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Pachucki MC, Hong CS, O'Malley AJ, Levy DE, Thorndike AN. Network spillover effects associated with the ChooseWell 365 workplace randomized controlled trial to promote healthy food choices. Soc Sci Med 2024; 355:117033. [PMID: 38981183 DOI: 10.1016/j.socscimed.2024.117033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 07/11/2024]
Abstract
Food choices are closely linked to culture, social relationships, and health. Because many adults spend up to half their time at work, the workplace provides a venue for changing population health-related behaviors and norms. It is unknown whether the effects of a workplace intervention to improve health behaviors might spread beyond participating employees due to social influence. ChooseWell 365 was a randomized controlled trial testing a 12-month healthy eating intervention grounded in principles of behavioral economics. This intervention leveraged an existing cafeteria traffic-light labeling system (green = healthy; red = unhealthy) in a large hospital workplace and demonstrated significant improvements in healthy food choices by employees in the intervention vs. control group. The current study used data from over 29 million dyadic purchasing events during the trial to test whether social ties to a trial participant co-worker (n = 299 intervention, n = 302 control) influenced the workplace food choices of non-participants (n = 7900). There was robust evidence that non-participants who were socially tied to more intervention group participants made healthier workplace food purchases overall, and purchased a greater proportion of healthy (i.e., green) food and beverages, and fewer unhealthy (i.e., red) beverages and modest evidence that the benefit of being tied to intervention participants was greater than being tied to control participants. Although individual-level effect sizes were small, a range of consistent findings indicated that this light-touch intervention yielded spillover effects of healthy eating behaviors on non-participants. Results suggest that workplace healthy eating interventions could have population benefits extending beyond participants.
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Affiliation(s)
- Mark C Pachucki
- Department of Sociology & Computational Social Science Institute, University of Massachusetts, Amherst, MA, 01003, USA.
| | - Chen-Shuo Hong
- Department of Sociology & Computational Social Science Institute, University of Massachusetts, Amherst, MA, 01003, USA
| | - A James O'Malley
- Department of Biomedical Data Science and the Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03756, USA
| | - Douglas E Levy
- Harvard Medical School, Boston, MA, 02115, USA; Mongan Institute Health Policy Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Anne N Thorndike
- Harvard Medical School, Boston, MA, 02115, USA; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
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He W, Zhang W, Jin Y, Zhou Q, Zhang H, Xia Q. Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis. J Med Internet Res 2024; 26:e54706. [PMID: 38687566 PMCID: PMC11094593 DOI: 10.2196/54706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/20/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND There is a dearth of feasibility assessments regarding using large language models (LLMs) for responding to inquiries from autistic patients within a Chinese-language context. Despite Chinese being one of the most widely spoken languages globally, the predominant research focus on applying these models in the medical field has been on English-speaking populations. OBJECTIVE This study aims to assess the effectiveness of LLM chatbots, specifically ChatGPT-4 (OpenAI) and ERNIE Bot (version 2.2.3; Baidu, Inc), one of the most advanced LLMs in China, in addressing inquiries from autistic individuals in a Chinese setting. METHODS For this study, we gathered data from DXY-a widely acknowledged, web-based, medical consultation platform in China with a user base of over 100 million individuals. A total of 100 patient consultation samples were rigorously selected from January 2018 to August 2023, amounting to 239 questions extracted from publicly available autism-related documents on the platform. To maintain objectivity, both the original questions and responses were anonymized and randomized. An evaluation team of 3 chief physicians assessed the responses across 4 dimensions: relevance, accuracy, usefulness, and empathy. The team completed 717 evaluations. The team initially identified the best response and then used a Likert scale with 5 response categories to gauge the responses, each representing a distinct level of quality. Finally, we compared the responses collected from different sources. RESULTS Among the 717 evaluations conducted, 46.86% (95% CI 43.21%-50.51%) of assessors displayed varying preferences for responses from physicians, with 34.87% (95% CI 31.38%-38.36%) of assessors favoring ChatGPT and 18.27% (95% CI 15.44%-21.10%) of assessors favoring ERNIE Bot. The average relevance scores for physicians, ChatGPT, and ERNIE Bot were 3.75 (95% CI 3.69-3.82), 3.69 (95% CI 3.63-3.74), and 3.41 (95% CI 3.35-3.46), respectively. Physicians (3.66, 95% CI 3.60-3.73) and ChatGPT (3.73, 95% CI 3.69-3.77) demonstrated higher accuracy ratings compared to ERNIE Bot (3.52, 95% CI 3.47-3.57). In terms of usefulness scores, physicians (3.54, 95% CI 3.47-3.62) received higher ratings than ChatGPT (3.40, 95% CI 3.34-3.47) and ERNIE Bot (3.05, 95% CI 2.99-3.12). Finally, concerning the empathy dimension, ChatGPT (3.64, 95% CI 3.57-3.71) outperformed physicians (3.13, 95% CI 3.04-3.21) and ERNIE Bot (3.11, 95% CI 3.04-3.18). CONCLUSIONS In this cross-sectional study, physicians' responses exhibited superiority in the present Chinese-language context. Nonetheless, LLMs can provide valuable medical guidance to autistic patients and may even surpass physicians in demonstrating empathy. However, it is crucial to acknowledge that further optimization and research are imperative prerequisites before the effective integration of LLMs in clinical settings across diverse linguistic environments can be realized. TRIAL REGISTRATION Chinese Clinical Trial Registry ChiCTR2300074655; https://www.chictr.org.cn/bin/project/edit?pid=199432.
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Affiliation(s)
- Wenjie He
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
- Dongguan Rehabilitation Experimental School, Dongguan, China
| | - Wenyan Zhang
- Lanzhou University Second Hospital, Lanzhou University, Lanzhou, China
| | - Ya Jin
- Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan, China
| | - Qiang Zhou
- Dongguan Rehabilitation Experimental School, Dongguan, China
| | - Huadan Zhang
- Dongguan Rehabilitation Experimental School, Dongguan, China
| | - Qing Xia
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Lim ZW, Stuart H, De Deyne S, Regier T, Vylomova E, Cohn T, Kemp C. A Computational Approach to Identifying Cultural Keywords Across Languages. Cogn Sci 2024; 48:e13402. [PMID: 38226686 DOI: 10.1111/cogs.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/26/2023] [Accepted: 12/19/2023] [Indexed: 01/17/2024]
Abstract
Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian, hati (heart) in Indonesian and Malay, and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful linguistic analyses (Peeters, 2020b; Wierzbicka, 1990). Because keywords are expected to have different statistical properties than related words in other languages, we argue that a quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim, we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally specific words that range from "obvious" examples, such as Amsterdam in Dutch, to non-obvious yet independently proposed examples, such as hati (heart) in Indonesian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.
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Affiliation(s)
- Zheng Wei Lim
- School of Computing and Information Systems, University of Melbourne
| | - Harry Stuart
- School of Computing and Information Systems, University of Melbourne
| | - Simon De Deyne
- Melbourne School of Psychological Sciences, University of Melbourne
| | - Terry Regier
- Department of Linguistics and Cognitive Science Program, University of California, Berkeley
| | | | - Trevor Cohn
- School of Computing and Information Systems, University of Melbourne
| | - Charles Kemp
- Melbourne School of Psychological Sciences, University of Melbourne
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Elmer T. Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices. EPJ DATA SCIENCE 2023; 12:58. [PMID: 38098785 PMCID: PMC10716103 DOI: 10.1140/epjds/s13688-023-00434-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Puberty is a phase in which individuals often test the boundaries of themselves and surrounding others and further define their identity - and thus their uniqueness compared to other individuals. Similarly, as Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve scientific rigor and refine its identity. However, there are certain areas within CSS that are reluctant to adopt rigorous scientific practices from other fields, which can be observed through an overreliance on passively collected data (e.g., through digital traces, wearables) without questioning the validity of such data. This paper argues that CSS should embrace the potential of combining both passive and active measurement practices to capitalize on the strengths of each approach, including objectivity and psychological quality. Additionally, the paper suggests that CSS would benefit from integrating practices and knowledge from other established disciplines, such as measurement validation, theoretical embedding, and open science practices. Based on this argument, the paper provides ten recommendations for CSS to mature as an interdisciplinary field of research.
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Affiliation(s)
- Timon Elmer
- Department of Psychology, Applied Social and Health Psychology, University of Zurich, Binzmühlestrasse 14/14, 8050 Zurich, Switzerland
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Selin NE, Giang A, Clark WC. Progress in modeling dynamic systems for sustainable development. Proc Natl Acad Sci U S A 2023; 120:e2216656120. [PMID: 37751553 PMCID: PMC10556647 DOI: 10.1073/pnas.2216656120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023] Open
Abstract
This Perspective evaluates recent progress in modeling nature-society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice-defining purpose, selecting components, analyzing interactions, and assessing interventions-we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature-society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.
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Affiliation(s)
- Noelle E Selin
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Amanda Giang
- Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - William C Clark
- John F. Kennedy School of Government, Harvard University, Cambridge, MA 02138
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6
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Leitgöb H, Prandner D, Wolbring T. Editorial: Big data and machine learning in sociology. FRONTIERS IN SOCIOLOGY 2023; 8:1173155. [PMID: 37229284 PMCID: PMC10203698 DOI: 10.3389/fsoc.2023.1173155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Affiliation(s)
- Heinz Leitgöb
- Institute of Sociology, Leipzig University, Leipzig, Germany
- Institute of Sociology, University of Frankfurt, Frankfurt, Germany
| | | | - Tobias Wolbring
- Institute of Labour Market and Socioeconomics, University of Erlangen-Nuremberg, Nuremberg, Germany
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Polzer JT. The rise of people analytics and the future of organizational research. RESEARCH IN ORGANIZATIONAL BEHAVIOR 2023. [DOI: 10.1016/j.riob.2023.100181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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8
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Global Trends in Research of Mitochondrial Biogenesis over past 20 Years: A Bibliometric Analysis. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2023; 2023:7291284. [PMID: 36644577 PMCID: PMC9833928 DOI: 10.1155/2023/7291284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/29/2022] [Accepted: 12/07/2022] [Indexed: 01/06/2023]
Abstract
Background Mitochondrial biogenesis-related studies have increased rapidly within the last 20 years, whereas there has been no bibliometric analysis on this topic to reveal relevant progress and development trends. Objectives In this study, a bibliometric approach was adopted to summarize and analyze the published literature in this field of mitochondrial biogenesis over the past 20 years to reveal the major countries/regions, institutions and authors, core literature and journal, research hotspots and frontiers in this field. Methods The Web of Science Core Collection database was used for literature retrieval and dataset export. The CiteSpace and VOSviewer visual mapping software were used to explore research collaboration between countries/regions, institutions and authors, distribution of subject categories, core journals, research hotspots, and frontiers in this field. Results In the last 20 years, the annual number of publications has shown an increasing trend yearly. The USA, China, and South Korea have achieved fruitful research results in this field, among which Duke University and Chinese Academy of Sciences are the main research institutions. Rick G Schnellmann, Claude A Piantadosi, and Hagir B Suliman are the top three authors in terms of number of publications, while RC Scarpulla, ZD Wu, and P Puigserver are the top three authors in terms of cocitation frequency. PLOS One, Biochemical and Biophysical Research Communications, and Journal of Biological Chemistry are the top three journals in terms of number of articles published. Three papers published by Richard C Scarpulla have advanced this field and are important literature for understanding the field. Mechanistic studies on mitochondrial biosynthesis have been a long-standing hot topic; the main keywords include skeletal muscle, oxidative stress, gene expression, activation, and nitric oxide, and autophagy and apoptosis have been important research directions in recent years. Conclusion These results summarize the major research findings in the field of mitochondrial biogenesis over the past 20 years in various aspects, highlighting the major research hotspots and possible future research directions and helping researchers to quickly grasp the overview of the developments in this field.
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9
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Graziul C, Belikov A, Chattopadyay I, Chen Z, Fang H, Girdhar A, Jia X, Krafft PM, Kleiman-Weiner M, Lewis C, Liang C, Muchovej J, Vientós A, Young M, Evans J. Does big data serve policy? Not without context. An experiment with in silico social science. COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY 2022; 29:188-219. [PMID: 36471867 PMCID: PMC9713146 DOI: 10.1007/s10588-022-09362-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/24/2022] [Indexed: 06/17/2023]
Abstract
The DARPA Ground Truth project sought to evaluate social science by constructing four varied simulated social worlds with hidden causality and unleashed teams of scientists to collect data, discover their causal structure, predict their future, and prescribe policies to create desired outcomes. This large-scale, long-term experiment of in silico social science, about which the ground truth of simulated worlds was known, but not by us, reveals the limits of contemporary quantitative social science methodology. First, problem solving without a shared ontology-in which many world characteristics remain existentially uncertain-poses strong limits to quantitative analysis even when scientists share a common task, and suggests how they could become insurmountable without it. Second, data labels biased the associations our analysts made and assumptions they employed, often away from the simulated causal processes those labels signified, suggesting limits on the degree to which analytic concepts developed in one domain may port to others. Third, the current standard for computational social science publication is a demonstration of novel causes, but this limits the relevance of models to solve problems and propose policies that benefit from the simpler and less surprising answers associated with most important causes, or the combination of all causes. Fourth, most singular quantitative methods applied on their own did not help to solve most analytical challenges, and we explored a range of established and emerging methods, including probabilistic programming, deep neural networks, systems of predictive probabilistic finite state machines, and more to achieve plausible solutions. However, despite these limitations common to the current practice of computational social science, we find on the positive side that even imperfect knowledge can be sufficient to identify robust prediction if a more pluralistic approach is applied. Applying competing approaches by distinct subteams, including at one point the vast TopCoder.com global community of problem solvers, enabled discovery of many aspects of the relevant structure underlying worlds that singular methods could not. Together, these lessons suggest how different a policy-oriented computational social science would be than the computational social science we have inherited. Computational social science that serves policy would need to endure more failure, sustain more diversity, maintain more uncertainty, and allow for more complexity than current institutions support.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - John Muchovej
- MIT, Cambridge, USA
- Harvard University, Cambridge, USA
| | | | | | - James Evans
- University of Chicago, Chicago, USA
- Santa Fe Institute, Santa Fe, USA
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Schwitter N, Pretari A, Marwa W, Lombardini S, Liebe U. Big data and development sociology: An overview and application on governance and accountability through digitalization in Tanzania. FRONTIERS IN SOCIOLOGY 2022; 7:909458. [PMID: 36466797 PMCID: PMC9712952 DOI: 10.3389/fsoc.2022.909458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/24/2022] [Indexed: 06/17/2023]
Abstract
The digital revolution and the widespread use of the internet have changed many realms of empirical social science research. In this paper, we discuss the use of big data in the context of development sociology and highlight its potential as a new source of data. We provide a brief overview of big data and development research, discuss different data types, and review example studies, before introducing our case study on active citizenship in Tanzania which expands on an Oxfam-led impact evaluation. The project aimed at improving community-driven governance and accountability through the use of digital technology. Twitter and other social media platforms were introduced to community animators as a tool to hold national and regional key stakeholders accountable. We retrieve the complete Twitter timelines up to October 2021 from all ~200 community animators and influencers involved in the project (over 1.5 million tweets). We find that animators have started to use Twitter as part of the project, but most have stopped tweeting in the long term. Employing a dynamic difference-in-differences design, we also do not find effects of Oxfam-led training workshops on different aspects of animators' tweeting behavior. While most animators have stopped using Twitter in the long run, a few have continued to use social media to raise local issues and to be part of conversations to this day. Our case study showcases how (big) social media data can be part of an intervention, and we end with recommendations on how to use digital data in development sociology.
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Affiliation(s)
- Nicole Schwitter
- Department of Sociology, University of Warwick, Coventry, United Kingdom
| | | | | | | | - Ulf Liebe
- Department of Sociology, University of Warwick, Coventry, United Kingdom
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Arseniev-Koehler A, Foster JG. Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat. SOCIOLOGICAL METHODS & RESEARCH 2022; 51:1484-1539. [PMID: 37974911 PMCID: PMC10653277 DOI: 10.1177/00491241221122603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight "fill in the blanks" about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.
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Affiliation(s)
- Alina Arseniev-Koehler
- Department of Sociology, Purdue University, West Lafayette, IN, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
| | - Jacob G. Foster
- Department of Sociology, University of California, Los Angeles, Los Angeles, CA, USA
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Macanovic A. Text mining for social science - The state and the future of computational text analysis in sociology. SOCIAL SCIENCE RESEARCH 2022; 108:102784. [PMID: 36334929 DOI: 10.1016/j.ssresearch.2022.102784] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 06/16/2023]
Abstract
The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in sociological research. Recent work in sociology of culture, science, and economic sociology has shown how computational text analysis can be used in theory building and testing. This review starts with an introduction of the history of computer-assisted text analysis in sociology and then proceeds to discuss five families of computational methods used in contemporary research. Using exemplary studies, it shows how dictionary methods, semantic and network analysis tools, language models, unsupervised, and supervised machine learning can assist sociologists with different analytical tasks. After presenting recent methodological developments, this review summarizes several important implications of using large datasets and computational methods to infer complex meaning in texts. Finally, it calls researchers from different methodological traditions to adopt text mining tools while remaining mindful of lessons learned from working with conventional data and methods.
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Affiliation(s)
- Ana Macanovic
- Utrecht University, Department of Sociology / ICS, Padualaan 14, 3584 CH, Utrecht, The Netherlands.
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Alvero AJ, Pal J, Moussavian KM. Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2022; 5:1709-1734. [PMID: 36213757 PMCID: PMC9524730 DOI: 10.1007/s42001-022-00185-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
UNLABELLED Variation in college application materials related to social stratification is a contentious topic in social science and national discourse in the United States. This line of research has also started to use computational methods to consider qualitative materials, such as personal statements and letters of recommendation. Despite the prominence of this topic, fewer studies have considered a fairly common academic pathway: transferring. Approximately 40% of all college students in the US transfer schools at least once. One quirk of the system is that students from community colleges are applying for the same spots for students already enrolled in four year schools and trying to transfer. How might different aspects the transfer application itself correlate with institutional stratification and make students more or less distinguishable? We use a dataset of 20,532 transfer admissions essays submitted to the University of California system to describe how transfer applicants vary linguistically, culturally, and narratively with respect to academic pathways and essay prompts. Using a variety of methods for computational text analysis and qualitative coding, we find that essays written by community college students tend to be distinct from those written by university students. However, the strength and character of these results changed with the writing prompt provided to applicants. These results show how some forms of stratification, such as the type of school students attend, inform educational processes intended to equalize opportunity and how combining computational and human reading might illuminate these patterns. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s42001-022-00185-5.
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Affiliation(s)
- AJ Alvero
- University of Florida,
Gainesville, FL USA
| | - Jasmine Pal
- University of California, Los Angeles, Los Angeles, CA USA
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Li QQ, Yue Y, Gao QL, Zhong C, Barros J. Towards a new paradigm for segregation measurement in an age of big data. URBAN INFORMATICS 2022; 1:5. [PMID: 36124239 PMCID: PMC9458482 DOI: 10.1007/s44212-022-00003-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/11/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
Recent theoretical and methodological advances in activity space and big data provide new opportunities to study socio-spatial segregation. This review first provides an overview of the literature in terms of measurements, spatial patterns, underlying causes, and social consequences of spatial segregation. These studies are mainly place-centred and static, ignoring the segregation experience across various activity spaces due to the dynamism of movements. In response to this challenge, we highlight the work in progress toward a new paradigm for segregation studies. Specifically, this review presents how and the extent to which activity space methods can advance segregation research from a people-based perspective. It explains the requirements of mobility-based methods for quantifying the dynamics of segregation due to high movement within the urban context. It then discusses and illustrates a dynamic and multi-dimensional framework to show how big data can enhance understanding segregation by capturing individuals’ spatio-temporal behaviours. The review closes with new directions and challenges for segregation research using big data.
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Fan Y, Lehmann S, Blok A. Extracting the interdisciplinary specialty structures in social media data-based research: A clustering-based network approach. J Informetr 2022. [DOI: 10.1016/j.joi.2022.101310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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16
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Schünemann WJ, Brand A, König T, Ziegler J. Leveraging Dynamic Heterogeneous Networks to Study Transnational Issue Publics. The Case of the European COVID-19 Discourse on Twitter. FRONTIERS IN SOCIOLOGY 2022; 7:884640. [PMID: 35846866 PMCID: PMC9280175 DOI: 10.3389/fsoc.2022.884640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
The ongoing COVID-19 pandemic constitutes a critical phase for the transnationalization of public spheres. Against this backdrop, we ask how transnational COVID-19 related online discourse has been throughout the EU over the first year of the pandemic. Which events triggered higher transnational coherence or national structuration of this specific issue public on Twitter? In order to study these questions, we rely on Twitter data obtained from the TBCOV database, i.e., a dataset for multilingual, geolocated COVID-19 related Twitter communication. We selected corpora for the 27 member states of the EU plus the United Kingdom. We defined three research periods representing different phases of the pandemic, namely April (1st wave), August (interim) and December 2020 (2nd wave) resulting in a set of 51,893,966 unique tweets for comparative analysis. In order to measure the level and temporal variation of transnational discursive linkages, we conducted a spatiotemporal network analysis of so-called Heterogeneous Information Networks (HINs). HINs allow for the integration of multiple, heterogeneous network entities (hashtags, retweets, @-mentions, URLs and named entities) to better represent the complex discursive structures reflected in social media communication. Therefrom, we obtained an aggregate measure of transnational linkages on a daily base by relating these linkages back to their geolocated authors. We find that the share of transnational discursive linkages increased over the course of the pandemic, indicating effects of adaptation and learning. However, stringent political measures of crisis management at the domestic level (such as lockdown decisions) caused stronger national structuration of COVID-19 related Twitter discourse.
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Affiliation(s)
- Wolf J. Schünemann
- Institute of Social Sciences, Hildesheim University, Hildesheim, Germany
| | - Alexander Brand
- Institute of Social Sciences, Hildesheim University, Hildesheim, Germany
| | - Tim König
- Institute of Social Sciences, Hildesheim University, Hildesheim, Germany
| | - John Ziegler
- Institute of Computer Science, Heidelberg University, Heidelberg, Germany
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17
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Paraman P, Anamalah S. Ethical artificial intelligence framework for a good AI society: principles, opportunities and perils. AI & SOCIETY 2022. [DOI: 10.1007/s00146-022-01458-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Tindall D, McLevey J, Koop-Monteiro Y, Graham A. Big data, computational social science, and other recent innovations in social network analysis. CANADIAN REVIEW OF SOCIOLOGY = REVUE CANADIENNE DE SOCIOLOGIE 2022; 59:271-288. [PMID: 35286014 DOI: 10.1111/cars.12377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
While sociologists have studied social networks for about one hundred years, recent developments in data, technology, and methods of analysis provide opportunities for social network analysis (SNA) to play a prominent role in the new research world of big data and computational social science (CSS). In our review, we focus on four broad topics: (1) Collecting Social Network Data from the Web, (2) Non-traditional and Bipartite/Multi-mode Networks, including Discourse and Semantic Networks, and Social-Ecological Networks, (3) Recent Developments in Statistical Inference for Networks, and (4) Ethics in Computational Network Research.
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Affiliation(s)
- David Tindall
- Department of Sociology, University of British Columbia, Vancouver, British Columbia, Canada
| | - John McLevey
- Department of Knowledge Integration, University of Waterloo, Waterloo, Ontario, Canada
| | - Yasmin Koop-Monteiro
- Department of Sociology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Graham
- Department of Knowledge Integration, University of Waterloo, Waterloo, Ontario, Canada
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19
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Fu Q, Gu J, Zeng ZA, Tindall D. A manifesto for computational sociology: The Canadian perspective. CANADIAN REVIEW OF SOCIOLOGY = REVUE CANADIENNE DE SOCIOLOGIE 2022; 59:200-206. [PMID: 35445550 DOI: 10.1111/cars.12379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Qiang Fu
- Department of Sociology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jiaxin Gu
- Department of Sociology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Ziqing Amy Zeng
- Department of Sociology, University of Toronto, Toronto, Ontario, Canada
| | - David Tindall
- Department of Sociology, The University of British Columbia, Vancouver, British Columbia, Canada
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20
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Zhou H, Guns R, Engels TCE. Are social sciences becoming more interdisciplinary? Evidence from publications 1960–2014. J Assoc Inf Sci Technol 2022. [DOI: 10.1002/asi.24627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Hongyu Zhou
- Centre for R&D Monitoring (ECOOM), Faculty of Social Sciences University of Antwerp Antwerp Belgium
| | - Raf Guns
- Centre for R&D Monitoring (ECOOM), Faculty of Social Sciences University of Antwerp Antwerp Belgium
| | - Tim C. E. Engels
- Centre for R&D Monitoring (ECOOM), Faculty of Social Sciences University of Antwerp Antwerp Belgium
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21
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Ali I, Asif M, Hamid I, Sarwar MU, Khan FA, Ghadi Y. A word embedding technique for sentiment analysis of social media to understand the relationship between Islamophobic incidents and media portrayal of Muslim communities. PeerJ Comput Sci 2022; 8:e838. [PMID: 35494844 PMCID: PMC9044206 DOI: 10.7717/peerj-cs.838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 12/09/2021] [Indexed: 06/14/2023]
Abstract
Islamophobia is a sentiment against the Muslim community; recently, atrocities towards Muslim communities witnessed this sentiment globally. This research investigates the correlation between how news stories covered by mainstream news channels impede the hate speech/Islamophobic sentiment. To examine the objective mentioned above, we shortlisted thirteen mainstream news channels and the ten most widely reported Islamophobic incidents across the globe for experimentation. Transcripts of the news stories are scraped along with their comments, likes, dislikes, and recommended videos as the users' responses. We used a word embedding technique for sentiment analysis, e.g., Islamophobic or not, three textual variables, video titles, video transcripts, and comments. This sentiment analysis helped to compute metric variables. The I-score represents the extent of portrayals of Muslims in a particular news story. The next step is to calculate the canonical correlation between video transcripts and their respective responses, explaining the relationship between news portrayal and hate speech. This study provides empirical evidence of how news stories can promote Islamophobic sentiments and eventually atrocities towards Muslim communities. It also provides the implicit impact of reporting news stories that may impact hate speech and crime against specific communities.
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Affiliation(s)
- Ishfaq Ali
- Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan
| | - Muhammad Asif
- Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan
| | - Isma Hamid
- Department of Computer Science, National Textile University, Faisalabad, Punjab, Pakistan
| | - Muhammad Umer Sarwar
- Department of Computer Science, Government College University, Faisalabad, Punjab, Pakistan
| | - Fakhri Alam Khan
- Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Yazeed Ghadi
- Department of Software Engineering/Computer Science, Al Ain University, Al Ain, UAE
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22
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Goekoop R, de Kleijn R. Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1701. [PMID: 34946007 PMCID: PMC8700347 DOI: 10.3390/e23121701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 12/10/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of 'disorder' (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, 'losing control' appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away.
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Affiliation(s)
- Rutger Goekoop
- Parnassia Group, PsyQ Parnassia Academy, Department of Anxiety Disorders, Early Detection and Intervention Team (EDIT), Lijnbaan 4, 2512 VA Den Haag, The Netherlands
| | - Roy de Kleijn
- Cognitive Psychology Unit, Institute of Psychology & Leiden Institute for Brain and Cognition, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands;
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23
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Gil-Clavel S, Zagheni E, Bordone V. Close Social Networks Among Older Adults: The Online and Offline Perspectives. POPULATION RESEARCH AND POLICY REVIEW 2021. [DOI: 10.1007/s11113-021-09682-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractQualitative studies have found that the use of Information and Communication Technologies is related to an enhanced quality of life for older adults, as these technologies might act as a medium to access social capital regardless of geographical distance. In order to quantitatively study the association between older people’s characteristics and the likelihood of having a network of close friends offline and online, we use data from the Survey of Health, Ageing and Retirement in Europe and data from Facebook. Using a novel approach to analyze aggregated and anonymous Facebook data within a regression framework, we show that the associations between having close friends and age, sex, and being a parent are the same offline and online. Migrants who use internet are less likely to have close friends offline, but migrants who are Facebook users are more likely to have close friends online, suggesting that digital relationships may compensate for the potential lack of offline close friendships among older migrants.
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24
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Levy DE, Pachucki MC, O'Malley AJ, Porneala B, Yaqubi A, Thorndike AN. Social connections and the healthfulness of food choices in an employee population. Nat Hum Behav 2021; 5:1349-1357. [PMID: 33888881 PMCID: PMC8530824 DOI: 10.1038/s41562-021-01103-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 03/23/2021] [Indexed: 02/02/2023]
Abstract
Unhealthy food choice is an important driver of obesity, but research examining the relationship of food choices and social influence has been limited. We sought to assess associations in the healthfulness of workplace food choices among a large population of diverse employees whose food-related social connections were identified using passively collected data in a validated model. Data were drawn from 3 million encounters where pairs of employees made purchases together in 2015-2016. The healthfulness of food items was defined by 'traffic light' labels. Cross-sectional simultaneously autoregressive models revealed that proportions of both healthy and unhealthy items purchased were positively associated between connected employees. Longitudinal generalized estimating equation models also found positive associations between an employee's current food purchase and the most recent previous food purchase a coworker made together with the employee. These data indicate that workplace interventions to promote healthy eating and reduce obesity should test peer-based strategies.
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Affiliation(s)
- Douglas E Levy
- Mongan Institute Health Policy Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Mark C Pachucki
- Department of Sociology & Computational Social Science Institute, University of Massachusetts, Amherst, Amherst, MA, USA
| | - A James O'Malley
- Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Awesta Yaqubi
- Boston University School of Medicine, Boston, MA, USA
| | - Anne N Thorndike
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
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25
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Uitermark J, van Meeteren M. Geographical Network Analysis. TIJDSCHRIFT VOOR ECONOMISCHE EN SOCIALE GEOGRAFIE = JOURNAL OF ECONOMIC AND SOCIAL GEOGRAPHY = REVUE DE GEOGRAPHIE ECONOMIQUE ET HUMAINE = ZEITSCHRIFT FUR OKONOMISCHE UND SOZIALE GEOGRAPHIE = REVISTA DE GEOGRAFIA ECONOMICA Y SOCIAL 2021; 112:337-350. [PMID: 34594058 PMCID: PMC8459335 DOI: 10.1111/tesg.12480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/31/2021] [Accepted: 04/09/2021] [Indexed: 06/13/2023]
Abstract
As the volume of digital data is growing exponentially and computational methods are advancing rapidly, network analysis is an increasingly important analytical tool to understand social life. This paper revisits the rich history of network analysis in geography and uses insights from that history to review contemporary computational social science. Based on that analysis, we synthesize the distinctive qualities of what we term geographical network analysis. Geographical network analysis presumes that networks are situated, construed through meaning, and reflect power relations. Instead of pursuing parsimonious explanations or universal theories, geographical network analysis strives to understand how uneven networks develop across space and within place through a constant back and forth between abstraction and contextualization. Drawing on the articles in this special issue, this paper illustrates how geographical network analysis can be put to work.
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Affiliation(s)
- Justus Uitermark
- Department of Geography, Planning, and International Development StudiesUniversity of AmsterdamAmsterdamthe Netherlands
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26
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Gualda E. Altruism, Solidarity and Responsibility from a Committed Sociology: Contributions to Society. THE AMERICAN SOCIOLOGIST 2021; 53:29-43. [PMID: 34376856 PMCID: PMC8342653 DOI: 10.1007/s12108-021-09504-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 06/13/2023]
Abstract
A careful look at the international development of Sociology highlights the centrality that the study of social problems and the approach to possible solutions to them have had in the history of this discipline, not infrequently for the sake of better social integration, stability, development, social change or even modernity. Recent approaches suggest shifting this focus of attention, arguing about the deficit in sociological research and practice concerning theor etical frameworks that pay attention to the positive aspects. This text reflects on the contributions that altruism, solidarity, and collective responsibility can have to improve the quality of life in contemporary societies and face humanitarian emergencies with a certain degree of success. For instance, the so-called refugee crisis or the current COVID-19 pandemic poses significant challenges for societies. This article also explores briefly new roles of data science in connection with responsibility and altruism. The text invites us to revisit sociology, thinking about the lights more than the shadows.
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Affiliation(s)
- Estrella Gualda
- ESEIS/COIDESO [ESEIS, Social Studies and Social Intervention Research Centre; COIDESO, Centro de Investigación en Pensamiento Contemporáneo e Innovación para el Desarrollo Social], Universidad de Huelva, Huelva, Spain
- Facultad de Trabajo Social, Universidad de Huelva, Campus El Carmen. Avda. Tres de Marzo, 21071 Huelva, Spain
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27
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Hannigan TR, Briggs AR, Valadao R, Seidel MDL, Jennings PD. A new tool for policymakers: Mapping cultural possibilities in an emerging AI entrepreneurial ecosystem. RESEARCH POLICY 2021. [DOI: 10.1016/j.respol.2021.104315] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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28
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Galesic M, Bruine de Bruin W, Dalege J, Feld SL, Kreuter F, Olsson H, Prelec D, Stein DL, van der Does T. Human social sensing is an untapped resource for computational social science. Nature 2021; 595:214-222. [PMID: 34194037 DOI: 10.1038/s41586-021-03649-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/17/2021] [Indexed: 02/06/2023]
Abstract
The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.
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Affiliation(s)
- Mirta Galesic
- Santa Fe Institute, Santa Fe, NM, USA. .,Complexity Science Hub Vienna, Vienna, Austria. .,Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA. .,Harding Center for Risk Literacy, University of Potsdam, Potsdam, Germany.
| | - Wändi Bruine de Bruin
- Sol Price School of Public Policy, University of South California, Los Angeles, CA, USA
| | | | - Scott L Feld
- Department of Sociology, Purdue University, West Lafayette, IN, USA
| | - Frauke Kreuter
- Joint Program in Survey Methodology, University of Maryland, Maryland, MD, USA.,Ludwig Maximilians Universität München, München, Germany
| | | | - Drazen Prelec
- Sloan School of Management, MIT, Cambridge, MA, USA.,Department of Economics, MIT, Cambridge, MA, USA.,Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Daniel L Stein
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
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29
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Burton JW, Cruz N, Hahn U. Reconsidering evidence of moral contagion in online social networks. Nat Hum Behav 2021; 5:1629-1635. [PMID: 34112981 DOI: 10.1038/s41562-021-01133-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/04/2021] [Indexed: 11/09/2022]
Abstract
The ubiquity of social media use and the digital data traces it produces has triggered a potential methodological shift in the psychological sciences away from traditional, laboratory-based experimentation. The hope is that, by using computational social science methods to analyse large-scale observational data from social media, human behaviour can be studied with greater statistical power and ecological validity. However, current standards of null hypothesis significance testing and correlational statistics seem ill-suited to markedly noisy, high-dimensional social media datasets. We explore this point by probing the moral contagion phenomenon, whereby the use of moral-emotional language increases the probability of message spread. Through out-of-sample prediction, model comparisons and specification curve analyses, we find that the moral contagion model performs no better than an implausible XYZ contagion model. This highlights the risks of using purely correlational evidence from large observational datasets and sounds a cautionary note for psychology's merge with big data.
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Affiliation(s)
- Jason W Burton
- Department of Psychological Sciences, Birkbeck, University of London, London, UK.
| | - Nicole Cruz
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
| | - Ulrike Hahn
- Department of Psychological Sciences, Birkbeck, University of London, London, UK
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30
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Abstract
AbstractIn this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.
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