists believe a characteristic is malleable whereas entity theorists believe it is fixed. People with entity theories about health may be less likely to engage in risk-mitigating behavior. Spontaneous self-affirmation (e.g., reflecting on one's values when threatened) may lessen defensiveness and unhealthy behaviors associated with fixed beliefs, and reduce the likelihood of responding to health risk information with fixed beliefs.MethodAcross two studies conducted in the US from 2012 to 2015, we investigated how self-affirmation and implicit theories about health and body weight were linked to engagement with genetic risk information. In Study 1, participants in a genome sequencing trial (n = 511) completed cross-sectional assessments of implicit theories, self-affirmation, and intentions to learn, share, and use genetic information. In Study 2, overweight women (n = 197) were randomized to receive genetic or behavioral explanations for weight; participants completed surveys assessing implicit theories, self-affirmation, self-efficacy, motivation, and intentions.ResultsFixed beliefs about weight were infrequently endorsed across studies (10.8-15.2%). In Study 1, participants with stronger fixed theories were less interested in learning and using genetic risk information about medically actionable disease; these associations were weaker among participants higher in self-affirmation. In Study 2, among participants given behavioral explanations for weight, stronger fixed theories about weight were associated with lower motivation and intentions to eat a healthy diet. Among participants given genetic explanations, being higher in self-affirmation was associated with less fixed beliefs.ConclusionStronger health-related fixed theories may decrease the likelihood of benefiting from genetic information, but less so for people who self-affirm.

Project description:BACKGROUND:The #MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began. OBJECTIVE:The aim of this study is to document, characterize, and quantify early public discourse and conversation of the #MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events. METHODS:We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase "MeToo" from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse. RESULTS:We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. We then estimated that in the first week of the movement, 11% of novel English language tweets with the words "MeToo" revealed details about the poster's experience of sexual assault or abuse and 5.8% revealed early life experiences of such events. We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement. CONCLUSIONS:These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement.

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Apologies as signals for change? Implicit theories of personality and reactions to apologies during the #MeToo movement.

Publications

Apologies as signals for change? Implicit theories of personality and reactions to apologies during the #MeToo movement.

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