Research Article

Trust in the News: A Digital Labelling Solution for Journalistic Contents

Zhan Liu 1 * , Matthieu Delaloye 1 , Nicole Glassey Balet 1 , Sébastien Hersant 2 , Frédéric Gris 2 , Laurent Sciboz 1
More Detail
1 University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), SWITZERLAND2 ESH Médias, SWITZERLAND* Corresponding Author
Online Journal of Communication and Media Technologies, 12(2), April 2022, e202207, https://doi.org/10.30935/ojcmt/11528
Published: 09 January 2022
OPEN ACCESS   2004 Views   1349 Downloads
Download Full Text (PDF)

ABSTRACT

Trust has long been considered an important factor that affects the relation between people and news. However, with the increasing amount of information online, as well as new digital tools and services, this relation has changed, everyone can create content, anytime and anywhere. Therefore, being able to identify and distinguish reliable sources of information online becomes a challenge for the public. In this paper, we focus on providing a digital labelling solution for journalistic contents to enhance the readers’ trust in the media by using design science method. Focus group interviews were conducted to examine reader’s trust perceptions in news contents and their opinions on the trust labelling mechanism. Discussion results helped us to build a list of trust indicators which were used in our labelling distribution system for news content evaluation. Finally, we designed and developed an intermedia certification system to distribute the labelling on trust news contents. Obtained evaluation results confirmed the utility of our system and provided support to readers in identification of the reliable news content.
Keywords: digital labelling, trust indicator, news content, journalist self-assessment, readerships perceptions, news article quality

CITATION (APA)

Liu, Z., Delaloye, M., Glassey Balet, N., Hersant, S., Gris, F., & Sciboz, L. (2022). Trust in the News: A Digital Labelling Solution for Journalistic Contents. Online Journal of Communication and Media Technologies, 12(2), e202207. https://doi.org/10.30935/ojcmt/11528

REFERENCES

  1. Abras, C., Maloney-Krichmar, D., & Preece, J. (2004). User-centered design. In W. S. Bainbridge (Ed.), Berkshire encyclopedia of human-computer interaction (pp. 445-456). SAGE.
  2. Arcinfo. (n.d.). Arcinfo.ch, actualités des cantons de Neuchâtel et du Jura [Arcinfo.ch, news from the cantons of Neuchâtel and Jura]. https://www.arcinfo.ch/
  3. Barthel, M., Mitchell, A., & Holcomb, J. (2016). Many Americans believe fake news is sowing confusion. https://www.pewresearch.org/journalism/2016/12/15/many-americans-believe-fake-news-is-sowing-confusion/
  4. Bennett, M., & Brandt, S. (2019). Newsguard: Fighting misinformation with nutrition labels for news. https://ylai.state.gov/newsguard-fighting-misinformation/
  5. Boulay, E. (2018). RSF and its partners unveil the journalism trust initiative to combat disinformation. https://rsf.org/en/news/rsf-and-its-partners-unveil-journalism-trust-initiative-combat-disinformation
  6. Bozarth, L., & Budak, C. (2020). Toward a better performance evaluation framework for fake news classification. Proceedings of the International AAAI Conference on Web and Social Media (pp. 60-71).
  7. Buber, E., Demir, O., & Sahingoz, O. K. (2017). Feature selections for the machine learning based detection of phishing websites. Proceedings of 2017 International Artificial Intelligence and Data Processing Symposium (pp. 1-5). https://doi.org/10.1109/IDAP.2017.8090317
  8. Buntain, C., & Golbeck, J. (2017). Automatically identifying fake news in popular Twitter threads. Proceedings of the 2017 IEEE International Conference on Smart Cloud (pp. 208-215). https://doi.org/10.1109/SmartCloud.2017.40
  9. Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47(1), 1-15. https://doi.org/10.1086/268763
  10. Deschamps, T. (2018). Google announces ‘Google News Initiative’ to help quality journalism in digital age. http://canoe.com/technology/google-announces-google-news-initiative-to-help-quality-journalism-in-digital-age
  11. Dinh, T. T. A., Wang, J., Chen, G., Liu, R., Ooi, B. C., & Tan, K.-L. (2017). Blockbench: A framework for analyzing private blockchains. Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1085-1100). https://doi.org/10.1145/3035918.3064033
  12. Dinkov, Y., Ali, A., Koychev, I., & Nakov, P. (2019). Predicting the leading political ideology of YouTube channels using acoustic, textual, and metadata Information. arXiv preprint arXiv:1910.08948. https://doi.org/10.21437/Interspeech.2019-2965
  13. ESH Média. (n.d.). ESH Médias, agir ensemble dans nos régions [ESH Médias, acting together in our regions]. https://www.eshmedias.ch/
  14. Hevner, A., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-105. https://doi.org/10.2307/25148625
  15. Iannucci, R. (2017). News or opinion? Online, it’s hard to tell. https://www.poynter.org/ethics-trust/2017/news-or-opinion-online-its-hard-to-tell/
  16. Knight Foundation. (2020). American views 2020: Trust, media, and democracy. https://knightfoundation.org/reports/american-views-2020-trust-media-and-democracy
  17. Krouwer, S., Poels, K., & Paulussen, S. (2020). Moving towards transparency for native advertisements on news websites: A test of more detailed disclosures. International Journal of Advertising, 39(1), 51-73. https://doi.org/10.1080/02650487.2019.1575107
  18. La Côte. (n.d.). La Côte, actualités régionales de la Côte [La Côte, regional news from Nyon, Rolle, Aubonne, and Morges]. https://www.lacote.ch/
  19. Lacey, A., & Luff, D. (2007). Qualitative research analysis. The NIHR RDS for the East Midlands/Yorkshire & the Humber. https://www.rds-yh.nihr.ac.uk/wp-content/uploads/2013/05/9_Qualitative_Data_Analysis_Revision_2009.pdf
  20. Le Nouvelliste. (n.d.). Le Nouvelliste, actualités du Valais [Le Nouvelliste, news from Valais]. https://www.lenouvelliste.ch/
  21. Lehrman, S. (2016). Trust project summit report. https://n36.08b.myftpupload.com/wp-content/uploads/2019/03/Summit_Report_Hearst20May_ms_sl-1.pdf
  22. Lewis, S. C., Holton, A. E., & Coddington, M. (2014). Reciprocal journalism: A concept of mutual exchange between journalists and audiences. Journalism Practice, 8(2), 229-241. https://doi.org/10.1080/17512786.2013.859840
  23. Liu, Z., Shabani, S., Balet, N. G., & Sokhn, M. (2019). Detection of satiric news on social media: analysis of the phenomenon with a French dataset. Proceedings of the 28th International Conference on Computer Communication and Networks (pp. 1-6). https://doi.org/10.1109/ICCCN.2019.8847041
  24. Liu, Z., Shan, J., Delaloye, M., Piguet, J. G., & Glassey, N. B. (2020). The role of public trust and media in managing the dissemination of COVID-19-related news in Switzerland. Journalism and Media, 1(1), 145-158. https://doi.org/10.3390/journalmedia1010010
  25. Newman, N., & Fletcher, R. (2017). Bias, bullshit and lies: Audience perspectives on low trust in the media. http://reutersinstitute.politics.ox.ac.uk/our-research/bias-bullshit-and-lies-audience-perspectives-low-trust-media/. https://doi.org/10.2139/ssrn.3173579
  26. Nørregaard, J., Horne, B. D., & Adali, S. (2019). Nela-gt-2018: A large multi-labelled news dataset for the study of misinformation in news articles. Proceedings of the International AAAI Conference on Web and Social Media (pp. 630-638).
  27. Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications, 540(123174), 1-17. https://doi.org/10.1016/j.physa.2019.123174
  28. Peacock, C., Masullo, G. M., & Stroud, N. J. (2020). What’s in a label? The effect of news labels on perceived credibility. Journalism. https://doi.org/10.1177/1464884920971522
  29. Publico. (2018). This is how the editorial transparency calculator of ‘Público’ works. https://blogs.publico.es/publico/2018/10/10/this-is-how-the-editorial-transparency-calculator-of-publico-works/
  30. Shabani, S., & Sokhn, M. (2018). Hybrid machine-crowd approach for fake news detection. Proceedings of the 2018 IEEE 4th International Conference on Collaboration and Internet Computing (pp. 299-306). https://doi.org/10.1109/CIC.2018.00048
  31. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22-36. https://doi.org/10.1145/3137597.3137600
  32. Srivastava, A., & Thomson, S. B. (2009). Framework analysis: A qualitative methodology for applied policy research, Journal of Administration Governance, 4(2), 72-79.
  33. Starbird, K. (2017). Examining the alternative media ecosystem through the production of alternative narratives of mass shooting events on Twitter. Proceedings of the International AAAI Conference on Web and Social Media (pp. 230-239).
  34. Stefanov, P., Darwish, K., Atanasov, A., & Nakov, P. (2020). Predicting the topical stance and political leaning of media using tweets. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 527-537). https://doi.org/10.18653/v1/2020.acl-main.50
  35. Stelter, B. (2018). This start-up wants to evaluate your news sources. https://money.cnn.com/2018/03/04/media/newsguard-steven-brill-gordon-crovitz/index.html
  36. Van Zandt, D. (2021). Media bias/fact check. https://mediabiasfactcheck.com/about/
  37. Wang, W. Y. (2017). “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (pp. 422-426). https://doi.org/10.18653/v1/P17-2067
  38. Zhang, A. X., Ranganathan, A., Metz, S. E., Appling, S., Sehat, C. M., Gilmore, N., Adams, N. B., Vincent, E., Lee, J., Robbins, M., Bice, E., Hawke, S., & Karger, D. (2018). A structured response to misinformation: Defining and annotating credibility indicators in news articles. Proceedings of The Web Conference 2018 (pp. 603-612). https://doi.org/10.1145/3184558.3188731