The DOI matches its Crossref record.
Social Network Analysis: Methods and Applications
Abstract
Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on relationships among social entities and is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. It is a reference book that can be used by those who want a comprehensive review of network methods, or by researchers who have gathered network data and want to find the most appropriate method by which to analyze it. It is also intended for use as a textbook as it is the first book to provide comprehensive coverage of the methodology and applications of the field.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Social network data were collected using widely accepted sociometric techniques (Wasserman and Faust 1994).”
From Enterprise System Implementation and Employee Job Performance: Understanding the Role of Advice Networks1 (2014) · cites this paper as Wasserman and Faust 1994
In the citing paper
“These include degree centrality, 5 https://qgis.org/api/qgsalgorithmextractbylocation_8h_source.html closeness centrality, and betweenness centrality (Wasserman and Faust, 1994;Freeman, 1979.”
From POI VizNet: New QGIS Tool to construct Visibility Networks in Cities (2024) · cites this paper as Wasserman and Faust, 1994
In the citing paper
“Network analysis metrics come in two flavours, global and local (Wasserman and Faust 1994;Collar et al. 2015).”
From Modelling of pathways and movement networks in archaeology: an overview of current approaches (2019) · cites this paper as Wasserman and Faust 1994
In the citing paper
“Firms may gain such an understanding by using social network analysis (Wasserman and Faust 1994), value blueprints (Adner 2012), or agent-based modeling (Gilbert 2008).”
From Stakeholder marketing: theoretical foundations and required capabilities (2015) · cites this paper as Wasserman and Faust 1994
Citations
Cited by
Sorted by citations
Items 101–200 of 1,053
Logit Models and Logistic Regressions for Social Networks: I. An Introduction to Markov Graphs and p
Wasserman, S., & Pattison, P. (1996) · Psychometrika · doi:10.1007/bf02294547 · Cited by 950
Self-citationCollaborative Networks as Determinants of Knowledge Diffusion Patterns
Singh, J. (2005) · Management Science · doi:10.1287/mnsc.1040.0349 · Cited by 1,192
Increasing Vaccination: Putting Psychological Science Into Action
Brewer, N. T., Chapman, G. B., Rothman, A. J., Leask, J., & Kempe, A. (2017) · Psychological Science in the Public Interest · doi:10.1177/1529100618760521 · Cited by 1,233
Scale-free networks in cell biology
Albert, R. (2005) · Journal of Cell Science · doi:10.1242/jcs.02714 · Cited by 1,237
Stam, W., & Elfring, T. (2008) · Academy of Management Journal · doi:10.5465/amj.2008.30744031 · Cited by 1,218
SciMAT : A new science mapping analysis software tool
Cobo, M. J., López-Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22688 · Cited by 1,360
Connectivity of Growing Random Networks
Krapivsky, P. L., Redner, S., & Leyvraz, F. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.4629 · Cited by 1,210
Theorising interventions as events in systems
Hawe, P., Shiell, A., & Riley, T. (2009) · American Journal of Community Psychology · doi:10.1007/s10464-009-9229-9 · Cited by 1,143
Watts, D. J. (2004) · Annual Review of Sociology · doi:10.1146/annurev.soc.30.020404.104342 · Cited by 1,168
Eigenvector-like measures of centrality for asymmetric relations
Bonacich, P., & Lloyd, P. (2001) · Social Networks · doi:10.1016/s0378-8733(01)00038-7 · Cited by 1,161
Identification and classification of hubs in brain networks
Sporns, O., Honey, C. J., & Kötter, R. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001049 · Cited by 1,165
Colleoni, E., Rozza, A., & Arvidsson, A. (2014) · Journal of Communication · doi:10.1111/jcom.12084 · Cited by 1,151
Contextualization and value-in-context: How context frames exchange
Chandler, J. D., & Vargo, S. L. (2011) · Marketing Theory · doi:10.1177/1470593110393713 · Cited by 1,161
Why is economic geography not an evolutionary science? Towards an evolutionary economic geography
Boschma, R. A., & Frenken, K. (2006) · Journal of Economic Geography · doi:10.1093/jeg/lbi022 · Cited by 1,232
A survey of heterogeneous information network analysis
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2598561 · Cited by 1,148
Synchronization in scale-free dynamical networks: robustness and fragility
Wang, X. F., & Chen, G. (2002) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/81.974874 · Cited by 1,129
Valente, T. W. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195301014.003.0010 · Cited by 1,143
Studying Online Social Networks
Garton, L., Haythornthwaite, C., & Wellman, B. (2006) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.1997.tb00062.x · Cited by 1,161
Güler, I., Guillén, M. F., & Macpherson, J. M. (2002) · Administrative Science Quarterly · doi:10.2307/3094804 · Cited by 1,083
Bipartite network projection and personal recommendation
Zhou, T., Ren, J., Medo, M., & Zhang, Y. (2007) · Physical Review E · doi:10.1103/physreve.76.046115 · Cited by 1,093
Structure and evolution of online social networks
Kumar, R., Novak, J., & Tomkins, A. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150476 · Cited by 1,091
Building Process Theory with Narrative: From Description to Explanation
Pentland, B. T. (1999) · Academy of Management Review · doi:10.5465/amr.1999.2553249 · Cited by 1,311
Diani, M., & McAdam, D. (2003) · doi:10.1093/0199251789.001.0001 · Cited by 1,492
Knowledge, Networks, and Knowledge Networks
Phelps, C., Heidl, R. A., & Wadhwa, A. (2012) · Journal of Management · doi:10.1177/0149206311432640 · Cited by 1,163
Cooperative Networks and Competitive Dynamics: A Structural Embeddedness Perspective
Gnyawalị, D. R., & Madhavan, R. (2001) · Academy of Management Review · doi:10.5465/amr.2001.4845820 · Cited by 1,285
Functional Molecular Ecological Networks
Zhou, J. Z., Deng, Y., Luo, F., He, Z., Tu, Q., & Zhi, X. (2010) · mBio · doi:10.1128/mbio.00169-10 · Cited by 1,039
Friends or Strangers? Firm-Specific Uncertainty, Market Uncertainty, and Network Partner Selection
Beckman, C. M., Haunschild, P. R., & Phillips, D. J. (2004) · Organization Science · doi:10.1287/orsc.1040.0065 · Cited by 1,003
Pinning control of scale-free dynamical networks
Wang, X. F., & Chen, G. (2002) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)00772-0 · Cited by 1,013
Network Analysis for International Relations
Hafner-Burton, E. M., Kahler, M., & Montgomery, A. H. (2009) · International Organization · doi:10.1017/s0020818309090195 · Cited by 1,030
Lavie, D. (2007) · Strategic Management Journal · doi:10.1002/smj.637 · Cited by 999
Detecting rich-club ordering in complex networks
Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006) · Nature Physics · doi:10.1038/nphys209 · Cited by 1,001
8. Dynamic Networks and Behavior: Separating Selection from Influence
Steglich, C., Snijders, T. A. B., & Pearson, M. A. (2010) · Sociological Methodology · doi:10.1111/j.1467-9531.2010.01225.x · Cited by 1,003
Getoor, L. C., & Diehl, C. (2005) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1117454.1117456 · Cited by 986
Stakeholder Analysis and Social Network Analysis in Natural Resource Management
Prell, C., Hubacek, K., & Reed, M. S. (2009) · Society & Natural Resources · doi:10.1080/08941920802199202 · Cited by 1,064
Performance of modularity maximization in practical contexts
Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937
What is an emerging technology?
Rotolo, D., Hicks, D. M., & Martin, B. R. (2015) · Research Policy · doi:10.1016/j.respol.2015.06.006 · Cited by 971
“…at the time t+ 1 and t+ 2 out of the number of times the term occurred at t− 1, 11A bipartite network is a network of which nodes can be partitioned into two distinct groups, N1 and N2, and all the links connect one node from N1 with a node from N2, or vice versa (Wassermann and Faust, 1994).”
Mathematical formulation of multilayer networks
De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2016) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 939
Persuasion Bias, Social Influence, and Unidimensional Opinions
DeMarzo, P. M., Vayanos, D., & Zwiebel, J. (2003) · The Quarterly Journal of Economics · doi:10.1162/00335530360698469 · Cited by 945
Influential Nodes in a Diffusion Model for Social Networks
Kempe, D., Kleinberg, J. M., & Tardos, É. (2005) · Lecture notes in computer science · doi:10.1007/11523468_91 · Cited by 957
Phelps, C. (2010) · Academy of Management Journal · doi:10.5465/amj.2010.52814627 · Cited by 96
Multirelational organization of large-scale social networks in an online world
Szell, M., Lambiotte, R., & Stefan Thurner, A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004008107 · Cited by 926
Social influence analysis in large-scale networks
Tang, J., Sun, J., Wang, C., & Yang, Z. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557108 · Cited by 978
Social Networks and Health: Models, Methods, and Applications
Valente, T. W. (2010) · doi:10.1093/acprof:oso/9780195301014.001.0001 · Cited by 933
Mathematical Formulation of Multilayer Networks
De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013) · Physical Review X · doi:10.1103/physrevx.3.041022 · Cited by 930
“fields such as engineering [34,35] and sociology [5,36,37].”
BackgroundInfluentialOrigins of Homophily in an Evolving Social Network
Kossinets, G., & Watts, D. J. (2009) · American Journal of Sociology · doi:10.1086/599247 · Cited by 912
A tool for filtering information in complex systems
Tumminello, M., Aste, T., Matteo, T. D., & Mantegna, R. N. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500298102 · Cited by 903
Constructing, conducting and interpreting animal social network analysis
Farine, D. R., & Whitehead, H. (2015) · Journal of Animal Ecology · doi:10.1111/1365-2656.12418 · Cited by 966
Balkundi, P., & Harrison, D. A. (2006) · Academy of Management Journal · doi:10.5465/amj.2006.20785500 · Cited by 941
What’s Different about Social Media Networks? A Framework and Research Agenda1
Kane, G. C., Alavi, M., Labianca, G., & Borgatti, S. P. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.13 · Cited by 923
“may differ in type, number, symmetry, strength, and affect (Wasserman and Faust 1994), yet little research accounts for the potential implications of these various design characteristics on”
BackgroundThe Emergence of Governance in an Open Source Community
O'Mahony, S., & Ferraro, F. (2007) · Academy of Management Journal · doi:10.5465/amj.2007.27169153 · Cited by 909
A computational model of trust and reputation
Mui, L., Mohtashemi, M., & Halberstadt, A. (2003) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2002.994181 · Cited by 923
The Genesis and Dynamics of Organizational Networks
Ahuja, G., Soda, G., & Zaheer, A. (2011) · Organization Science · doi:10.1287/orsc.1110.0695 · Cited by 904
Network Learning: The Effects of Partners' Heterogeneity of Experience on Corporate Acquisitions
Beckman, C. M., & Haunschild, P. R. (2002) · Administrative Science Quarterly · doi:10.2307/3094892 · Cited by 893
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Marlow, C. A., Naaman, M., boyd, D., & Davis, M. (2006) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/1149941.1149949 · Cited by 890
Evolutionary dynamics of group interactions on structured populations: a review
Perz, M., Gómez‐Gardeñes, J., Szolnoki, A., Florı́a, L. M., & Moreno, Y. (2013) · Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) · Cited by 883
Geographic routing in social networks
Liben‐Nowell, D., Novak, J., Kumar, R., Raghavan, P., & Tomkins, A. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503018102 · Cited by 843
Xu, X., Yuruk, N., Feng, Z., & Schweiger, T. A. J. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281280 · Cited by 855
Kautz, H., Selman, B., & Shah, M. A. (1997) · Communications of the ACM · doi:10.1145/245108.245123 · Cited by 853
Network topology of the interbank market
Boss, M., Elsinger, H., Summer, M., & Stefan Thurner, A. (2004) · Quantitative Finance · doi:10.1080/14697680400020325 · Cited by 828
How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature
Small, M. L. (2011) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102657 · Cited by 829
Organizational Ambidexterity: Towards a Multilevel Understanding
Şimşek, Z. (2009) · Journal of Management Studies · doi:10.1111/j.1467-6486.2009.00828.x · Cited by 892
The Network Analysis of Urban Streets: A Primal Approach
Porta, S., Crucitti, P., & Latora, V. (2006) · Environment and Planning B Planning and Design · doi:10.1068/b32045 · Cited by 870
Game theory meets network security and privacy
Manshaei, M. H., Zhu, Q., Alpcan, T., Bacşar, T., & Hubaux, J. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480742 · Cited by 818
Limiting the spread of misinformation in social networks
Budak, C., Agrawal, D., & Abbadi, A. E. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963499 · Cited by 825
A plea for the ‘de-migranticization' of research on migration and integration
Dahinden, J. (2016) · Ethnic and Racial Studies · doi:10.1080/01419870.2015.1124129 · Cited by 906
Communities, modules and large-scale structure in networks
Newman, M. E. J. (2012) · Nature Physics · doi:10.1038/nphys2162 · Cited by 787
Dissecting the Social: On the Principles of Analytical Sociology
Hedstrom, P. (2005) · doi:10.1017/cbo9780511488801 · Cited by 816
An information-theoretic framework for resolving community structure in complex networks
Rosvall, M., & Bergstrom, C. T. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611034104 · Cited by 789
Public Service Motivation: A Systematic Literature Review and Outlook
Ritz, A., Brewer, G. A., & Neumann, O. (2016) · Public Administration Review · doi:10.1111/puar.12505 · Cited by 944
Clustering in complex directed networks
Fagiolo, G. (2007) · Physical Review E · doi:10.1103/physreve.76.026107 · Cited by 816
Analyzing and modeling real-world phenomena with complex networks: a survey of applications
Costa, L. D. F., Oliveira, O. N., Travieso, G., Rodrigues, F. A., Villas-Boas, P. R., Antiqueira, L., Viana, M. P., & Rocha, L. E. C. (2011) · Advances In Physics · doi:10.1080/00018732.2011.572452 · Cited by 839
The stability of centrality measures when networks are sampled
Costenbader, E., & Valente, T. W. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00012-1 · Cited by 821
Deriving Value from Social Commerce Networks
Stephen, A. T., & Toubia, O. (2010) · Journal of Marketing Research · doi:10.1509/jmkr.47.2.215 · Cited by 902
An integrated trust and reputation model for open multi-agent systems
Huynh, T. D., Jennings, N. R., & Shadbolt, N. R. (2006) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-005-6825-4 · Cited by 794
Peer Effects and Social Networks in Education
Calvó‐Armengol, A., Patacchini, E., & Zénou, Y. (2009) · The Review of Economic Studies · doi:10.1111/j.1467-937x.2009.00550.x · Cited by 823
Finding local community structure in networks
Clauset, A. J. (2005) · Physical Review E · doi:10.1103/physreve.72.026132 · Cited by 789
Expertise networks in online communities
Zhang, J., Ackerman, M. S., & Adamic, L. A. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242603 · Cited by 797
Model-Based Clustering for Social Networks
Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2007.00471.x · Cited by 759
Knowledge Sharing in Organizations: Multiple Networks, Multiple Phases
Hansen, M. T., Mors, M. L., & Løvås, B. (2005) · Academy of Management Journal · doi:10.5465/amj.2005.18803922 · Cited by 783
Models of social networks based on social distance attachment
Boguñá, M., Pastor‐Satorras, R., Dı́az-Guilera, A., & Arenas, À. (2004) · Physical Review E · doi:10.1103/physreve.70.056122 · Cited by 764
Supply network disruption and resilience: A network structural perspective
Kim, Y., Chen, Y., & Linderman, K. (2014) · Journal of Operations Management · doi:10.1016/j.jom.2014.10.006 · Cited by 817
Structural investigation of supply networks: A social network analysis approach
Kim, Y., Choi, T. Y., Yan, T., & Dooley, K. J. (2010) · Journal of Operations Management · doi:10.1016/j.jom.2010.11.001 · Cited by 805
Jamali, M., & Ester, M. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557067 · Cited by 771
Structural measures for multiplex networks
Battiston, F., Nicosia, V., & Latora, V. (2014) · Physical Review E · doi:10.1103/physreve.89.032804 · Cited by 771
Co‐Authorship in Management and Organizational Studies: An Empirical and Network Analysis*
Acedo, F. J., Castro, C. B., Casanueva, C., & González, J. L. G. (2006) · Journal of Management Studies · doi:10.1111/j.1467-6486.2006.00625.x · Cited by 805
Comparing innovation systems: a framework and application to China’s transitional context
Liu, X., & White, S. C. (2001) · Research Policy · doi:10.1016/s0048-7333(00)00132-3 · Cited by 787
Ducheneaut, N., Yee, N., Nickell, E., & Moore, R. J. (2006) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124834 · Cited by 778
Quantum simulation of frustrated Ising spins with trapped ions
Kim, K., Chang, M.-S., Korenblit, S., Islam, R., Edwards, E. E., Freericks, J. K., Lin, G.-D., Duan, L.-M., & Monroe, C. (2010) · Nature · doi:10.1038/nature09071 · Cited by 750
SPECTRAL CLUSTERING AND THE HIGH-DIMENSIONAL STOCHASTIC BLOCKMODEL
Rohe, K., Chatterjee, S., & Yu, B. (2010) · Cited by 782
Cartography of complex networks: modules and universal roles
Guimerà, R., & Amaral, L. A. N. (2005) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2005/02/p02001 · Cited by 768
Evolutionary games on multilayer networks: a colloquium
Wang, Z., Wang, L., Szolnoki, A., & Perc, M. (2015) · The European Physical Journal B · doi:10.1140/epjb/e2015-60270-7 · Cited by 811
“The rapidly growing availability of human generated data [66], together with the everincreasing computational capabilities continue to drive progress in this field, in turn leading to the development of new and more complex theoretical models that are able to accurately describe certain aspects of reality, as reviewed…”
BackgroundStrong, Weak, and Latent Ties and the Impact of New Media
Haythornthwaite, C. (2002) · The Information Society · doi:10.1080/01972240290108195 · Cited by 750
Resources, Knowledge and Influence: The Organizational Effects of Interorganizational Collaboration*
Hardy, C., Phillips, N., & Lawrence, T. B. (2003) · Journal of Management Studies · doi:10.1111/1467-6486.00342 · Cited by 741
Clusters, networks, and firm innovativeness
Bell, G. G. (2005) · Strategic Management Journal · doi:10.1002/smj.448 · Cited by 771
Social Capital and Value Creation: The Role of Intrafirm Networks
Tsai, W., & Ghoshal, S. (1998) · Academy of Management Journal · doi:10.5465/257085 · Cited by 739
Generalizations of the clustering coefficient to weighted complex networks
Saramäki, J., Kivelä, M., Onnela, J.-P. “., Kaski, K. K., & Kertész, J. (2007) · Physical Review E · doi:10.1103/physreve.75.027105 · Cited by 727
Small, M. L. (2009) · doi:10.1093/acprof:oso/9780195384352.001.0001 · Cited by 752
Communicability in complex networks
Estrada, E., & Hatano, N. (2008) · Physical Review E · doi:10.1103/physreve.77.036111 · Cited by 719
Newman, M. E. J. (2000) · Journal of Statistical Physics · doi:10.1023/a:1026485807148 · Cited by 719
Preserving Privacy in Social Networks Against Neighborhood Attacks
Zhou, B., & Pei, J. (2008) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2008.4497459 · Cited by 731
Metadata
- Authors
- Wasserman, S., & Faust, K.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- University of South Carolina (US)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1994-11-25
- OpenAlex
- W950821216
- Cited by
- 17,557
- References
- 0
- Citations by year
- 2026: 619 · 2025: 780 · 2024: 747 · 2023: 763 · 2022: 783 · 2021: 931 · 2020: 907 · 2019: 898 · 2018: 955 · 2017: 891 · 2016: 877 · 2015: 872 · 2014: 974 · 2013: 947 · 2012: 856
- FWCI
- 10.62
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Social Capital and Networks
- Social Sciences › Sociology and Political Science › Social Power and Status Dynamics
- Keywords
- social network analysis, network methods, network data analysis, social networks, behavioral research, marketing, industrial engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review