Finding Love on a First Data: Matching Algorithms in Online Dating

Sharabi, Liesel L.

doi:10.1162/99608f92.1b5c3b7b

Cited by 12 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, dating apps serve both public and private roles in the interpersonal engagements of divorced groups. The current mode of dating leverages algorithmic recommendation features, which have become central to the functioning of online dating platforms ( Sharabi, 2022 ). Despite concerns about algorithmic matching such as privacy issues ( Narr, 2022 ) and control over user information ( Lutz et al, 2020 ), an increasing number of users are opting to form relationships via dating apps ( Vogels and Mcclain, 2023 ).…”

Section: Navigating New Connections: Exploring the Dynamics Of Dating...mentioning

confidence: 99%

Disembedding and re-embedding: the online interaction mechanisms of divorced youth in China

Wang,

Guo

2024

Front. Psychol.

View full text Add to dashboard Cite

IntroductionIn recent years, China’s divorce rates have remained high, especially in metropolitan areas such as Beijing and Shanghai, where rates reach up to 40%. Additionally, there has been a notable shift towards younger demographics in divorce cases. In a society that highly values marital harmony, divorce is often seen as a cultural transgression. Anthony Giddens’ theory of disembedding and re-embedding provides a useful framework for understanding these changes. This study addresses a gap in literature by focusing on the online social interactions of divorced Chinese youth, exploring their use of dating apps for emotional support and social reconnections.MethodsThis qualitative study employed semi-structured interviews with 19 divorced young adults in China who engaged with dating apps such as Momo, Tantan, and Soul. Participants were recruited via Douban and Xiaohongshu. The interviews, conducted through WeChat voice calls and Tencent Meetings, lasted 45-70 minutes each. Data was analyzed using Nvivo12 to understand the disembedding and re-embedding processes in their online interactions, exploring themes such as motivations, self-presentation, and the transition from online to offline engagements.Results and discussionThe findings reveal that these individuals face societal challenges, biases, and the residual effects of past marriages, leading them to seek refuge in online environments to avoid stigmatization. In digital spaces, they cautiously engage, revealing a lack of confidence through selective self-disclosure. Their goals range from forming same-sex and opposite-sex friendships to seeking new romantic relationships, indicating a nuanced approach to remarriage and challenging stereotypes of dating app users. Re-engaging online, they discover social support and a sense of community, which aids in regaining confidence post-divorce, underscoring the complex interplay between societal influences and individual adaptation strategies in the digital age. The study highlights the unique challenges faced by this demographic, including maintaining anonymity and dealing with societal prejudices. Future research should consider a broader age range and gender differences to provide a more comprehensive understanding of the online behaviors and experiences of divorced individuals.

show abstract

Section: Navigating New Connections: Exploring the Dynamics Of Dating...mentioning

confidence: 99%

Disembedding and re-embedding: the online interaction mechanisms of divorced youth in China

Wang,

Guo

2024

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…It is a straightforward self-correcting algorithm that is easily modified, resulting in several extensions and modifications throughout the years (Glickman, 1999;Schölkopf, Platt, & Hofmann, 2007). Due to its intuitive nature, the ERS has known widespread adoption in several domains including online gaming, economics and social sciences (DeLong et al, 2011;Neumann et al, 2011;Sharabi, 2022;Véron, Marin, & Monnet, 2014;Yang, Dimitrov, & Mantin, 2014).…”

Section: The Elo Rating Systemmentioning

confidence: 99%

Balancing stability and flexibility: Investigating a dynamic K value approach for the Elo rating system in adaptive learning environments

Vermeiren,

Hofman,

Bolsinova

et al. 2024

Preprint

View full text Add to dashboard Cite

In adaptive digital learning environments, it is essential to track learning trajectories. The Elo rating system, known for its computational simplicity, is frequently employed for this purpose. Current Elo-based systems cannot handle rapid changes in ability or are unable to balance accuracy and speed when updating player and item ratings. Changes in Elo ratings depend on the sensitivity parameter $K$. Using fixed $K$ values necessitates a trade-off: Larger values facilitate the tracking of evolving ability levels but introduce greater rating volatility. Smaller values yield more stable estimates, but are slower to reflect actual ability levels. Existing modifications of the Elo system, which diminish $K$ as the number of responses increases, are inadequate in scenarios characterized by considerable ability fluctuation, a common occurrence in digital learning environments. To address this challenge, we introduce a novel approach for dynamically adjusting $K$ values in response to observed trends in rating changes. This method increases $K$ during noticeable upward or downward shifts in ratings and reduces it otherwise. We present a computationally efficient implementation of this idea and validate its superiority over existing $K$ adjustment strategies through simulation studies. Additionally, we describe the implementation of this adaptive $K$ model in a widely-used digital learning platform, Math Garden, which leverages both accuracy and response time in its assessments. By successfully integrating speed and precision, this innovative implementation enhances the effectiveness of digital adaptive learning environments

show abstract

“…Humans are increasingly relying more and more on artificial intelligence (AI). AI plays a key role in tasks ranging from medical decision‐making (Giordano et al, 2021; Kumar et al, 2022) to resume scanning and job candidate selection (Lacroux & Martin‐Lacroux, 2022) to dating apps (Sharabi, 2022; Wu & Kelly, 2020).…”

Section: Introductionmentioning

confidence: 99%

Prevalence of bias against neurodivergence‐related terms in artificial intelligence language models

Brandsen,

Chandrasekhar,

Franz

et al. 2024

Autism Research

View full text Add to dashboard Cite

Given the increasing role of artificial intelligence (AI) in many decision‐making processes, we investigate the presence of AI bias towards terms related to a range of neurodivergent conditions, including autism, ADHD, schizophrenia, and obsessive‐compulsive disorder (OCD). We use 11 different language model encoders to test the degree to which words related to neurodiversity are associated with groups of words related to danger, disease, badness, and other negative concepts. For each group of words tested, we report the mean strength of association (Word Embedding Association Test [WEAT] score) averaged over all encoders and find generally high levels of bias. Additionally, we show that bias occurs even when testing words associated with autistic or neurodivergent strengths. For example, embedders had a negative average association between words related to autism and words related to honesty, despite honesty being considered a common strength of autistic individuals. Finally, we introduce a sentence similarity ratio test and demonstrate that many sentences describing types of disabilities, for example, “I have autism” or “I have epilepsy,” have even stronger negative associations than control sentences such as “I am a bank robber.”

show abstract

Finding Love on a First Data: Matching Algorithms in Online Dating

Cited by 12 publications

References 19 publications

Disembedding and re-embedding: the online interaction mechanisms of divorced youth in China

Disembedding and re-embedding: the online interaction mechanisms of divorced youth in China

Balancing stability and flexibility: Investigating a dynamic K value approach for the Elo rating system in adaptive learning environments

Prevalence of bias against neurodivergence‐related terms in artificial intelligence language models

Contact Info

Product

Resources

About