The Supreme Court’s Dobbs v. Jackson Women’s Health Organization decision in June 2022 reversed 50 years of precedent by allowing states to formulate their own abortion policies. This resetting of abortion policy created a new raft of opportunities and threats across the states for both pro-life and pro-choice supporters. In this study, we aim to analyze how public discourse around abortion responded to this changed political context. Using a dataset of 288,325 abortion-related Tweets posted in 2022, we examine public reaction to Dobbs using both quantitative and qualitative approaches. We categorize Tweets by abortion stance (pro-choice and pro-life ) and geo-political context by state groups ( protected, restricted, and unsettled based on abortion access policy). Our temporal analysis shows that while both pro-choice and pro-life Twitter activity spiked after both the leaked draft in May 2022 and the final decision, only pro-choice discussions maintained a heightened level of engagement over time. Analyzing the discussion frames among the Tweets reveals that pro-choice users emphasized a wider range of arguments that varied by state context, while pro-life Tweets were generally unresponsive to state context. Our findings indicate that the new threats and opportunities had uneven effects within pro-life and pro-choice public discourse.

The emergence and advancement of new technologies present promising opportunities for data-driven research in the Digital Humanities (DH). As an innovative intersection of quantum computing and humanities data, Quantum Humanities (QH) holds significant research potential. However, current studies related to QH remain scarce with limited applications involving quantum deep learning, and its feasibility, effectiveness, and efficiency on humanities data need in-depth exploration. To address this gap, this paper takes the role type recognition of painted faces of Beijing Opera as a case study. Several classical and competitive quantum deep learning–based and classical deep learning–based models (e.g., quantum convolutional neural networks and classical convolutional neural networks) are selected for comparison on the public image dataset Painted Faces of Beijing Opera digitized from books. Extensive empirical experimental results demonstrate that quantum deep learning–based models are of certain advantages and hold promising prospects in applied DH practices.

This study investigates the dynamics of anti-Muslim hate speech within Norwegian social media during the period between 2010 and 2021. Using a dataset of more than one million comments from Twitter and Facebook, we developed a custom hate speech classifier trained on an annotated corpus of 3,277 comments in Norwegian language. We identify that despite representing a small share of the total comments, hate speech content has increased over time. In an effort to understand the social network characteristics of hate speech content, we delve deeper into Twitter conversations as we can more easily identify how this content is spread. We develop network metrics to assess the prevalence, distribution, and diffusion of hateful content. The findings reveal that regardless of the number of users or tweets in a conversation, the volume of hateful content tends to remain constant. Furthermore, a small fraction of users contribute disproportionately to the dissemination of hate speech, with most conversations being limited in participant diversity. These results contribute to the growing field of computational social science by offering a novel methodology for studying hate speech in under-resourced languages and suggesting that mitigating hate speech may be possible through targeted network interventions rather than content removal alone.

This study investigates which members of the 118th U.S. Congress adopt and use Threads and TikTok, and what political, demographic, and constituency-level characteristics explain this variation. Grounded in diffusion of innovation theory, we ask: What factors predict the adoption and use of these emerging platforms? We compiled original data on all members of Congress ( N = 535) by collecting social media account information from official congressional websites and manually verifying platform presence. Adoption was measured as a binary variable, and usage as the number of posts made through November 2023. Using probit and OLS regression models, we tested predictors including party affiliation, age, race, leadership status, and prior digital engagement. The empirical analyses reveal that Democrats and younger legislators are more likely to adopt Threads and TikTok. Prior digital engagement consistently predicts usage on both platforms. Notably, racial identity plays a critical role: non-white members are more likely to adopt and use TikTok, while white members are more likely to use Threads. This study offers the first empirical analysis of congressional adoption and usage of Threads and TikTok. Our findings demonstrate that platform choice is shaped by identity, institutional context, and political strategy. These findings offer new insights into the determinants of early platform adoption among U.S. congress members and the importance of aligning communication choices with constituent behavior and platform culture.

Unlike past studies that examine whether fact-checking can counter conspiratorial belief, we reverse the lens to investigate if fact-checking itself prompts conspiracy belief. Our study occurs in the days immediately preceding the 2024 US election. Shortly thereafter, Meta’s CEO Mark Zuckerberg abandoned Facebook’s third-party program altogether, arguing fact-checkers “have destroyed more trust than they have created.” We provide timely insight into fact-checking concerns using a preregistered online survey-based experiment of US Facebook users’ ( n = 2,409), randomly assigned to view either a generic Facebook fact-check (treatment) or a Facebook login screen. Results show no overall effects of third-party fact-checking on users’ propensity for conspiratorial beliefs. However, when individuals with high conspiracy mentality and strong conservative identification encounter a fact-check, they are more likely to endorse Facebook-related conspiracy beliefs. We also observe a three-way interaction among political independents with high and low conspiracy beliefs, where fact-checking potentially triggers or reduces such beliefs.

Digital platforms now serve as crucial archives for analysing societal trends, yet their research APIs pose methodological challenges. This study critically evaluates TikTok Research API through comparative analysis of 6,373 videos from 14 creators in the United States and United Kingdom (2020–2022), contrasting API-derived outputs with manual collection and third-party analytics. The API demonstrated scalability, retrieving more videos than alternative methods and providing 22 variables, including eight unavailable elsewhere. However, limitations were substantial: transcriptions covered about 10% of the content, with more transcripts returned from American male creators. Engagement metrics exhibited inconsistent accuracy across data sources, with the API showing systematically lower view counts but higher comment and share counts compared to manual collection. The number of videos varied depending on sample composition, indicating that small changes in inclusion criteria could shift outcomes disproportionately. These results highlight systematic inconsistencies, showing why multi-method approaches remain necessary despite automation. While TikTok Research API offers valuable scale and ethical compliance, its demographic biases and metadata inconsistencies compromise validity. The study advocates integrated auditing protocols and targeted API refinements to improve representativeness and accuracy in platform-based research.

This study investigates the impact of self-efficacy among marketing professionals with digital tools affecting their strategic flexibility (ability to adjust marketing strategies to changing trends) and career adaptability (willingness to learn or change roles), and how these variables influence the adoption of metaverse marketing tools. Data were gathered using a cross-sectional online survey of 395 marketing and digital strategy professionals working in the e-commerce, digital marketing, information technology, and educational-technology sectors in China. Structural Equation Modeling (SEM) was used to examine a moderated mediation model between self-efficacy, strategic flexibility, career adaptability, and metaverse marketing adoption. Results show that Marketing self-efficacy significantly predicts strategic flexibility (β = 0.35, p < 0.001) and career adaptability (β = 0.30, p < 0.001) which fully mediate its impact on adoption, indicating an indirect behavioral path. Interestingly, avatar anthropomorphism strengthens these relationships (β = 0.20, p < 0.01 for strategic flexibility; β = 0.18, p < 0.01 for career adaptability) with higher human-likeness intensifying the mediated effects and highlighting the impact of anthropomorphic cues in virtual environments. This study provides insights for equipping professionals for digital transformation and offering actionable strategies for designing avatars to improve technology adoption in virtual marketing environments. The findings highlight that well calibrating avatar features—responsiveness, realism, and feedback—can enhance perceived trust and usability and social presence. Overall, this study extends research on virtual environments by identifying avatar anthropomorphism as a key boundary condition in technology adoption and offering design implications for AI-driven metaverse interfaces.

Objective: This paper analyzes gender equality across countries in the year 2024 by using the GGGI, with the intention of disentangling the unseen structural and non-deterministic patterns. Instead of repeating the process of calculating the index, it is openly recognizing the compositional feature of the GGGI and the unseen similarities between the indices. Methods: This research employs a global cross-sectional study of 146 countries over the four primary GGGI sectors: economic participation, education, health and survival, and empowerment. Where OLS is only employed as a diagnostic test, as its almost perfect fit (R 2 ∼1) is squarely mechanical and lacks relevance for inference. Apart from ensemble models employed for predictions, K-means clustering, SHAP analysis, and GridSearchCV optimization are also used. Findings: The out-of-sample predictions demonstrate high levels of predictive accuracy, with Gradient Boosting models yielding an R 2 of approximately 0.90 and an RMSE of approximately 0.045, indicating that there is significant nonlinear information beyond index aggregation. Unsupervised clustering techniques show that there are seven distinct country clusters that go beyond traditional geographic and income divisions, which can be identified with more than 93% accuracy. The SHAP results show that empowerment and economic participation are drivers, while there is insignificant variation in healthcare. Contribution: This study identifies the boundaries of regression analysis in index research, as well as the advantages of machine learning analysis in determining structural patterns related to gender equity.

This study investigates networked social influence in Sky: Children of the Light , a social multiplayer online game. Drawing on survey responses ( n = 9,254) and in-game data from over 660,000 players, we use an innovative graph-based machine learning approach to quantify how individuals influence others’ playtime, and regression analyses to test predictors from the COM-B model. Results show that Capability enhances influence, although excessive task focus correlates negatively with social impact; Opportunity emerges as the strongest predictor, with active social interactions significantly boosting influence; and Motivation varies by playstyle, with socializers and competitors demonstrating greater influence than narrative-focused players. By applying the COM-B model in a digital gaming context, this research highlights behavioral dimensions of player influence and employs a novel metric for quantifying interpersonal influence. These findings suggest practical implications for game design, particularly by highlighting how social interaction opportunities and different player motivations shape influence within communities.

In June 2024, youth-led protests in Kenya against a controversial Finance Bill demonstrated the connection between digital technologies and political activism in the Global South. This study examines how generative artificial intelligence (GAI) shapes political participation by focusing on Kenyan Gen Z activists who used ChatGPT to create custom models: Finance_Bill_GPT, Corrupt_Politicians_GPT, and MPs_Contribution_GPT (collectively called Protest_GPT_KE). These tools simplified complex laws, exposed corruption, and mobilized young people online, allowing them to bypass traditional sources such as media and elites. However, using GAI for activism raises ethical and political concerns, including surveillance, data rights, and state repression. The study surveyed 374 Kenyan Gen Z participants, primarily in Nairobi, and used Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the connections among AI use, tool appropriation, and political participation. Results show that ChatGPT use alone did not directly increase offline activism; its effect appeared when combined with Protest_GPT_KE and online participation. This study is one of the first to document how youth in the Global South are creatively using GAI for grassroots mobilization, demonstrating that GAI’s political influence depends on user innovation and context.