We study games with natural‐language labels (i.e., strategic problems where options are denoted by words), for which we propose and test a measurable characterization of prominence. We assume that—ceteris paribus—players find particularly prominent those strategies that are denoted by words more frequently used in their everyday language. To operationalize this assumption, we suggest that the prominence of a strategy‐label is correlated with its frequency of occurrence in large text corpora, such as the Google Books corpus (“n‐gram” frequency). In testing for the strategic use of word frequency, we consider experimental games with different incentive structures (such as incentives to and not to coordinate), as well as subjects from different cultural/linguistic backgrounds. Our data show that frequently‐mentioned labels are more (less) likely to be selected when there are incentives to match (mismatch) others. Furthermore, varying one's knowledge of the others' country of residence significantly affects one's reliance on word frequency. Overall, the data show that individuals play strategies that fulfill our characterization of prominence in a (boundedly) rational manner.
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What might books be teaching young children about gender?
We investigated how gender is represented in children’s books using a novel 200,000 word corpus comprising 247 popular, contemporary books for young children. Using human judgments and word co-occurrence data, we quantified gender biases of words in individual books and in the whole corpus. We find that children’s books contain many words that adults judge as gendered. Semantic analyses based on co-occurrence data yielded word clusters related to gender stereotypes (e.g., feminine: emotions; masculine: tools). Co-occurrence data also indicate that many books instantiate gender stereotypes identified in other research (e.g., girls are better at reading and boys at math). Finally, we used large-scale data to estimate the gender distribution of the audience for individual books, and find that children are more often exposed to gender stereotypes for their own gender. Together the data suggest that children’s books may be an early source of gender associations and stereotypes.
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- Award ID(s):
- 2020969
- NSF-PAR ID:
- 10302096
- Date Published:
- Journal Name:
- Psychological science
- ISSN:
- 0956-7976
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Social media platforms are accused repeatedly of creating environments in which women are bullied and harassed. We argue that online aggression toward women aims to reinforce traditional feminine norms and stereotypes. In a mixed methods study, we find that this type of aggression on Twitter is common and extensive and that it can spread far beyond the original target. We locate over 2.9 million tweets in one week that contain instances of gendered insults (e.g., “bitch,” “cunt,” “slut,” or “whore”)—averaging 419,000 sexist slurs per day. The vast majority of these tweets are negative in sentiment. We analyze the social networks of the conversations that ensue in several cases and demonstrate how the use of “replies,” “retweets,” and “likes” can further victimize a target. Additionally, we develop a sentiment classifier that we use in a regression analysis to compare the negativity of sexist messages. We find that words in a message that reinforce feminine stereotypes inflate the negative sentiment of tweets to a significant and sizeable degree. These terms include those insulting someone’s appearance (e.g., “ugly”), intellect (e.g., “stupid”), sexual experience (e.g., “promiscuous”), mental stability (e.g., “crazy”), and age (“old”). Messages enforcing beauty norms tend to be particularly negative. In sum, hostile, sexist tweets are strategic in nature. They aim to promote traditional, cultural beliefs about femininity, such as beauty ideals, and they shame victims by accusing them of falling short of these standards. Harassment on social media constitutes an everyday, routine occurrence, with researchers finding 9,764,583 messages referencing bullying on Twitter over the span of two years (Bellmore et al. 2015). In other words, Twitter users post over 13,000 bullying-related messages on a daily basis. Forms of online aggression also carry with them serious, negative consequences. Repeated research documents that bullying victims suffer from a host of deleterious outcomes, such as low self-esteem (Hinduja and Patchin 2010), emotional and psychological distress (Ybarra et al. 2006), and negative emotions (Faris and Felmlee 2014; Juvonen and Gross 2008). Compared to those who have not been attacked, victims also tend to report more incidents of suicide ideation and attempted suicide (Hinduja and Patchin 2010). Several studies document that the targets of cyberbullying are disproportionately women (Backe et al. 2018; Felmlee and Faris 2016; Hinduja and Patchin 2010; Pew Research Center 2017), although there are exceptions depending on definitions and venues. Yet, we know little about the content or pattern of cyber aggression directed toward women in online forums. The purpose of the present research, therefore, is to examine in detail the practice of aggressive messaging that targets women and femininity within the social media venue of Twitter. Using both qualitative and quantitative analyses, we investigate the role of gender norm regulation in these patterns of cyber aggression.more » « less
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Abstract Psycholinguistic research on children's early language environments has revealed many potential challenges for language acquisition. One is that in many cases, referents of linguistic expressions are hard to identify without prior knowledge of the language. Likewise, the speech signal itself varies substantially in clarity, with some productions being very clear, and others being phonetically reduced, even to the point of uninterpretability. In this study, we sought to better characterize the language‐learning environment of American English‐learning toddlers by testing how well phonetic clarity and referential clarity align in infant‐directed speech. Using an existing Human Simulation Paradigm (HSP) corpus with referential transparency measurements and adding new measures of phonetic clarity, we found that the phonetic clarity of words’ first mentions significantly predicted referential clarity (how easy it was to guess the intended referent from visual information alone) at that moment. Thus, when parents’ speech was especially clear, the referential semantics were also clearer. This suggests that young children could use the phonetics of speech to identify globally valuable instances that support better referential hypotheses, by homing in on clearer instances and filtering out less‐clear ones. Such multimodal “gems” offer special opportunities for early word learning.
Research Highlights In parent‐infant interaction, parents’ referential intentions are sometimes clear and sometimes unclear; likewise, parents’ pronunciation is sometimes clear and sometimes quite difficult to understand.
We find that clearer referential instances go along with clearer phonetic instances, more so than expected by chance.
Thus, there are globally valuable instances (“gems”) from which children could learn about words’ pronunciations and words’ meanings at the same time.
Homing in on clear phonetic instances and filtering out less‐clear ones would help children identify these multimodal “gems” during word learning.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.31234/osf.io/ntgfe
