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), SWITZERLAND
2 ESH Médias, SWITZERLAND
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 12, Issue 2, Article No: e202207.
https://doi.org/10.30935/ojcmt/11528
OPEN ACCESS 1934 Views 1266 Downloads Published online: 09 Jan 2022
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.
CITATION
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
- 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.
- 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/
- 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/
- Bennett, M., & Brandt, S. (2019). Newsguard: Fighting misinformation with nutrition labels for news. https://ylai.state.gov/newsguard-fighting-misinformation/
- 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
- 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).
- 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
- 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
- Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47(1), 1-15. https://doi.org/10.1086/268763
- 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
- 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
- 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
- ESH Média. (n.d.). ESH Médias, agir ensemble dans nos régions [ESH Médias, acting together in our regions]. https://www.eshmedias.ch/
- 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
- 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/
- Knight Foundation. (2020). American views 2020: Trust, media, and democracy. https://knightfoundation.org/reports/american-views-2020-trust-media-and-democracy
- 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
- 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/
- 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
- Le Nouvelliste. (n.d.). Le Nouvelliste, actualités du Valais [Le Nouvelliste, news from Valais]. https://www.lenouvelliste.ch/
- Lehrman, S. (2016). Trust project summit report. https://n36.08b.myftpupload.com/wp-content/uploads/2019/03/Summit_Report_Hearst20May_ms_sl-1.pdf
- 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
- 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
- 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
- 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
- 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).
- 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
- 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
- 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/
- 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
- 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
- Srivastava, A., & Thomson, S. B. (2009). Framework analysis: A qualitative methodology for applied policy research, Journal of Administration Governance, 4(2), 72-79.
- 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).
- 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
- 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
- Van Zandt, D. (2021). Media bias/fact check. https://mediabiasfactcheck.com/about/
- 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
- 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