Black Twitter is gold

Klassen, Shamika

doi:10.1145/3505681

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…This became increasingly salient during COVID-19 pandemic lockdowns as digital communication became a positive force and lifeline for many people [97,98]. Furthermore, social media can be highly beneficial to facilitating community building and advocacy work, allowing Black youth to connect across geographic boundaries [99,100]. Similarly, SGM youth can especially benefit from online platforms, including by viewing informative/educational content supporting their identity formation, finding peer support or role models, and navigating the coming out process [101][102][103].…”

Section: Discussionmentioning

confidence: 99%

Probing the digital exposome: associations of social media use patterns with youth mental health

Pagliaccio,

Tran,

Visoki

et al. 2024

NPP—Digit Psychiatry Neurosci

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Recently, the U.S. Surgeon General issued an advisory highlighting the lack of knowledge about the safety of ubiquitous social media use on adolescent mental health. For many youths, social media use can become excessive and can contribute to frequent exposure to adverse peer interactions (e.g., cyberbullying, and hate speech). Nonetheless, social media use is complex, and although there are clear challenges, it also can create critical new avenues for connection, particularly among marginalized youth. In the current project, we leverage a large nationally diverse sample of adolescents from the Adolescent Brain Cognitive Development (ABCD) Study assessed between 2019–2020 (N = 10,147, Mage = 12.0, 48% assigned female at birth, 20% Black, 20% Hispanic) to test the associations between specific facets of adolescent social media use (e.g., type of apps used, time spent, addictive patterns of use) and overall mental health. Specifically, a data-driven exposome-wide association was applied to generate digital exposomic risk scores that aggregate the cumulative burden of digital risk exposure. This included general usage, cyberbullying, having secret accounts, problematic/addictive use behavior, and other factors. In validation models, digital exposomic risk explained substantial variance in general child-reported psychopathology, and a history of suicide attempt, over and above sociodemographics, non-social screentime, and non-digital adversity (e.g., abuse, poverty). Furthermore, differences in digital exposomic scores also shed insight into mental health disparities, among youth of color and sexual and gender minority youth. Our work using a data-driven approach supports the notion that digital exposures, in particular social media use, contribute to the mental health burden of US adolescents.

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Section: Discussionmentioning

confidence: 99%

Probing the digital exposome: associations of social media use patterns with youth mental health

Pagliaccio,

Tran,

Visoki

et al. 2024

NPP—Digit Psychiatry Neurosci

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“…This keyword-centric approach may have missed some relevant tweets. However, using keyword searches to collect data from Twitter is a common approach to study user activities on Twitter [46], including for studies about information-sharing by users on Twitter about user-driven audits on other platforms [44].…”

Section: Limitationsmentioning

confidence: 99%

Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?

Kingsley

Fan

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Recent years have witnessed an interesting phenomenon in which users come together to interrogate potentially harmful algorithmic behaviors they encounter in their everyday lives. Researchers have started to develop theoretical and empirical understandings of these user-driven audits, with a hope to harness the power of users in detecting harmful machine behaviors. However, little is known about users' participation and their division of labor in these audits, which are essential to support these collective eorts in the future. Through collecting and analyzing 17,984 tweets from four recent cases of user-driven audits, we shed light on patterns of users' participation and engagement, especially with the top contributors in each case. We also identied the various roles users' generated content played in these audits, including hypothesizing, data collection, amplication, contextualization, and escalation. We discuss implications for designing tools to support user-driven audits and users who labor to raise awareness of algorithm bias. CCS CONCEPTS• Human-centered computing ! Human computer interaction (HCI); Empirical studies in HCI .

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“…he proliferation of mobile devices has witnessed a surge in the utilization of social networking applications, including prominent platforms such as Twitter [1] and Instagram [2], alongside numerous others [3][4][5]. These applications have emerged as pivotal avenues for facilitating mobile-based social interactions and the rapid dissemination of information, consequently augmenting the scale of data within social networks [6].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Community Detection Algorithm in Online Social Networks

Han

Shi

Liu

et al. 2023

Preprint

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The advent of social networks has brought about a paradigm shift in the conventional modes of social interaction and information exchange. Consequently, there is an increasing scholarly interest in the precise detection of communities and analysis of network structures. However, most of the prevailing methods for community detection suffer from limitations in accuracy and efficiency due to the requirement of manually configuring attribute vector dimensions during the extraction of attribute information. Moreover, these algorithms often neglect the pervasive influence of users within the global network and their capacity to disseminate information, thereby undermining the accuracy of community detection. To address these challenges, this study proposes a novel community detection algorithm, named HL Louvain, which draws upon the Hypertext Induced Topic Search (HITS) technique. The HL Louvain algorithm initially applies graph compression to the entire network and subsequently leverages the HITS algorithm to extract global node characteristics. By combining semantic attribute information with local user features, the algorithm defines the collective influence of users. This, in turn, facilitates accurate community detection by modifying the iterative approach of the conventional Louvain algorithm. Furthermore, the algorithm significantly enhances accuracy and stability by autonomously optimizing the iterative process to determine the dimensionality of the attribute vector and the number of topics within the identified network. Experimental evaluations conducted on three distinct Twitter datasets with varying degrees of complexity, as well as a public dataset, demonstrate that the HL Louvain algorithm outperforms other state-of-the-art algorithms in terms of accuracy and stability.

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Black Twitter is gold

Abstract: Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor

Cited by 9 publications

References 3 publications

Probing the digital exposome: associations of social media use patterns with youth mental health

Probing the digital exposome: associations of social media use patterns with youth mental health

Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?

An Efficient Community Detection Algorithm in Online Social Networks

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