Social Science Computer Review

Dearden, Thomas E.; Bergeron, Andréanne

doi: 10.1177/08944393261416786pmid: N/A

This study investigates international differences in hacking attempts using honeypot data. By analyzing 504,877 login attempts from 314 unique IP addresses over a 3-month period, the research aims to understand how socio-economic factors influence cybercriminal behavior. We consider prior international work on cybercrime to develop hypotheses regarding opportunity, which predict that countries with higher unemployment and poverty rates, as well as lower GDP and education expenditures, will exhibit more frequent hacking attempts. We found that higher unemployment rates and lower education expenditures correlate with an increase in the mean number of breach attempts per country. Lower education expenditures also correlate with higher success rates of breach attempts. No significant relationship was found between GDP or population below the poverty line and hacking behavior. This study highlights the role of socio-economic conditions in shaping cybercriminal activities, demonstrating that cybercrime does not occur in a vacuum but is influenced by the broader geopolitical context.

Kuran, Emre Can; Kuran, Umut

doi: 10.1177/08944393261419796pmid: N/A

Digital search platforms enable real-time observation of relationship distress through behavioral traces. This study tests whether Google Trends predicts official divorce rates in the United States, Germany, the Netherlands, and the United Kingdom from 2009 to 2023. We introduce Digital Behavioral Decoupling, in which online distress signals diverge from legal outcomes as divorce shifts from an institutional procedure to an emotionally mediated digital phenomenon. Methods include unit root tests, cointegration analysis, Granger causality, spectral coherence, and rolling-origin nowcasting. Search queries are grouped into pre-divorce (cognitive distress), during-divorce (procedural action), and post-divorce (emotional recovery) phases. Results show 62.5% of terms lead divorce rates by 1–2 years, yet only 8.3% remain significant after False Discovery Rate correction. The Netherlands demonstrates 100% forecast improvements beyond autoregressive models (DM 2.87–3.43, p < 0.02) across all search terms, from early relationship therapy queries through procedural and post-divorce searches, indicating systematic capture of the entire divorce pathway. Germany shows intermediate results with 33% forecast success beyond autoregressive benchmarks (DM 2.40–2.81) limited to problem-recognition terms, suggesting episodic crisis-driven engagement. The United States and United Kingdom show no forecast gains beyond autoregressive models despite high search volumes, consistent with information saturation in normalized divorce cultures. Lead-lag relationships are frequency-specific, concentrated at 3–5 year periodicities. Findings link family sociology with affective computing and provide a replicable toolkit for tracking relationship dissolution in algorithmically curated information environments.

Balafrej, Lamia

doi: 10.1177/08944393261441295pmid: N/A

This essay argues that the black box—both as cryptic device and as critique of illegibility—is not unique to modern technology and has deep roots in the medieval Mediterranean world. Technical opacity was frequently addressed in Latin and Arabic sources, often with a critical undertone. Then as now, technoskeptical writers saw the self-acting device as treacherous, due to its reliance on hidden labor and mechanisms. This critique arose especially in relation to unfamiliar or foreign devices, like animated idols; as such, it was often racializing, attributing opacity as well as deceit to the object and its makers. Modern critiques of technology that focus on invisible labor may reproduce similar biases by enforcing a privileged, first-world perspective. A transhistorical approach thus not only shows the enduring history of the black box; it also illuminates the religious genealogy of techno-skepticism, as well as the biases that inhere in the black box, especially when deployed as a critical discourse.

Qin, Xuanlong; Tam, Tony

doi: 10.1177/08944393261437640pmid: N/A

Social evaluation is fundamental to everyday interactions, yet our understanding has been constrained by fragmented theories and the lack of a scalable method for tracking group attitudes in real time. This paper resolves this methodological gap by introducing and validating a computational framework that empirically synthesizes three major theoretical models (Stereotype Content Model, Dual Perspective Model, and Semantic Differential) within a unified word embedding space. We demonstrate that social evaluation is structured by two core latent dimensions: Warmth-Communion-Evaluation (WCE), capturing affective and moral judgments, and Competence-Agency (CA), reflecting perceptions of ability and effectiveness. To validate its real-world utility, we apply this framework to U.S.-based Twitter posts about Chinese and Japanese individuals before and during the COVID-19 pandemic. Our analysis reveals that while perceptions of competence (CA) remained stable, affective evaluations (WCE) of Chinese individuals declined sharply, a dynamic not observed for Japanese individuals. This work offers a robust, scalable instrument for tracking intergroup attitudes during crises and provides a crucial bridge between social psychological theory and computational social science, enabling the real-time analysis of intergroup dynamics.

de León, Ernesto; Boeschoten, Laura; Votta, Fabio; Mulder, Joris; Struminskaya, Bella; Oberski, Daniel; Araujo, Theo; de Vreese, Claes

doi: 10.1177/08944393261435088pmid: N/A

Digital trace “data donation” studies offer researchers a unique opportunity to collect high-quality behavioral data, but decisions about the scope of requested data can impact both dataset richness and participant compliance. This paper examines the tradeoffs between requesting larger data packages, which include more extensive historical records, and participants’ willingness to donate. In a randomized experiment with Facebook and Instagram data donations, we compare a control condition where participants are asked to request the default 1-year data period to a treatment condition in which they are asked to request data for their entire account history. We analyze how different request sizes affect (1) participants’ compliance rates and (2) the characteristics of the data resulting from these different requests. We find that participants asked to request more data are less likely to complete the task. However, we propose that this is not primarily due to heightened privacy concerns, but rather because these data packages are significantly larger and therefore take longer for the platforms to deliver. This additional time to deliver data packages results in increased attrition. In terms of the effects on the data itself, we show that decisions about the time-span of the data impacts not only the volume of data requested, but also has implications for measurement validity, as the temporal window fundamentally redefines what key constructs represent, potentially transforming intended static indicators into narrow snapshots of recent behavior. We provide guidance for researchers navigating these decisions, considering both the benefits of richer longitudinal data and the risks of reduced participation.

Park, Julie

doi: 10.1177/08944393261441268pmid: N/A

This essay conceives artificial intelligence as a chapter in the history of writing through reconsidering the eighteenth-century automaton writer created by Jaquet Droz, a Swiss clock making workshop. As much an innovation in writing technology as it was an early example of artificial intelligence, the Jaquet Droz automaton writer reveals how artificial intelligence is a historical idea and material artifact deeply entangled with the history of writing, an embodied as well as deeply emotional form of cognitive activity and one of the oldest human technologies.

Nowshin, Nafisa; Kretschmer, Kelsy; Borradaile, Glencora

doi: 10.1177/08944393261421115pmid: N/A

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.

Borowski, Audrey

doi: 10.1177/08944393261441266pmid: N/A

Centuries before the advent of computers, the German philosopher and mathematician Gottfried Wilhelm von Leibniz (1646–1716) sketched out a “computational ontology” whereby information operates as an organic principle imposing order, molding and driving it, in such a way that the world gains a form of consciousness, and thought produces its being at the same time as it thinks itself.

Çelik, Sadullah; Köroğlu, Cemile Zehra

doi: 10.1177/08944393261419809pmid: N/A

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 (R2∼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 R2 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.

Awais, Muhammad

doi: 10.1177/08944393261426533pmid: N/A

Pakistan is one of the few remaining countries where wild poliovirus remains endemic, despite decades of eradication campaigns. Yet, vaccine hesitancy persists, not merely due to biomedical skepticism but through digital discourse. Drawing on 6,399 Urdu language tweets, this study uses natural language processing and lexicon based modeling to test four hypotheses on the emotional and symbolic drivers of hesitancy. Emotions are operationalized using the NRC Emotion Lexicon, treating trust and fear as measurable affective signals. The findings challenge Western behavioral models such as the Extended Parallel Process Model (EPPM): trust is negatively associated with hesitancy, whereas fear is positively associated with it even when trust is present. Notably, fear’s effect weakens in security framed tweets, which express moral resolve and collective strength rather than panic. Religious framing also predicts hesitancy, but it is often based on misquoted or misinterpreted religious references. In many cases, such discourse misaligns with the actual teachings of the religion, which historically endorse disease prevention and public health. Vaccine hesitancy in this context emerges not as an individual risk judgment, but as a culturally embedded form of communicative resistance, requiring discourse based, context sensitive approaches to global health communication.