Social Science Computer Review最新文献_第3页

Computational Public Opinion Measurement: A Systematic Review of Methods and Methodological Limitations 计算民意测量：方法和方法局限性的系统回顾

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-06-10 DOI: 10.1177/08944393261457540

Eun Gyo Joung

{"title":"Computational Public Opinion Measurement: A Systematic Review of Methods and Methodological Limitations","authors":"Eun Gyo Joung","doi":"10.1177/08944393261457540","DOIUrl":"https://doi.org/10.1177/08944393261457540","url":null,"abstract":"Computational Public Opinion Measurement (CPOM) uses natural language processing and machine learning to infer public attitudes from social media. However, the methodological foundations and validity constraints of CPOM remain inadequately documented. This systematic literature review examines the computational methods, sentiment representations, and measurement strategies employed in CPOM research, and identifies the structural and methodological limitations that constrain CPOM as an approach to measurement. This systematic review (PRISMA, 2020) searched seven databases, identifying 56 studies from 5,108 records (2008–2025). Methods shifted substantially across eras: lexicon-based approaches declined from 77.8% (2011–2015) to 28.6% (2021–2025) while deep learning grew from 0% to 40.0%. Despite this technical evolution, empirical validation remains rare. Only 11 of 56 studies (19.6%) computed a quantitative statistic against an external benchmark such as a survey or poll. In other words, methodological sophistication and validation rigour moved in opposite directions: none of the 14 deep learning studies computed a quantitative validation statistic, while all 11 that did used lexicon-based or classical machine learning. The broader field shows an even larger gap: 49.4% of all full-text-assessed papers were excluded for providing no external validation or representativeness analysis at all. The recognition-action gap widened over time: TSE awareness grew from 44.4% to 80.0% across eras while quantitative validation rates fell from 33% to 11%, showing that growing awareness has not translated into action. CPOM must move beyond technical sophistication toward systematic criterion validation, demographic adjustment, and transparent reporting.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"18 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148289739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Whose Centre Holds? White Normativity in Race Dimensions Across Word Embeddings 谁的中心占上风？跨词嵌入的种族维度中的白人规范性

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-06-01 DOI: 10.1177/08944393261456180

Nnaemeka Ohamadike, Kevin Durrheim, Mpho Primus

{"title":"Whose Centre Holds? White Normativity in Race Dimensions Across Word Embeddings","authors":"Nnaemeka Ohamadike, Kevin Durrheim, Mpho Primus","doi":"10.1177/08944393261456180","DOIUrl":"https://doi.org/10.1177/08944393261456180","url":null,"abstract":"Bias in word embeddings is often measured using bipolar dimensions, constructed as the difference between two anchor centroids. This technique assumes both poles are symmetrical and equally informative. However, normativity literature shows that one category may function as the unmarked norm, with others framed as marked deviations. In race, whiteness typically holds the normative position, and embedding-based race dimensions may inherit the skew. We test this possibility using dimensions constructed from validated African–European name anchors, probed with neutral and valence words. In three embedding models (Wiki-News, South African news, Google News), we assess whether race dimensions favour whiteness as a normative anchor, whether this skew is stronger in culturally specific models (SA, Google), and whether bipolar offsets amplify one pole, given unipolar evidence. Results show that neutral and valence terms cluster nearer to the white pole (most strongly in the Wiki-News model), indicating whiteness as the semantic default. Overshoot favoured Black in Google and Wiki-News, while White overshoot only occurred in the South African model. We argue that this captures racialised variance where the pole with more spread tends to exert greater leverage on the bipolar axis. The study provides quantitative evidence of white-normative anchoring and diagnostics for asymmetric amplification in embedding-based bias measures.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"31 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148289700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can Machines Perform a Qualitative Data Analysis? Reading the Debate With Alan Turing 机器可以进行定性数据分析吗？阅读与艾伦·图灵的辩论

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-05-21 DOI: 10.1177/08944393261451707

Stefano De Paoli

引用次数: 0

Expanding Your Vocabulary: A Framework for Topic Integration in Texts 扩充词汇量：文章主题整合的框架

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-27 DOI: 10.1177/08944393261443513

Roy Gardner, Matthew Martin, Ashley Moran, Zachary Elkins, Andrés Cruz, Guillermo Pérez

{"title":"Expanding Your Vocabulary: A Framework for Topic Integration in Texts","authors":"Roy Gardner, Matthew Martin, Ashley Moran, Zachary Elkins, Andrés Cruz, Guillermo Pérez","doi":"10.1177/08944393261443513","DOIUrl":"https://doi.org/10.1177/08944393261443513","url":null,"abstract":"Topic discovery and integration are vital for maintaining vocabularies that categorize textual corpora. Automated approaches are often computationally expensive and lack domain-specific conceptual nuance; manual approaches are costly in terms of time and potential bias. To address this dilemma, we introduce the segments-as-topic (SAT) methodology, a four-stage process that combines automation and human expertise to assess candidate topics for vocabulary inclusion. In the SAT generation stage, a topic is formulated and refined through collaboration with domain experts, and then a sentence-level semantic similarity model retrieves corpus segments semantically aligned with the topic. The SAT expansion stage uses this seed set to find additional semantically similar segments, which are iteratively accepted or rejected to build a final segment set. During the review stage, a panel of scholars evaluates the topic for inclusion. In the integration stage, all segments in the final segment set are automatically tagged with the new topic. We apply this methodology to the Comparative Constitutions Project vocabulary that tracks over 330 topics in national constitutions, and demonstrate the addition of three new topics to the vocabulary. The SAT approach balances computational efficiency with expert judgment, offering a systematic, user-friendly, and replicable framework for social scientists to expand domain-specific vocabularies.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147751529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Donation: A First Step Towards Improving Representativeness in Algorithmic Hiring Datasets 数据捐赠：提高算法招聘数据集代表性的第一步

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-21 DOI: 10.1177/08944393261441840

Jorge Saldivar, Jessica Wulf, Carlos Castillo

{"title":"Data Donation: A First Step Towards Improving Representativeness in Algorithmic Hiring Datasets","authors":"Jorge Saldivar, Jessica Wulf, Carlos Castillo","doi":"10.1177/08944393261441840","DOIUrl":"https://doi.org/10.1177/08944393261441840","url":null,"abstract":"The use of algorithmic hiring is on the rise, becoming more and more common due to its capacity to process a large number of applications with efficiency. Despite the advantages of this approach, there have been numerous cases of discriminatory hiring outcomes. A significant contributing factor to such outcomes is the lack of representativeness in the data used to develop these systems. This results in a significant decline in performance for underrepresented groups, disproportionately impacting marginalized communities. Addressing bias in algorithmic hiring requires access to comprehensive datasets that include curriculum vitae (CV) and demographic information reflecting diverse backgrounds. Unfortunately, there is a lack of datasets that serve such a purpose. This paper introduces a data donation campaign designed to collect real-world CVs, including demographic and sensitive information, from a representative sample of individuals in the EU workforce. The paper discusses the design decisions underpinning the campaign, along with the challenges encountered during its deployment and execution. Finally, it offers lessons learned and practical solutions to overcome these challenges, thereby contributing valuable insights for future efforts in this domain.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"5 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147725650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Writing as an 18th-Century Automaton 写作就像一台18世纪的机器

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-20 DOI: 10.1177/08944393261441268

Julie Park

引用次数: 0

The Human Soul and its Deceptions: Early Modern Imitation Games 人类灵魂及其欺骗：早期现代模仿游戏

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-18 DOI: 10.1177/08944393261441270

Jonathan Regier

引用次数: 0

Decoding Psychological Abuse: A Comparative Study of Natural Language Processing (NLP) Classifiers Using Reddit Data 解码心理虐待：使用Reddit数据的自然语言处理（NLP）分类器的比较研究

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-15 DOI: 10.1177/08944393261435087

Maya Ashkenazi, Jessica Lilly Neubauer, María Pérez-Ortiz, Enrico Mariconti, Leonie Maria Tanczer

{"title":"Decoding Psychological Abuse: A Comparative Study of Natural Language Processing (NLP) Classifiers Using Reddit Data","authors":"Maya Ashkenazi, Jessica Lilly Neubauer, María Pérez-Ortiz, Enrico Mariconti, Leonie Maria Tanczer","doi":"10.1177/08944393261435087","DOIUrl":"https://doi.org/10.1177/08944393261435087","url":null,"abstract":"Psychological abuse, a form of intimate partner violence (IPV), involves non-physical behaviors meant to control, humiliate, punish, or threaten a partner. Despite its strong link to other forms of abuse, scholarly disagreement makes its definition ambiguous. Data-driven approaches, particularly machine learning (ML), remain underutilized in IPV research but offer advantages such as uncovering hidden patterns, efficiency, and scalability. This work applies natural language processing (NLP) modeling techniques (traditional modeling, fine-tuning, and few-shot learning) to classify six types of psychological abuse on a dataset of 1,500 labeled Reddit posts. Our LLaMA-3.70 few-shot model establishes a state-of-the-art baseline for this dataset. Not only does our study reveal that it is possible to employ NLP modeling to detect psychological abuse, it also demonstrates how some computational methods can make use of very limited datasets to produce high-quality results within the social sciences. We also apply an explainability measure (LIME) to surface model biases. This technique deepens understanding of model behavior, contextualizes performance, and sets a precedent for using computational methods in nuanced, human-centered social science research. This study demonstrates how ML paired with explainability measures can advance rigorous, responsible research in social disciplines involving ethical or semantic uncertainty.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"62 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

“Leibniz, Computing, and AI” “莱布尼茨、计算和人工智能”

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-09 DOI: 10.1177/08944393261441266

Audrey Borowski

引用次数: 0

Intelligent Artifice: Machine Learning From the Middle Ages to the Enlightenment 智能技术：从中世纪到启蒙运动的机器学习

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2026-04-08 DOI: 10.1177/08944393261441271

E. R. Truitt

引用次数: 0