research-article

Polarization and Fake News: Early Warning of Potential Misinformation Targets

Authors:

Michela Del Vicario,

Walter Quattrociocchi,

Fabiana ZolloAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 13, Issue 2

Article No.: 10, Pages 1 - 22

https://doi.org/10.1145/3316809

Published: 27 March 2019 Publication History

Abstract

Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users’ behavior on online social media such as Facebook, making a first, important step towards the mitigation of misinformation phenomena by supporting the identification of potential misinformation targets and thus the design of tailored counter-narratives.

References

[1]

Statista. 2018. Number of monthly active Facebook users worldwide as of 3rd quarter 2017 (in millions). Retrieved from https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/.

[2]

Oxford Dictionaries. 2017. Oxford dictionaries word of the year 2016 is...post-truth. Retrieved from https://www.oxforddictionaries.com/press/news/2016/12/11/WOTY-16.

[3]

Robert Allen. 2017. What happens online in 60 seconds? Retrieved from https://www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds/.

[4]

Nic Newman, Richard Fletcher, Antonis Kalogeropoulos, David AL Levy, and Rasmus Kleis Nielsen. 2017. Reuters digital news report.

[5]

Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, and Walter Quattrociocchi. 2016. The spreading of misinformation online. Proc. Natl. Acad. Sci. 113, 3 (2016), 554--559.

[6]

Ana LucÃa Schmidt, Fabiana Zollo, Michela Del Vicario, Alessandro Bessi, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, and Walter Quattrociocchi. 2017. Anatomy of news consumption on Facebook. Proc. Natl. Acad. Sci. 114, 12 (2017).

[7]

Michela Del Vicario, Fabiana Zollo, Guido Caldarelli, Antonio Scala, and Walter Quattrociocchi. 2017. Mapping social dynamics on Facebook: The Brexit debate. Soc. Netw. 50, Supplement C (2017), 6--16.

[8]

Ana Lucηa Schmidt, Fabiana Zollo, Antonio Scala, Cornelia Betsch, and Walter Quattrociocchi. 2018. Polarization of the vaccination debate on Facebook. Vaccine 36, 25 (2018), 3,606--3,612.

[9]

Fabiana Zollo, Alessandro Bessi, Michela Del Vicario, Antonio Scala, Guido Caldarelli, Louis Shekhtman, Shlomo Havlin, and Walter Quattrociocchi. 2017. Debunking in a world of tribes. PLOS ONE 12, 7 (07 2017), 1--27.

[10]

W. Lee Howell. 2013. Digital Wildfires in a Hyperconnected World. Technical Report Global Risks. World Economic Forum.

[11]

Fabiana Zollo and Walter Quattrociocchi. 2018 (in press). Social dynamics in the age of credulity: The misinformation risk and its fallout. In Digital Dominance. The Power of Google, Amazon, Facebook, and Apple, Martin Moore and Damian Tambini (Eds.). Oxford University Press, Oxford.

[12]

Fabiana Zollo and Walter Quattrociocchi. 2018. Misinformation spreading on Facebook. In Complex Spreading Phenomena in Social Systems, Sune Lehmann and Yong-Yeol Ahn (Eds.). Springer Nature.

[13]

Sotirios Antoniadis, Iouliana Litou, and Vana Kalogeraki. 2015. A model for identifying misinformation in online social networks. In On the Move to Meaningful Internet Systems: OTM 2015 Conferences, Christophe Debruyne, Hervé Panetto, Robert Meersman, Tharam Dillon, Georg Weichhart, Yuan An, and Claudio Agostino Ardagna (Eds.). Springer International Publishing, Cham, 473--482.

Digital Library

[14]

Meet Rajdev and Kyumin Lee. 2015. Fake and spam messages: Detecting misinformation during natural disasters on social media. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’15), Vol. 1. IEEE, 17--20.

Digital Library

[15]

Christina Boididou, Symeon Papadopoulos, Lazaros Apostolidis, and Yiannis Kompatsiaris. 2017. Learning to detect misleading content on Twitter. In Proceedings of the 2017 ACM International Conference on Multimedia Retrieval. ACM, 278--286.

Digital Library

[16]

Christina Boididou, Stuart E Middleton, Zhiwei Jin, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, and Yiannis Kompatsiaris. 2017. Verifying information with multimedia content on Twitter. Multimed. Tools Appl. 77, 12 (2017), 1--27.

Digital Library

[17]

Ana-Maria Popescu and Marco Pennacchiotti. 2010. Detecting controversial events from Twitter. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 1873--1876.

Digital Library

[18]

Aditi Gupta, Hemank Lamba, Ponnurangam Kumaraguru, and Anupam Joshi. 2013. Faking Sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 729--736.

Digital Library

[19]

Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web. ACM, 675--684.

Digital Library

[20]

Cody Buntain and Jennifer Golbeck. 2017. Automatically identifying fake news in popular Twitter threads. In Proceedings of the IEEE International Conference on Smart Cloud (SmartCloud). IEEE, 208--215.

[21]

Srijan Kumar, Robert West, and Jure Leskovec. 2016. Disinformation on the web: Impact, characteristics, and detection of Wikipedia hoaxes. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 591--602.

Digital Library

[22]

Stefan Siersdorfer, Sergiu Chelaru, Jose San Pedro, Ismail Sengor Altingovde, and Wolfgang Nejdl. 2014. Analyzing and mining comments and comment ratings on the social web. ACM Trans. Web 8, 3 (2014), 17.

Digital Library

[23]

Sadia Afroz, Michael Brennan, and Rachel Greenstadt. 2012. Detecting hoaxes, frauds, and deception in writing style online. In Proceedings of the IEEE Symposium on Security and Privacy (SP’12). IEEE, 461--475.

Digital Library

[24]

Yelena Mejova, Amy X. Zhang, Nicholas Diakopoulos, and Carlos Castillo. 2014. Controversy and sentiment in online news. arXiv preprint arXiv:1409.8152 (2014).

[25]

Lada A. Adamic and Natalie Glance. 2005. The political blogosphere and the 2004 US election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery. ACM, 36--43.

Digital Library

[26]

Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2016. Quantifying controversy in social media. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, 33--42.

Digital Library

[27]

Andrew Guess, Brendan Nyhan, and Jason Reifler. 2018. Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council, 9.

[28]

Johan Ugander, Lars Backstrom, Cameron Marlow, and Jon Kleinberg. 2012. Structural diversity in social contagion. Proc. Natl. Acad. Sci. 109, 16 (2012), 5,962--5,966.

[29]

Pedro Henrique Calais Guerra, Wagner Meira Jr, Claire Cardie, and Robert Kleinberg. 2013. A measure of polarization on social media networks based on community boundaries. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM’13).

[30]

Ana Lucía Schmidt, Fabiana Zollo, Antonio Scala, and Walter Quattrociocchi. 2018. Polarization rank: A study on European news consumption on Facebook. arXiv preprint arXiv:1805.08030 (2018).

[31]

Mauro Conti, Daniele Lain, Riccardo Lazzeretti, Giulio Lovisotto, and Walter Quattrociocchi. 2017. It’s always April fools’ day&excl; On the difficulty of social network misinformation classification via propagation features. arXiv preprint arXiv:1701.04221 (2017).

[32]

Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor. Newslett. 19, 1 (2017), 22--36.

Digital Library

[33]

Savvas Zannettou, Michael Sirivianos, Jeremy Blackburn, and Nicolas Kourtellis. 2018. The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. arXiv preprint arXiv:1804.03461 (2018).

[34]

Srijan Kumar and Neil Shah. 2018. False information on web and social media: A survey. arXiv preprint arXiv:1804.08559 (2018).

[35]

Srijan Kumar, Meng Jiang, Taeho Jung, Roger Jie Luo, and Jure Leskovec. 2018. MIS2: Misinformation and misbehavior mining on the web. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 799--800.

Digital Library

[36]

Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schölkopf, and Manuel Gomez-Rodriguez. 2018. Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 324--332.

Digital Library

[37]

Nikhil Karamchandani and Massimo Franceschetti. 2013. Rumor source detection under probabilistic sampling. In Proceedings of the IEEE International Symposium on Information Theory (ISIT’13). IEEE, 2,184--2,188.

[38]

Sejeong Kwon, Meeyoung Cha, and Kyomin Jung. 2017. Rumor detection over varying time windows. PloS One 12, 1 (2017), e0168344.

[39]

Zhaoxu Wang, Wenxiang Dong, Wenyi Zhang, and Chee Wei Tan. 2014. Rumor source detection with multiple observations: Fundamental limits and algorithms. In ACM SIGMETRICS Perf. Eval. Rev., Vol. 42. ACM, 1--13.

Digital Library

[40]

Sam Spencer and R. Srikant. 2016. Maximum likelihood rumor source detection in a star network. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’16). IEEE, 2,199--2,203.

[41]

Yang Liu and Songhua Xu. 2016. Detecting rumors through modeling information propagation networks in a social media environment. IEEE Trans. Comput. Soc. Systems 3, 2 (2016), 46--62.

[42]

Zhe Zhao, Paul Resnick, and Qiaozhu Mei. 2015. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1,395--1,405.

Digital Library

[43]

Srijan Kumar, Francesca Spezzano, and V. S. Subrahmanian. 2015. Vews: A Wikipedia vandal early warning system. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’15). ACM, 607--616.

Digital Library

[44]

Ming Yang, Melody Kiang, and Wei Shang. 2015. Filtering big data from social media—Building an early warning system for adverse drug reactions. J. Biomed. Inform. 54 (2015), 230--240.

Digital Library

[45]

Michela Del Vicario, Sabrina Gaito, Walter Quattrociocchi, Matteo Zignani, and Fabiana Zollo. 2017. News consumption during the Italian Referendum: A cross-platform analysis on Facebook and Twitter. In Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics. IEEE.

[46]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12 (2011), 2,825--2,830.

Digital Library

[47]

Issa Alsmadi and Gan Keng Hoon. 2018. Term weighting scheme for short-text classification: Twitter corpuses. Neural Comput. Appl. (2018), 1--13.

[48]

Selma Ayşe Özel, Esra Saraç, Seyran Akdemir, and Hülya Aksu. 2017. Detection of cyberbullying on social media messages in Turkish. In Proceedings of the International Conference on Computer Science and Engineering (UBMK’17). IEEE, 366--370.

[49]

Apalak Khatua and Aparup Khatua. 2017. Cricket World Cup 2015: Predicting users’ orientation through mix tweets on Twitter platform. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM, 948--951.

Digital Library

[50]

Despoina Antonakaki, Iasonas Polakis, Elias Athanasopoulos, Sotiris Ioannidis, and Paraskevi Fragopoulou. 2016. Exploiting abused trending topics to identify spam campaigns in Twitter. Soc. Netw. Anal. Mining 6, 1 (2016), 48.

[51]

Jeff Hemsley, Sikana Tanupabrungsun, and Bryan Semaan. 2017. Call to retweet: Negotiated diffusion of strategic political messages. In Proceedings of the 8th International Conference on Social Media 8 Society. ACM, 9.

Digital Library

[52]

Ward van Zoonen and Toni G. L. A. van der Meer. 2016. Social media research: The application of supervised machine learning in organizational communication research. Comput. Hum. Behav. 63 (2016), 132--141.

Digital Library

[53]

Soroush Vosoughi and Deb Roy. 2016. Tweet acts: A speech act classifier for Twitter. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM’16). 711--715.

[54]

Che-Chia Chang, Shu-I Chiu, and Kuo-Wei Hsu. 2017. Predicting political affiliation of posts on Facebook. In Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication. ACM, 57.

Digital Library

[55]

Ryan M. Rifkin and Ross A. Lippert. 2007. Notes on regularized least squares. MIT CSAIL Technical Reports. http://hdl.handle.net/1721.1/37318.

[56]

Mark Schmidt, Nicolas Le Roux, and Francis Bach. 2017. Minimizing finite sums with the stochastic average gradient. Math. Program. 162, 1--2 (2017), 83--112.

Digital Library

[57]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273--297.

Digital Library

[58]

Jon Louis Bentley. 1975. Multidimensional binary search trees used for associative searching. Commun. ACM 18, 9 (1975), 509--517.

Digital Library

[59]

David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1988. Learning representations by back-propagating errors. Cog. Model. 5, 3 (1988), 1.

[60]

Leo Breiman. 2017. Classification and Regression Trees. Routledge.

[61]

Marina Sokolova and Guy Lapalme. 2009. A systematic analysis of performance measures for classification tasks. Inform. Proc. Manag. 45, 4 (2009), 427--437.

Digital Library

[62]

Charles E. Metz. 1978. Basic principles of ROC analysis. In Sem. Nucl. Med., Vol. 8. Elsevier, 283--298.

[63]

ADS. 2016. Elenchi Testate. Retrieved from http://www.adsnotizie.it/_testate.asp.

[64]

Bufale.net. 2016. The Black List: La Lista Nera Del Web. (2016). Retrieved from http://www.adsnotizie.it/_testate.asp.

[65]

BUTAC. 2016. The Black List. (2016). Retrieved from http://www.butac.it/the-black-list/.

[66]

Facebook. 2013. Using the Graph API. (2013). Retrieved from https://developers.facebook.com/docs/graph-api/using-graph-api/.

[67]

SpazioDati. 2017. Dandelion API. Retrieved from https://dandelion.eu/docs/.

[68]

Raquel Fonseca Canales and Edgar Casasola Murillo. 2017. Evaluation of entity recognition algorithms in short texts. CLEI Electron. J. 20, 1 (2017).

[69]

Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou. 2009. Exploratory undersampling for class-imbalance learning. IEEE Trans. Systems, Man, Cyber., Part B (Cyber.) 39, 2 (2009), 539--550.

Digital Library

[70]

Chris Drummond and Robert C. Holte. 2003. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II, Vol. 11. Citeseer, 1--8.

[71]

N. V. Chawla, N. Japkowicz, and A. Kotcz. 2004. Editorial: Special issue on learning from imbalanced data sets. SIGKDD Explor. Newslett. 6: 1--6.

Digital Library

[72]

Kai Shu, Suhang Wang, and Huan Liu. 2017. Exploiting tri-relationship for fake news detection. arXiv preprint arXiv:1712.07709 (2017).

[73]

Katya Demidova. 2016. Getting real about fake news. GitHub. Retrieved from https://github.com/demidovakatya/competitions/blob/master/fake-news/README.md.

Cited By

García-Marín D(2024)Mapa mundial del fact-checking en Instagram. Formatos de contenido y su efecto en el engagementEstudios sobre el Mensaje Periodístico10.5209/emp.9647330:3(489-501)Online publication date: 26-Sep-2024
https://doi.org/10.5209/emp.96473
Maksymov IPogrebna G(2024)Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social NetworksInformation10.3390/info1503017015:3(170)Online publication date: 20-Mar-2024
https://doi.org/10.3390/info15030170
Hirakura NAida M(2024)On relationships between similarity of topics and opinion formationNonlinear Theory and Its Applications, IEICE10.1587/nolta.15.22615:2(226-236)Online publication date: 2024
https://doi.org/10.1587/nolta.15.226
Show More Cited By

Index Terms

Polarization and Fake News: Early Warning of Potential Misinformation Targets

Recommendations

Science Disinformation: On the Problem of Fake News
Abstract
This article is devoted to an important socio-cultural phenomenon that undermines public confidence in science, that is, fake science news. The term fake news is analyzed and data on the dissemination of fake news on social networks is provided. ...
Consuming Fake News: A Matter of Age? The Perception of Political Fake News Stories in Facebook Ads
Human Aspects of IT for the Aged Population. Technology and Society
Abstract
Social media are increasingly being used by young and old as a source of information. Fake news is also on the rise. The role played by age in the consumption of fake news on social media, however, is unclear. This paper explores the generational ...
The diffusion of misinformation on social media

This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temporal pattern, content mutation, and sources of misinformation. We traced the lifecycle of 17 popular political rumors ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 13, Issue 2

May 2019

156 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3313948

Editor:
Brian D. Davison
Lehigh University, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2019

Accepted: 01 February 2019

Revised: 01 December 2018

Received: 01 February 2018

Published in TWEB Volume 13, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

AMOFI (Analysis and Modeling OF social medIa)
IMT/Extrapola Srl project

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

191
Total Citations
View Citations
5,066
Total Downloads

Downloads (Last 12 months)568
Downloads (Last 6 weeks)65

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

García-Marín D(2024)Mapa mundial del fact-checking en Instagram. Formatos de contenido y su efecto en el engagementEstudios sobre el Mensaje Periodístico10.5209/emp.9647330:3(489-501)Online publication date: 26-Sep-2024
https://doi.org/10.5209/emp.96473
Maksymov IPogrebna G(2024)Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social NetworksInformation10.3390/info1503017015:3(170)Online publication date: 20-Mar-2024
https://doi.org/10.3390/info15030170
Hirakura NAida M(2024)On relationships between similarity of topics and opinion formationNonlinear Theory and Its Applications, IEICE10.1587/nolta.15.22615:2(226-236)Online publication date: 2024
https://doi.org/10.1587/nolta.15.226
Mall RNagpal MSalminen JAlmerekhi HJung SJansen B(2024)Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ CommentsSage Open10.1177/2158244024125643814:2Online publication date: 29-May-2024
https://doi.org/10.1177/21582440241256438
Ghafouri VAlatawi FKarami MSuch JSuarez-Tangil G(2024)Transformer-Based Quantification of the Echo Chamber Effect in Online CommunitiesProceedings of the ACM on Human-Computer Interaction10.1145/36870068:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687006
Zhuang WChen CLi C(2024)Towards Robust Rumor Detection with Graph Contrastive and Curriculum LearningACM Transactions on Knowledge Discovery from Data10.1145/365302318:7(1-21)Online publication date: 30-Mar-2024
https://dl.acm.org/doi/10.1145/3653023
Umair MBouguettaya ALakhdari AOuzzani MLiu Y(2024)Exif2Vec: A Framework to Ascertain Untrustworthy Crowdsourced Images Using MetadataACM Transactions on the Web10.1145/364509418:3(1-27)Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1145/3645094
Ibañez-Lissen LGonzález-Manzano Lde Fuentes JGoyanes M(2024)On the Feasibility of Predicting Volumes of Fake News—The Spanish CaseIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.329709311:4(5230-5240)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3297093
Güven EBoyacı ASarıtemur FTürk ZSütçü GTurna Ö(2024)ICON: Instagram Profile Classification Using Image and Natural Language Processing MethodsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.327542811:2(2776-2783)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3275428
Mao YHovakimyan NAbdelzaher TTheodorou E(2024)Social System Inference From Noisy ObservationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322959911:1(639-651)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3229599
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents