Julius Klingelhoefer, Alicia Gilbert, Adrian Meier
A much-discussed solution for undesirable (over-)use of mobile technologies lies in digital disconnection. Reasons for why individuals reduce their digital media use have been assessed mostly cross-sectionally without accounting for various disconnection practices across everyday situations. This study focuses on three motivations to disconnect that can vary between situations: to (a) avoid distractions, (b) improve well-being, and (c) be more present. A 14-day experience sampling study with 230 young adults (Mage = 25.31, SD = 4.50) yielded 7,360 situations of disconnective behavior. Multilevel regression analyses show that motivations to avoid distractions and to be more present were relevant for disconnection on the situational level. However, a person’s average level of these motivations did not predict disconnective behavior. The well-being motivation was not associated with disconnection either between or within participants. Additional analyses explore variations across time and different levels of digital disconnection.
{"title":"Momentary motivations for digital disconnection: an experience sampling study","authors":"Julius Klingelhoefer, Alicia Gilbert, Adrian Meier","doi":"10.1093/jcmc/zmae013","DOIUrl":"https://doi.org/10.1093/jcmc/zmae013","url":null,"abstract":"A much-discussed solution for undesirable (over-)use of mobile technologies lies in digital disconnection. Reasons for why individuals reduce their digital media use have been assessed mostly cross-sectionally without accounting for various disconnection practices across everyday situations. This study focuses on three motivations to disconnect that can vary between situations: to (a) avoid distractions, (b) improve well-being, and (c) be more present. A 14-day experience sampling study with 230 young adults (Mage = 25.31, SD = 4.50) yielded 7,360 situations of disconnective behavior. Multilevel regression analyses show that motivations to avoid distractions and to be more present were relevant for disconnection on the situational level. However, a person’s average level of these motivations did not predict disconnective behavior. The well-being motivation was not associated with disconnection either between or within participants. Additional analyses explore variations across time and different levels of digital disconnection.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"430 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The spread of multimodal coronavirus disease 2019 (COVID-19) misinformation on social media poses considerable public health risks. Yet limited research has addressed the efficacy of citizen-contributed, multimodal debunking messages, especially the roles of audiovisual structural features. In a between-subject online experiment, we assessed the impacts of misleading TikTok videos promoting the false claim that COVID-19 vaccines cause infertility and compared the effectiveness of debunking videos from medical experts vs. laypeople. We independently varied the presence of background music. Results showed that while misleading TikTok videos increased misperceptions, most debunking videos effectively countered such misinformation. Notably, compared with laypeople’s testimonial corrections, expert didactic videos benefited more from incorporating high-tempo background music, primarily through the suppression of counterarguing rather than through enhanced encoding. These findings underscore the importance to consider audiovisual structural features, such as background music, as well as the cognitive pathway through distracted counterarguing, in future research on multimodal misinformation and correction.
{"title":"Correction by distraction: how high-tempo music enhances medical experts’ debunking TikTok videos","authors":"Mengyu Li, Gaofei Li, Sijia Yang","doi":"10.1093/jcmc/zmae007","DOIUrl":"https://doi.org/10.1093/jcmc/zmae007","url":null,"abstract":"The spread of multimodal coronavirus disease 2019 (COVID-19) misinformation on social media poses considerable public health risks. Yet limited research has addressed the efficacy of citizen-contributed, multimodal debunking messages, especially the roles of audiovisual structural features. In a between-subject online experiment, we assessed the impacts of misleading TikTok videos promoting the false claim that COVID-19 vaccines cause infertility and compared the effectiveness of debunking videos from medical experts vs. laypeople. We independently varied the presence of background music. Results showed that while misleading TikTok videos increased misperceptions, most debunking videos effectively countered such misinformation. Notably, compared with laypeople’s testimonial corrections, expert didactic videos benefited more from incorporating high-tempo background music, primarily through the suppression of counterarguing rather than through enhanced encoding. These findings underscore the importance to consider audiovisual structural features, such as background music, as well as the cognitive pathway through distracted counterarguing, in future research on multimodal misinformation and correction.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"95 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Two studies examine how experiencing a social need threat (ostracism and rejection) impacts subsequent preferences for self-disclosure to various digital audiences. Findings consider how contextual/situational factors like need threats may impact the appeal of two established perceived social affordances of media: personalization and privacy/visibility. Participants took part in a (bogus) social media activity to elicit feelings of inclusion/ostracization/rejection and then were asked about sharing their media preferences with various potential audiences. Results show that social need threats have no significant impact on privacy preferences but do affect preferences for sharing with some audiences and not others. Notably, ostracized and rejected participants show different patterns of preferences, suggesting these forms of social need threat may have distinct impacts on future self-disclosures. Implications for online relationship development and community building are considered in the discussion.
{"title":"Does ostracism/rejection impact self-disclosures? Examining the appeal of perceived social affordances after social threat","authors":"Sara M Grady, Allison Eden, Ron Tamborini","doi":"10.1093/jcmc/zmae012","DOIUrl":"https://doi.org/10.1093/jcmc/zmae012","url":null,"abstract":"Two studies examine how experiencing a social need threat (ostracism and rejection) impacts subsequent preferences for self-disclosure to various digital audiences. Findings consider how contextual/situational factors like need threats may impact the appeal of two established perceived social affordances of media: personalization and privacy/visibility. Participants took part in a (bogus) social media activity to elicit feelings of inclusion/ostracization/rejection and then were asked about sharing their media preferences with various potential audiences. Results show that social need threats have no significant impact on privacy preferences but do affect preferences for sharing with some audiences and not others. Notably, ostracized and rejected participants show different patterns of preferences, suggesting these forms of social need threat may have distinct impacts on future self-disclosures. Implications for online relationship development and community building are considered in the discussion.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"11 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Part of the current mental health crisis is attributed to the increasing reliance on social media for daily tasks. By understanding behavioral or cognitive patterns that influence facets of well-being in real-time within individuals, we can empower individuals to intentionally adjust their behavior, thereby enhancing these aspects. This study utilized an experience sampling method to investigate the real-time effects of social media-induced social comparisons and posting on self-esteem and connectedness. Six times per day for 5 days, 74 adults reported on their social media use in the previous hour and their experiences of connectedness and self-esteem. Multilevel modeling demonstrated statistically significant within-person associations. Social media-induced upward comparison was related to momentary decreases in self-esteem, and moments when individuals posted on social media were related to higher levels of connectedness. The findings emphasize that specific experiences on social media may produce immediate effects for connectedness and self-esteem.
{"title":"Subtle momentary effects of social media experiences: an experience sampling study of posting and social comparisons on connectedness and self-esteem","authors":"Malinda Desjarlais","doi":"10.1093/jcmc/zmae004","DOIUrl":"https://doi.org/10.1093/jcmc/zmae004","url":null,"abstract":"Part of the current mental health crisis is attributed to the increasing reliance on social media for daily tasks. By understanding behavioral or cognitive patterns that influence facets of well-being in real-time within individuals, we can empower individuals to intentionally adjust their behavior, thereby enhancing these aspects. This study utilized an experience sampling method to investigate the real-time effects of social media-induced social comparisons and posting on self-esteem and connectedness. Six times per day for 5 days, 74 adults reported on their social media use in the previous hour and their experiences of connectedness and self-esteem. Multilevel modeling demonstrated statistically significant within-person associations. Social media-induced upward comparison was related to momentary decreases in self-esteem, and moments when individuals posted on social media were related to higher levels of connectedness. The findings emphasize that specific experiences on social media may produce immediate effects for connectedness and self-esteem.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"29 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarah Shugars, Alexi Quintana-Mathé, Robin Lange, David Lazer
Studies of gendered phenomena online have highlighted important disparities, such as who is likely to be elevated as an expert or face gender-based harassment. This research, however, typically relies upon inferring user gender—an act that perpetuates notions of gender as an easily observable, binary construct. Motivated by work in gender and queer studies, we therefore compare common approaches to gender inference in the context of online settings. We demonstrate that gender inference can have downstream consequences when studying gender inequities and find that nonbinary users are consistently likely to be misgendered or overlooked in analysis. In bringing a theoretical focus to this common methodological task, our contribution is in problematizing common measures of gender, encouraging researchers to think critically about what these constructs can and cannot capture, and calling for more research explicitly focused on gendered experiences beyond a binary.
{"title":"Categorizing the non-categorical: the challenges of studying gendered phenomena online","authors":"Sarah Shugars, Alexi Quintana-Mathé, Robin Lange, David Lazer","doi":"10.1093/jcmc/zmad053","DOIUrl":"https://doi.org/10.1093/jcmc/zmad053","url":null,"abstract":"Studies of gendered phenomena online have highlighted important disparities, such as who is likely to be elevated as an expert or face gender-based harassment. This research, however, typically relies upon inferring user gender—an act that perpetuates notions of gender as an easily observable, binary construct. Motivated by work in gender and queer studies, we therefore compare common approaches to gender inference in the context of online settings. We demonstrate that gender inference can have downstream consequences when studying gender inequities and find that nonbinary users are consistently likely to be misgendered or overlooked in analysis. In bringing a theoretical focus to this common methodological task, our contribution is in problematizing common measures of gender, encouraging researchers to think critically about what these constructs can and cannot capture, and calling for more research explicitly focused on gendered experiences beyond a binary.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"175 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study sought to investigate whether scholarly impact and academic influence differ between men and women in the field of communication and the extent to which the gender gap has persisted on social media platforms, an arena increasingly used for research dissemination. Data were collected from 10,736 articles, published in prominent communication journals between 2012 and 2022, using a combination of three sources: OpenAlex, Altmetric, and Twitter. The gender of 6,827 first authors was identified using ChatGPT, with an accuracy of 0.94. The findings confirmed the presence of the Matilda effect, indicating a bias toward male scholars in terms of research performance, academic mobility, and online popularity. Furthermore, the study revealed uneven gains between male and female scholars in their use of social media for research dissemination. These results have implications for how science communities can effectively promote research on social media.
{"title":"Can social media combat gender inequalities in academia? Measuring the prevalence of the Matilda effect in communication","authors":"Yunya Song, Xiaohui Wang, Guanrong Li","doi":"10.1093/jcmc/zmad050","DOIUrl":"https://doi.org/10.1093/jcmc/zmad050","url":null,"abstract":"This study sought to investigate whether scholarly impact and academic influence differ between men and women in the field of communication and the extent to which the gender gap has persisted on social media platforms, an arena increasingly used for research dissemination. Data were collected from 10,736 articles, published in prominent communication journals between 2012 and 2022, using a combination of three sources: OpenAlex, Altmetric, and Twitter. The gender of 6,827 first authors was identified using ChatGPT, with an accuracy of 0.94. The findings confirmed the presence of the Matilda effect, indicating a bias toward male scholars in terms of research performance, academic mobility, and online popularity. Furthermore, the study revealed uneven gains between male and female scholars in their use of social media for research dissemination. These results have implications for how science communities can effectively promote research on social media.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"1 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The underrepresentation of women in open-source software is frequently attributed to women’s lack of innate aptitude compared to men: natural gender differences in technical ability (Trinkenreich et al., 2021). Approaching code as a form of communication, I conduct a novel empirical study of gender differences in Python programming on GitHub. Based on 1,728 open-source projects, I ask if there is a gender difference in the quality and style of Python code measured in adherence to PEP-8 guidelines. I found significant gender differences in structure and how Python files are organized. While there is gendered variation in programming style, there is no evidence of gender difference in code quality. Using a Random Forest model, I show that the gender of a programmer can be predicted from the style of their Python code. The study concludes that gender differences in Python code are a matter of style, not quality.
{"title":"Programmed differently? Testing for gender differences in Python programming style and quality on GitHub","authors":"Siân Brooke","doi":"10.1093/jcmc/zmad049","DOIUrl":"https://doi.org/10.1093/jcmc/zmad049","url":null,"abstract":"The underrepresentation of women in open-source software is frequently attributed to women’s lack of innate aptitude compared to men: natural gender differences in technical ability (Trinkenreich et al., 2021). Approaching code as a form of communication, I conduct a novel empirical study of gender differences in Python programming on GitHub. Based on 1,728 open-source projects, I ask if there is a gender difference in the quality and style of Python code measured in adherence to PEP-8 guidelines. I found significant gender differences in structure and how Python files are organized. While there is gendered variation in programming style, there is no evidence of gender difference in code quality. Using a Random Forest model, I show that the gender of a programmer can be predicted from the style of their Python code. The study concludes that gender differences in Python code are a matter of style, not quality.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"288 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines how gender stereotypes are reflected in discourses around controversial science issues across two platforms, YouTube and TikTok. Utilizing the Social Identity Model of Deindividuation Effects, we developed hypotheses and research questions about how content creators might use gender-related stereotypes to engage audiences. Our analyses of climate change and vaccination videos, considering various modalities such as captions and thumbnails, revealed that themes related to children and health often appeared in videos mentioning women, while science misinformation was more common in videos mentioning men. We observed cross-platform differences in portraying gender stereotypes. YouTube’s video descriptions often highlighted women-associated moral language, whereas TikTok emphasized men-associated moral language. YouTube’s thumbnails frequently featured climate activists or women with nature, while TikTok’s thumbnails showed women in Vlog-style selfies and with feminine gestures. These findings advance understanding about gender and science through a cross-platform, multi-modal approach and offer potential intervention strategies.
{"title":"Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms","authors":"Kaiping Chen, Zening Duan, Sang Jung Kim","doi":"10.1093/jcmc/zmad052","DOIUrl":"https://doi.org/10.1093/jcmc/zmad052","url":null,"abstract":"This study examines how gender stereotypes are reflected in discourses around controversial science issues across two platforms, YouTube and TikTok. Utilizing the Social Identity Model of Deindividuation Effects, we developed hypotheses and research questions about how content creators might use gender-related stereotypes to engage audiences. Our analyses of climate change and vaccination videos, considering various modalities such as captions and thumbnails, revealed that themes related to children and health often appeared in videos mentioning women, while science misinformation was more common in videos mentioning men. We observed cross-platform differences in portraying gender stereotypes. YouTube’s video descriptions often highlighted women-associated moral language, whereas TikTok emphasized men-associated moral language. YouTube’s thumbnails frequently featured climate activists or women with nature, while TikTok’s thumbnails showed women in Vlog-style selfies and with feminine gestures. These findings advance understanding about gender and science through a cross-platform, multi-modal approach and offer potential intervention strategies.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"2 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This special issue collects studies about how gender divides manifest in digital environments, spanning online repositories, social media, and AI-powered technologies. Computational research helps in assessing the nature and prevalence of gender divides: Identifying differences and bias requires defining benchmarks, systematic departures, and overall incidence. This collection showcases evidence uncovered quantitatively and illustrates how such evidence can advance theoretical understanding of gender dynamics as socially constructed phenomena. Social interactions and discursive practices are shaped by the technologies we use to communicate, work, and organize. These technologies shape, in turn, how we perceive and reinforce gender stereotypes. In this editors‘ note, we discuss how the seven articles included in the special issue unpack communicative processes in the context of various online environments, disentangling gendered dynamics from the use of digital technologies. Ultimately, our goal is to energize a research agenda that requires continued work as technologies morph and evolve in unprecedented directions.
{"title":"Quantifying gender disparities and bias online: editors’ introduction to “Gender Gaps in Digital Spaces” special issue","authors":"Emőke-Ágnes Horvát, Sandra González-Bailón","doi":"10.1093/jcmc/zmad054","DOIUrl":"https://doi.org/10.1093/jcmc/zmad054","url":null,"abstract":"This special issue collects studies about how gender divides manifest in digital environments, spanning online repositories, social media, and AI-powered technologies. Computational research helps in assessing the nature and prevalence of gender divides: Identifying differences and bias requires defining benchmarks, systematic departures, and overall incidence. This collection showcases evidence uncovered quantitatively and illustrates how such evidence can advance theoretical understanding of gender dynamics as socially constructed phenomena. Social interactions and discursive practices are shaped by the technologies we use to communicate, work, and organize. These technologies shape, in turn, how we perceive and reinforce gender stereotypes. In this editors‘ note, we discuss how the seven articles included in the special issue unpack communicative processes in the context of various online environments, disentangling gendered dynamics from the use of digital technologies. Ultimately, our goal is to energize a research agenda that requires continued work as technologies morph and evolve in unprecedented directions.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"294 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139680111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luhang Sun, Mian Wei, Yibing Sun, Yoo Ji Suh, Liwei Shen, Sijia Yang
Generative Artificial Intelligence (AI) models like DALL·E 2 can interpret prompts and generate high-quality images that exhibit human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL·E 2 images spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google Images. Our findings reveal that DALL·E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL·E 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL·E 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-generated images on the media ecology.
{"title":"Smiling women pitching down: auditing representational and presentational gender biases in image-generative AI","authors":"Luhang Sun, Mian Wei, Yibing Sun, Yoo Ji Suh, Liwei Shen, Sijia Yang","doi":"10.1093/jcmc/zmad045","DOIUrl":"https://doi.org/10.1093/jcmc/zmad045","url":null,"abstract":"Generative Artificial Intelligence (AI) models like DALL·E 2 can interpret prompts and generate high-quality images that exhibit human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL·E 2 images spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google Images. Our findings reveal that DALL·E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL·E 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL·E 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-generated images on the media ecology.","PeriodicalId":48319,"journal":{"name":"Journal of Computer-Mediated Communication","volume":"254 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139680116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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