research-article

From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism

Authors:

Sachita Nishal,

Nicholas DiakopoulosAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 441, Pages 1 - 28

https://doi.org/10.1145/3555542

Published: 11 November 2022 Publication History

Abstract

The scale of scientific publishing continues to grow, creating overload on science journalists who are inundated with choices for what would be most interesting, important, and newsworthy to cover in their reporting. Our work addresses this problem by considering the viability of creating a predictive model of newsworthiness of scientific articles that is trained using crowdsourced evaluations of newsworthiness. We proceed by first evaluating the potential of crowd-sourced evaluations of newsworthiness by assessing their alignment with expert ratings of newsworthiness, analyzing both quantitative correlations and qualitative rating rationale to understand limitations. We then demonstrate and evaluate a predictive model trained on these crowd ratings together with arXiv article metadata, text, and other computed features. Based on the crowdsourcing protocol we developed, we find that while crowdsourced ratings of newsworthiness often align moderately with expert ratings, there are also notable differences and divergences which limit the approach. Yet despite these limitations we also find that the predictive model we built provides a reasonably precise set of rankings when validated against expert evaluations (P@10 = 0.8, P@15 = 0.67), suggesting that a viable signal can be learned from crowdsourced evaluations of newsworthiness. Based on these findings we discuss opportunities for future work to leverage crowdsourcing and predictive approaches to support journalistic work in discovering and filtering newsworthy information.

References

[1]

Tanja Aitamurto. 2016. Crowdsourcing as a Knowledge-Search Method in Digital Journalism: Ruptured ideals and blended responsibility. Digital Journalism, Vol. 4, 2 (Feb. 2016), 280--297. https://doi.org/10.1080/21670811.2015.1034807

[2]

Stuart Allan. 2011. Introduction: Science journalism in a digital age. Journalism, Vol. 12, 7 (Oct. 2011), 771--777. https://doi.org/10.1177/1464884911412688

[3]

Sigurd Allern. 2002. Journalistic and Commercial News Values: News Organizations as Patrons of an Institution and Market Actors. Nordicom Review, Vol. 23, 1--2 (Sept. 2002), 137--152. https://doi.org/10.1515/nor-2017-0327 Publisher: Sciendo Section: Nordicom Review.

[4]

Cw Anderson. 2011. Between creative and quantified audiences: Web metrics and changing patterns of newswork in local US newsrooms. Journalism, Vol. 12, 5 (July 2011), 550--566. https://doi.org/10.1177/1464884911402451

[5]

Chittaranjan Andrade. 2011. How to write a good abstract for a scientific paper or conference presentation. Indian journal of psychiatry, Vol. 53, 2 (April 2011), 172--175. https://doi.org/10.4103/0019--5545.82558 Publisher: Medknow Publications.

[6]

Ron Artstein and Massimo Poesio. 2008. Inter-Coder Agreement for Computational Linguistics. Computational Linguistics, Vol. 34, 4 (dec 2008), 555--596. https://doi.org/10.1162/coli.07-034-R2

Digital Library

[7]

Earl R. Babbie. 2008. The basics of social research 4th ed ed.). Thomson/Wadsworth, Belmont, CA.

[8]

Franziska Badenschier and Holger Wormer. 2012. Issue Selection in Science Journalism: Towards a Special Theory of News Values for Science News? In The Sciences' Media Connection --Public Communication and its Repercussions, Simone Rödder, Martina Franzen, and Peter Weingart (Eds.). Vol. 28. Springer Netherlands, Dordrecht, 59--85. https://doi.org/10.1007/978--94-007--2085--5_4 Series Title: Sociology of the Sciences Yearbook.

[9]

Mariella Bastian, Natalie Helberger, and Mykola Makhortykh. 2021. Safeguarding the Journalistic DNA: Attitudes towards the Role of Professional Values in Algorithmic News Recommender Designs. Digital Journalism, Vol. 9, 6 (July 2021), 835--863. https://doi.org/10.1080/21670811.2021.1912622

[10]

Rena Kim Bivens. 2008. The Internet, Modile Phones and Blogging: How new media are transforming traditional journalism. Journalism Practice, Vol. 2, 1 (Feb. 2008), 113--129. https://doi.org/10.1080/17512780701768568

[11]

Deborah Blum. 2021. Science journalism grows up. Science, Vol. 372, 6540 (April 2021), 323--323. https://doi.org/10.1126/science.abj0434 Publisher: American Association for the Advancement of Science.

[12]

Lutz Bornmann, Robin Haunschild, and Rüdiger Mutz. 2021. Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications, Vol. 8, 1 (Oct. 2021), 224. https://doi.org/10.1057/s41599-021-00903-w

[13]

Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology, Vol. 3, 2 (Jan. 2006), 77--101. https://doi.org/10.1191/1478088706qp063oa

[14]

L. Breiman. 2004. Random Forests. Machine Learning, Vol. 45 (2004), 5--32.

Digital Library

[15]

Aengus Bridgman, Eric Merkley, Peter John Loewen, Taylor Owen, Derek Ruths, Lisa Teichmann, and Oleg Zhilin. 2020. The causes and consequences of COVID-19 misperceptions: Understanding the role of news and social media. Harvard Kennedy School Misinformation Review (June 2020). https://doi.org/10.37016/mr-2020-028

[16]

Paul Brighton and Dennis Foy. 2007. News values. SAGE Publications, London ; Thousand Oaks, Calif. OCLC: ocn153557904.

[17]

Dominique Brossard and Dietram A. Scheufele. 2013. Science, New Media, and the Public. Science, Vol. 339, 6115 (Jan. 2013), 40--41. https://doi.org/10.1126/science.1232329

[18]

Taina Bucher. 2012. Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society, Vol. 14, 7 (Nov. 2012), 1164--1180. https://doi.org/10.1177/1461444812440159

[19]

Jesse Chandler, Gabriele Paolacci, and Pam Mueller. 2013. Risks and Rewards of Crowdsourcing Marketplaces. In Handbook of Human Computation, Pietro Michelucci (Ed.). Springer New York, New York, NY, 377--392. https://doi.org/10.1007/978--1--4614--8806--4_30

[20]

Scott Clifford, Ryan M Jewell, and Philip D Waggoner. 2015. Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, Vol. 2, 4 (Oct. 2015), 205316801562207. https://doi.org/10.1177/2053168015622072

[21]

Kelley Cotter. 2019. Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram:. New Media & Society, Vol. 21, 4 (April 2019), 895--913. https://doi.org/10.1177/1461444818815684

[22]

Michael A. DeVito, Darren Gergle, and Jeremy Birnholtz. 2017. "Algorithms Ruin Everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17). Association for Computing Machinery, New York, NY, USA, 3163--3174. https://doi.org/10.1145/3025453.3025659

Digital Library

[23]

Nicholas Diakopoulos. 2020. Computational News Discovery: Towards Design Considerations for Editorial Orientation Algorithms in Journalism. Digital Journalism, Vol. 8, 7 (Aug. 2020), 945--967. https://doi.org/10.1080/21670811.2020.1736946

[24]

Nicholas Diakopoulos, Munmun De Choudhury, and Mor Naaman. 2012. Finding and assessing social media information sources in the context of journalism. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). Association for Computing Machinery, New York, NY, USA, 2451--2460. https://doi.org/10.1145/2207676.2208409

Digital Library

[25]

Nicholas Diakopoulos, Madison Dong, Leonard Bronner, and Jeremy Bowers. 2020. Generating Location-Based News Leads for National Politics Reporting. In Proc. Computation Journalism Symposium.

[26]

Nicholas Diakopoulos and Michael Koliska. 2016. Algorithmic Transparency in the News Media. Digital Journalism, Vol. 5, 7 (08 2016), 1--20. https://doi.org/10.1080/21670811.2016.1208053

[27]

Nicholas Diakopoulos, Daniel Trielli, and Grace Lee. 2021. Towards Understanding and Supporting Journalistic Practices Using Semi-Automated News Discovery Tools. Proc. ACM Hum.-Comput. Interact., Vol. 5, CSCW2, Article 406, 30 pages. https://doi.org/10.1145/3479550

Digital Library

[28]

Murray Dick. 2014. Interactive Infographics and News Values. Digital Journalism, Vol. 2, 4 (Oct. 2014), 490--506. https://doi.org/10.1080/21670811.2013.841368

[29]

Sharon Dunwoody. 2012. Science journalism. In Routledge Handbook of Public Communication of Science and Technology. Routledge. https://doi.org/10.4324/9780203483794.ch3

[30]

Declan Fahy and Matthew C. Nisbet. 2011. The science journalist online: Shifting roles and emerging practices. Journalism, Vol. 12, 7 (Oct. 2011), 778--793. https://doi.org/10.1177/1464884911412697

[31]

Patrick Ferrucci, Jacob L Nelson, and Miles P Davis. 2020. From "Public Journalism" to "Engaged Journalism": Imagined Audiences and Denigrating Discourse. International Journal of Communication, Vol. 14, 0 (02 2020), 19.

[32]

Richard Fletcher, Steve Schifferes, and Neil Thurman. 2020. Building the ?Truthmeter': Training algorithms to help journalists assess the credibility of social media sources. Convergence, Vol. 26, 1 (Feb. 2020), 19--34. https://doi.org/10.1177/1354856517714955 Publisher: SAGE Publications Ltd.

[33]

Silke Fürst. 2020. In the Service of Good Journalism and Audience Interests? How Audience Metrics Affect News Quality. Media and Communication, Vol. 8, 3 (Aug. 2020), 270--280. https://doi.org/10.17645/mac.v8i3.3228

[34]

Johan Galtung and Mari Holmboe Ruge. 1965. The Structure of Foreign News: The Presentation of the Congo, Cuba and Cyprus Crises in Four Norwegian Newspapers. Journal of Peace Research, Vol. 2, 1 (March 1965), 64--90. https://doi.org/10.1177/002234336500200104

[35]

Tong Gao, Jessica R. Hullman, Eytan Adar, Brent Hecht, and Nicholas Diakopoulos. 2014. NewsViews: an automated pipeline for creating custom geovisualizations for news. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, Toronto Ontario Canada, 3005--3014. https://doi.org/10.1145/2556288.2557228

Digital Library

[36]

P. Geurts, D. Ernst, and L. Wehenkel. 2006. Extremely randomized trees. Machine Learning, Vol. 63 (2006), 3--42.

Digital Library

[37]

Peter Golding and Philip Ross Courtney Elliott. 1979. Making the news 1st ed ed.). Longman, London ; New York.

[38]

Andreas Graefe. 2016. Guide to Automated Journalism. (2016). https://doi.org/10.7916/D80G3XDJ Publisher: Columbia University.

[39]

Marisela Gutierrez Lopez, Colin Porlezza, Glenda Cooper, Stephann Makri, Andrew MacFarlane, and Sondess Missaoui. 2022. A Question of Design: Strategies for Embedding AI-Driven Tools into Journalistic Work Routines. Digital Journalism (March 2022), 1--20. https://doi.org/10.1080/21670811.2022.2043759

[40]

James Hamilton. 2004. All the News That's Fit to Sell. Princeton University Press. https://doi.org/10.2307/j.ctt7smgs

[41]

James Hamilton and Fred Turner. 2009. Accountability Through Algorithm: Developing the Field of Computational Journalism. Center For Advanced Study in the Behavioral Sciences Summer Workshop (2009). http://web.stanford.edu/ fturner/Hamilton%20Turner%20Acc%20by%20Alg%20Final.pdf

[42]

Tony Harcup and Deirdre O'Neill. 2001. What Is News? Galtung and Ruge revisited. Journalism Studies, Vol. 2, 2 (Jan. 2001), 261--280. https://doi.org/10.1080/14616700118449

[43]

Tony Harcup and Deirdre O'Neill. 2017. What is News?: News values revisited (again). Journalism Studies, Vol. 18, 12 (Dec. 2017), 1470--1488. https://doi.org/10.1080/1461670X.2016.1150193

[44]

Andrew J. Hoffman. 2021. The engaged scholar : expanding the impact of academic research in today's world. Stanford University Press.

[45]

Alok Jha. 2010. What is a story tracker? https://www.theguardian.com/science/blog/2010/jun/09/science-story-trackers

[46]

Aniket Kittur, Boris Smus, Susheel Khamkar, and Robert E. Kraut. 2011. CrowdForge: crowdsourcing complex work. In Proceedings of the 24th annual ACM symposium on User interface software and technology - UIST '11. ACM Press, Santa Barbara, California, USA, 43. https://doi.org/10.1145/2047196.2047202

Digital Library

[47]

Peder Olesen Larsen and Markus von Ins. 2010. The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics, Vol. 84, 3 (Sept. 2010), 575--603. https://doi.org/10.1007/s11192-010-0202-z

[48]

Fabienne Lind, Maria Gruber, and Hajo G. Boomgaarden. 2017. Content Analysis by the Crowd: Assessing the Usability of Crowdsourcing for Coding Latent Constructs. Communication Methods and Measures, Vol. 11, 3 (July 2017), 191--209. https://doi.org/10.1080/19312458.2017.1317338

[49]

Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Sameena Shah, Robert Martin, and John Duprey. 2017. Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data. arXiv:1711.04068 [cs] (Nov. 2017). http://arxiv.org/abs/1711.04068 arXiv: 1711.04068.

[50]

Tetyana Lokot and Nicholas Diakopoulos. 2016. News Bots: Automating news and information dissemination on Twitter. Digital Journalism, Vol. 4, 6 (Aug. 2016), 682--699. https://doi.org/10.1080/21670811.2015.1081822

[51]

Ansel MacLaughlin, John Wihbey, and David Smith. 2018. Predicting News Coverage of Scientific Articles. Proceedings of the International AAAI Conference on Web and Social Media, Vol. 12, 1 (June 2018). https://ojs.aaai.org/index.php/ICWSM/article/view/14999 Section: Full Papers.

[52]

Måns Magnusson, Jens Finnäs, and Leonard Wallentin. 2016. Finding the news lead in the data haystack: Automated local data journalism using crime data. (2016), 4.

[53]

Winter Mason and Siddharth Suri. 2012. Conducting behavioral research on Amazon's Mechanical Turk. Behavior Research Methods, Vol. 44, 1 (March 2012), 1--23. https://doi.org/10.3758/s13428-011-0124--6

[54]

Melinda McClure Haughey, Meena Devii Muralikumar, Cameron A. Wood, and Kate Starbird. 2020. On the Misinformation Beat: Understanding the Work of Investigative Journalists Reporting on Problematic Information Online. Proc. of the ACM on Human-Computer Interaction, Vol. 4, CSCW2 (Oct. 2020), 1--22. https://doi.org/10.1145/3415204

Digital Library

[55]

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model cards for model reporting. (2019), 220--229.

[56]

Enrico Motta, Enrico Daga, Andreas L. Opdahl, and Bjornar Tessem. 2020. Analysis and Design of Computational News Angles. IEEE Access, Vol. 8 (2020), 120613--120626. https://doi.org/10.1109/ACCESS.2020.3005513

[57]

Priyanka Nanayakkara, Jessica Hullman, and Nicholas Diakopoulos. 2021. Unpacking the expressed consequences of AI research in broader impact statements. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 795--806.

Digital Library

[58]

Nature. 2019. How to Write a Nature Summary. https://www.nature.com/documents/nature-summary-paragraph.pdf

[59]

Chelsea Peterson-Salahuddin and Nicholas Diakopoulos. 2020. Negotiated Autonomy: The Role of Social Media Algorithms in Editorial Decision Making. Media and Communication, Vol. 8, 3 (2020), 12.

[60]

Tzipora Rakedzon, Elad Segev, Noam Chapnik, Roy Yosef, and Ayelet Baram-Tsabari. 2017. Automatic jargon identifier for scientists engaging with the public and science communication educators. PLOS ONE, Vol. 12, 8 (Aug. 2017), e0181742. https://doi.org/10.1371/journal.pone.0181742

[61]

Zvi Reich. 2006. The Process Model of News Initiative: Sources lead first, reporters thereafter. Journalism Studies, Vol. 7, 4 (Aug. 2006), 497--514. https://doi.org/10.1080/14616700600757928

[62]

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 3980--3990. https://doi.org/10.18653/v1/D19--1410

[63]

Jason D. M. Rennie, Lawrence Shih, Jaime Teevan, and David R. Karger. 2003. Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), Washington, D.C. proceedings of the twentieth international conference on machine learning (icml 2003), washington, d.c. ed.). https://www.microsoft.com/en-us/research/publication/tackling-poor-assumptions-naive-bayes-text-classifiers/

[64]

Carole Rich. 2016. Writing and reporting news: a coaching method 8th ed ed.). Cengage Learning, Boston, MA. OCLC: ocn908085863.

[65]

Daniel Riffe, Stephen Lacy, Brendan R. Watson, and Frederick Fico. 2019. Analyzing Media Messages: Using Quantitative Content Analysis in Research 4th ed.). Lawrence Erlbaum, Mahwah, NJ, USA.

[66]

Ida Schultz. 2007. The Journalistic Gut Feeling: Journalistic doxa, news habitus and orthodox news values. Journalism Practice, Vol. 1, 2 (June 2007), 190--207. https://doi.org/10.1080/17512780701275507

[67]

Raz Schwartz, Mor Naaman, and Rannie Teodoro. 2015. Editorial Algorithms: Using Social Media to Discover and Report Local News. Proceedings of the International AAAI Conference on Web and Social Media, Vol. 9, 1 (April 2015). https://ojs.aaai.org/index.php/ICWSM/article/view/14633 Number: 1.

[68]

C. Estelle Smith, Xinyi Wang, Raghav Pavan Karumur, and Haiyi Zhu. 2018. [Un]breaking News: Design Opportunities for Enhancing Collaboration in Scientific Media Production. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). Association for Computing Machinery, New York, NY, USA, 1--13. https://doi.org/10.1145/3173574.3173955

Digital Library

[69]

Alexander Spangher, Nanyun Peng, Jonathan May, and Emilio Ferrara. 2021. Modeling "Newsworthiness" for Lead-Generation Across Corpora. arXiv:2104.09653 [cs] (April 2021). http://arxiv.org/abs/2104.09653 arXiv: 2104.09653.

[70]

Julie Suleski and Motomu Ibaraki. 2010. Scientists are talking, but mostly to each other: a quantitative analysis of research represented in mass media. Public Understanding of Science, Vol. 19, 1 (Jan. 2010), 115--125. https://doi.org/10.1177/0963662508096776

[71]

Kyle A. Thomas and Scott Clifford. 2017. Validity and Mechanical Turk: An assessment of exclusion methods and interactive experiments. Computers in Human Behavior, Vol. 77 (Dec. 2017), 184--197. https://doi.org/10.1016/j.chb.2017.08.038

Digital Library

[72]

Damian Trilling, Petro Tolochko, and Björn Burscher. 2017. From Newsworthiness to Shareworthiness: How to Predict News Sharing Based on Article Characteristics. Journalism & Mass Communication Quarterly, Vol. 94, 1 (March 2017), 38--60. https://doi.org/10.1177/1077699016654682

[73]

Johanna Vehkoo. 2013. Crowdsourcing in Investigative Journalism. Reuters Institute for the Study of Journalism, Vol. Oxford: Report (2013).

[74]

Meng-Han Wu and Alexander Quinn. 2017. Confusing the crowd: Task instruction quality on amazon mechanical turk. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol. 5. 206--215.

Cited By

Nishal SSinchai JDiakopoulos N(2024)Understanding Practices around Computational News Discovery Tools in the Domain of Science JournalismProceedings of the ACM on Human-Computer Interaction10.1145/36374198:CSCW1(1-36)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637419
Aubin Le Quéré MNaaman MFields J(2024)Not Quite Filling the Void: Comparing the Perceptions of Local Online Groups and Local Media Pages on FacebookProceedings of the ACM on Human-Computer Interaction10.1145/36373778:CSCW1(1-22)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637377
Neupane KYeddulapalli HKambhampati ACheng XNeupane RKung ERinehart ACalyam P(2023)Automation of News Content Curation and Storytelling for Local Newsrooms2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00151(1051-1060)Online publication date: 17-Dec-2023
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00151

Index Terms

From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Human computer interaction (HCI)
    1. HCI design and evaluation methods

Recommendations

Modus Operandi of Crowd Workers: The Invisible Role of Microtask Work Environments

The ubiquity of the Internet and the widespread proliferation of electronic devices has resulted in flourishing microtask crowdsourcing marketplaces, such as Amazon MTurk. An aspect that has remained largely invisible in microtask crowdsourcing is that ...
A transfer learning based framework of crowd-selection on twitter
KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Crowd selection is essential to crowd sourcing applications, since choosing the right workers with particular expertise to carry out crowdsourced tasks is extremely important. The central problem is simple but tricky: given a crowdsourced task, who are ...
Make Hay While the Crowd Shines: Towards Efficient Crowdsourcing on the Web
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Within the scope of this PhD proposal, we set out to investigate two pivotal aspects that influence the effectiveness of crowdsourcing: (i) microtask design, and (ii) workers behavior. Leveraging the dynamics of tasks that are crowdsourced on the one ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

CSCW

November 2022

8205 pages

EISSN:2573-0142

DOI:10.1145/3571154

Editor:
Jeff Nichols
Google

Issue’s Table of Contents

Copyright © 2022 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: 11 November 2022

Published in PACMHCI Volume 6, Issue CSCW2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
153
Total Downloads

Downloads (Last 12 months)75
Downloads (Last 6 weeks)2

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Nishal SSinchai JDiakopoulos N(2024)Understanding Practices around Computational News Discovery Tools in the Domain of Science JournalismProceedings of the ACM on Human-Computer Interaction10.1145/36374198:CSCW1(1-36)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637419
Aubin Le Quéré MNaaman MFields J(2024)Not Quite Filling the Void: Comparing the Perceptions of Local Online Groups and Local Media Pages on FacebookProceedings of the ACM on Human-Computer Interaction10.1145/36373778:CSCW1(1-22)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637377
Neupane KYeddulapalli HKambhampati ACheng XNeupane RKung ERinehart ACalyam P(2023)Automation of News Content Curation and Storytelling for Local Newsrooms2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00151(1051-1060)Online publication date: 17-Dec-2023
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00151

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.

Media

Figures

Other

Tables

View Issue’s Table of Contents