Impact and Use of Artificial Intelligence in Risk Communication: Challenges and New Opportunities
DOI:
https://doi.org/10.52152/RCR.V12.12Keywords:
Artificial Intelligence, Risk Communication, Health, Social Networks, ReviewAbstract
Artificial Intelligence (AI) is having a growing impact on society, and its presence is increasing in many areas. At the same time, risk communication has been gaining prominence and importance in both society, especially in the wake of the recent COVID-19 pandemic, and academia. In view of the magnitude of both phenomena, this article aims to identify the different points and aspects where they converge, as well as the potential theoretical and practical implications of AI in risk communication. To this end, we carried out an exploratory, systematic review of the scientific literature from a holistic perspective, taking a mixed methods approach that considered both quantitative and qualitative aspects in order to analyse the state of academic research and its implications. The results show a marked increase in scientific production that addresses both concepts jointly, particularly from 2019 onward, coinciding with the COVID-19 pandemic, with this risk being the main subject of study. Moreover, social networks, especially X (formerly Twitter), emerge as the most interesting platforms for research, while other platforms receive a lower level of attention. Our findings suggest that AI has a dual impact on risk communication, presenting both challenges by generating new risk scenarios, and opportunities by providing new methods that allow new horizons to be explored. Finally, different theoretical and practical implications arise from this research, and it is necessary to address the challenges and take advantage of the opportunities provided by AI to improve risk communication.
References
Abadía, A., Manfredi, L. C., & Sayago, J. T. (2023). Crisis communication during the COVID-19 pandemic and its impact on citizens feelings. Opiniao Publica, 29(1), 199-225.
Aguaded, I., Vizcaíno-Verdú, A., García-Prieto, V., & de-Casas-Moreno, P. (2023). The impact of post-pandemic learning loss on education development: A systematic review. Review of Communication Research, 11, 172-189. https://doi.org/10.5680/RCR.V11.7
Al Momin, K., Kays, H., & Sadri, A. M. (2023). Identifying crisis response communities in online social networks for compound disasters: The case of hurricane Laura and COVID-19. Transportation Research Record. https://doi.org/10.1177/03611981231168120
Ali, K., Zain-ul-abdin, K., Li, C., Johns, L., Ali, A. A., & Carcioppolo, N. (2019). Viruses going viral: Impact of fear- arousing sensationalist social media messages on user engagement. Science Communication, 41(3), 314-338. https://doi.org/10.1177/1075547019846124
Babic, K., Petrovic, M., Beliga, S., Martincic-Ipsic, S., Matesic, M., & Mestrovic, A. (2021). Characterisation of COVID-19-related tweets in the Croatian language: Framework based on the cro-CoV-cseBERT model. Applied Sciences-Basel, 11(21). https://doi.org/10.3390/app112110442
Bedregal, P., Besoain, C., Reinoso, A., & Zubarew, T. (2017). Qualitative research methodology in health care. Revista Medíca de Chile, 145(3), 373-379.
Beedasy, J., Zúñiga, A., Chandler, T., & Slack, T. (2020). Online community discourse during the deepwater horizon oil spill: An analysis of Twitter interactions. International Journal of Disaster Risk Reduction. https://doi.org/5110.1016/j.ijdrr.2020.101870
Beliga, S., Martincic-Ipsic, S., Matesic, M., Vuksanovic, I. P., & Mestrovic, A. (2021). Infoveillance of the Croatian online media during the COVID-19 pandemic: One-year longitudinal study using natural language processing. Jmir Public Health and Surveillance, 7(12). https://doi.org/10.2196/31540
Bouder, F., Perko, T., Lofstedt, R., Renn, O., Rossmann, C., Hevey, D., . . . Reifenhäuser, C. (2021). The Potsdam radon communication manifesto. Journal of Risk Research, 24(7), 909-912.
Brown-Devlin, N., Lim, H. S., & Tao, J. Y. (2022). Examining the influence of algorithmic message personalization on source credibility and reputation. International Journal of Business Communication. https://doi.org/10.1177/23294884221126489
Bukar, U. A., Sidi, F., Jabar, M. A., Nor, R., Abdullah, S., & Ishak, I. (2022). A multistage analysis of predicting public resilience of impactful social media crisis communication in flooding emergencies. IEEE Access, 10, 57266- 57282.
Catalan-Matamoros, D., Prieto-Sanchez, I., & Langbecker, A. (2023). Crisis communication during COVID-19: English, French, Portuguese, and Spanish discourse of AstraZeneca vaccine and omicron variant on social media. Vaccines, 11(6). https://doi.org/10.3390/vaccines11061100
Chang, A., Schulz, P. J., Tu, S., & Liu, M. T. (2020). Communicative blame in online communication of the COVID-19 pandemic: Computational approach of stigmatizing cues and negative sentiment gauged with automated analytic techniques. Journal of Medical Internet Research, 22(11). https://doi.org/10.2196/21504
Che, S. P., & Kim, J. H. (2023). Sentiment impact of public health agency communication strategies on TikTok under COVID-19 normalization: Deep learning exploration. Journal of Public Health-Heidelberg. https://doi.org/10.1007/s10389-023-01921-5
Che, S. P., Zhou, Y. H., Zhang, S., Nan, D. Y., & Kim, J. H. (2023). Impact of ByteDance crisis communication strategies on different social media users. Humanities & Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02170-3
Cherry, M. G., Boland, A., & Dickson, R. (Eds.). (2023). Doing a systematic review: A student′s guide (3rd ed.). London, UK: Sage.
Codina, L. (2018). Revisiones bibliográficas sistematizadas: Procedimientos generales y Framework para ciencias humanas y sociales [Systematic literature reviews: General procedures and framework for humanities and social sciences] (Master’s thesis, Universitat Pompeu Fabra, Barcelona, Spain). Retrieved from http://hdl.handle.net/10230/34497
Covello, V. T., Slovic, P., & Von Winterfeldt, D. (1986). Risk communication: A review of the literature. Risk Abstracts, 3, 171-182.
Covello, V. T., Von Winterfeldt, D., & Slovic, P. (1988). Risk communication. In C.C. Travis (Ed.), Carcinogen risk assessment (pp. 193-207). https://doi.org/10.1007/978-1-4684-5484-0_15
Cunneen, M., Mullins, M., & Murphy, F. (2019). Artificial intelligence assistants and risk: Framing a connectivity risk narrative. AI & Society, 35, 625-634.
Dehghani, A., Ghomian, Z., Rakhshanderou, S., Khankeh, H., & Kavousi, A. (2022). Process and components of disaster risk communication in health systems: A thematic analysis. Jamba: Journal of Disaster Risk Studies, 14(1). https://doi.org/10.4102/JAMBA.V14I1.1367
DiClemente, R. J., & Jackson, J. M. (2016). Risk communication. International Encyclopedia of Public Health, 378-382. https://doi.org/10.1016/B978-0-12-803678-5.00389-1
Ems, L., & Gonzales, A. L. (2016). Subculture-centered public health communication: A social media strategy. New Media & Society, 18(8), 1750-1767.
Fineberg, H. V., & Stern, P. C. (Eds.). (1996). Understanding risk: Informing decisions in a democratic society. Washington, DC: The National Academies Press.
Frewer, L. (2004). The public and effective risk communication. Toxicology Letters, 149(1), 391-397.
García García, S. (2014). Las narrativas del riesgo. Revista de Antropología Social, 23, 281-286.
García-Orosa, B. (2021). Disinformation, social media, bots, and astroturfing: The fourth wave of digital democracy. El Profesional De La Informacion, 30(6). https://doi.org/10.3145/epi.2021.nov.03
García-Orosa, B., Canavilhas, J., & Vázquez-Herrero, J. (2023). Algoritmos y comunicación: Revisión sistematizada de la literatura. Comunicar (Huelva, Spain), 31(74), 9-21.
Gates, C. S., Li, N., Peng, H., Sarma, B., Qi, Y., Potharaju, R., . . . Molloy, I. (2014). Generating summary risk scores for mobile applications. IEEE Transactions on Dependable and Secure Computing, 11(3), 238-251.
Glik, D. C. (2007). Risk communication for public health emergencies. Annual Review of Public Health, 28(1), 33- 54.
Goh, O. S., & Fung, C. C. (2005). Automated knowledge extraction from Internet for a crisis communication portal. In L. Wang & Y. Jin (Eds.), Fuzzy Systems and Knowledge Discovery: Second International Conference, FSKD 2005 (pp. 1226-1235). Berlin, Germany: Springer.
Gui, X., Wang, Y., Kou, Y., Reynolds, T. L., Chen, Y., Mei, Q., & Zheng, K. (2017). Understanding the patterns of health information dissemination on social media during the Zika outbreak. AMIA...Annual Symposium Proceedings. AMIA Symposium, 2017, 820-829.
Heydari, S. T., Zarei, L., Sadati, A. K., Moradi, N., Akbari, M., Mehralian, G., & Lankarani, K. B. (2021). The effect of risk communication on preventive and protective Behaviours during the COVID-19 outbreak: Mediating role of risk perception. BMC Public Health, 21. https://doi.org/10.1186/s12889-020-10125-5
Hohma, E., Boch, A., Trauth, R., & Lütge, C. (2023). Investigating accountability for artificial intelligence through risk governance: A workshop-based exploratory study. Frontiers in Psychology, 14, 1073686. https://doi.org/10.3389/fpsyg.2023.1073686
Hu, Q., & Zhong, W. (2023). State-level politicization of crisis communication on Twitter during COVID-19: Conceptualization, measurement, and impacts. Public Administration Review, 83(5), 1266-1280.
Huang, Y., & Wang, W. (2022). When a story contradicts: Correcting health misinformation on social media through different message formats and mechanisms. Information, Communication & Society, 25(8), 1192-1209. https://doi.org/10.1080/1369118X.2020.1851390
Karinshak, E., & Jin, Y. (2023). AI-driven disinformation: A framework for organizational preparation and response. Journal of Communication Management, 27(4), 539-562. https://doi.org/10.1108/JCOM-09-2022- 0113
Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., . . . Ratick, S. (1988). The social amplification of risk: A conceptual framework. Risk analysis, 8(2), 177-187. https://doi.org/10.1111/j.1539- 6924.1988.tb01168.x
Kaur, M., Verma, R., & Otoo, F. N. K. (2021). Emotions in leader’s crisis communication: Twitter sentiment analysis during COVID-19 outbreak. Journal of Human Behavior in the Social Environment, 31(1-4), 362-372. https://doi.org/10.1080/10911359.2020.1829239
Lazard, A. J., Scheinfeld, E., Bernhardt, J. M., Wilcox, G. B., & Suran, M. (2015). Detecting themes of public concern: A text mining analysis of the centers for disease control and prevention's ebola live twitter chat. American Journal of Infection Control, 43(10), 1109-1111. https://doi.org/10.1016/j.ajic.2015.05.025
Lee, C. H., & Yu, H. (2020). The impact of language on retweeting during acute natural disasters: Uncertainty
reduction and language expectancy perspectives. Industrial Management & Data Systems, 120(8), 1501-1519. https://doi.org/10.1108/IMDS-12-2019-0711
Liu, X. H., Kar, B., Zhang, C. Y., & Cochran, D. M. (2019). Assessing relevance of tweets for risk communication. International Journal of Digital Earth, 12(7), 781-801. https://doi.org/10.1080/17538947.2018.1480670
Mannes, A. (2020). Governance, risk, and artificial intelligence. AI Magazine, 41(1), 61-69.
McComas, K. A. (2006). Defining moments in risk communication research: 1996-2005. Journal of Health Communication, 11(1), 75-91. https://doi.org/10.1080/10810730500461091
Michela, E., Rosenberg, J. M., Kimmons, R., Sultana, O., Burchfield, M. A., & Thomas, T. (2022). "We are trying to communicate the best we can": Understanding districts' communication on Twitter during the COVID-19 pandemic. Aera Open, 8. https://doi.org/10.1177/23328584221078542
Milani, E., Weitkamp, E., & Webb, P. (2020). The visual vaccine debate on Twitter: A social network analysis. Media and Communication, 8(2), 364-375. https://doi.org/10.17645/mac.v8i2.2847
Mir, A. A., Rathinam, S., & Gul, S. (2022). Public perception of COVID-19 vaccines from the digital footprints left on twitter: Analyzing positive, neutral and negative sentiments of Twitterati. Library Hi Tech, 40(2), 340-356. https://doi.org/10.1108/LHT-08-2021-0261
Moher, D. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264. https://doi.org/10.1371/journal.pmed.1000097
Moura, R., Beer, M., Patelli, E., & Lewis, J. (2017). Learning from major accidents: Graphical representation and analysis of multi-attribute events to enhance risk communication. Safety Science, 99, 58-70. https://doi.org/10.1016/j.ssci.2017.03.005
National Research Council. (1989). Improving risk communication. Washington, DC: The National Academy Press.
Ng, R., Chow, T., & Yang, W. S. (2021). News media narratives of COVID-19 across 20 countries: Early global convergence and later regional divergence. PLOS One, 16(9). https://doi.org/10.1371/journal.pone.0256358
Nguyen, A., & Catalan-Matamoros, D. (2020). Digital mis/disinformation and public engagement with health and science controversies: Fresh perspectives from COVID-19. Media and Communication, 8(2), 323-328. https://doi.org/10.17645/mac.v8i2.3352
Ni, B., Wu, F., & Huang, Q. (2023). When artificial intelligence voices human concerns: The paradoxical effects of AI voice on climate risk perception and pro-environmental behavioral intention. International Journal of Environmental Research and Public Health, 20(4). https://doi.org/10.3390/ijerph20043772
Ogie, R. I., Rho, J. C., & Clarke, R. J. (2018, December). Artificial intelligence in disaster risk communication: A systematic literature review. In 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) (pp. 1-8). https://doi.org/10.1109/ICT-DM.2018.8636380
Oladipo, T. (2024). 20+ top social media sites and platforms to grow your brand in 2024. Retrieved from https://buffer.com/library/social-media-sites/
Park, S., Han, S., Kim, J., Molaie, M. M., Vu, H. D., Singh, K., . . . Cha, M. (2021). COVID-19 discourse on Twitter in four Asian countries: Case study of risk communication. Journal of Medical Internet Research, 23(3), e23272. https://doi.org/10.2196/23272
Pascual-Presa, N., Fernández-Pichel, M., Losada, D. E., & García-Orosa, B. (2024). Web search or conversation with an artificial intelligence? Analysis of misinformation and relevance in the case of radon gas. Profesional De La información, 33(2). https://doi.org/10.3145/epi.2024.0220
Petty, R. E., & Briñol, P. (2011). The elaboration likelihood model. Handbook of Theories of Social Psychology, 1, 224-245.
Plough, A., & Krimsky, S. (2013). The emergence of risk communication studies: Social and political context. In T. S. Glickman & M. Gough (Eds.), Readings in risk (pp. 223-231). Washington, DC: RFF Press.
Prahl, A., & Goh, W. (2021). "Rogue machines" and crisis communication: When AI fails, how do companies publicly respond? Public Relations Review, 47(4). https://doi.org/10.1016/j.pubrev.2021.102077
Purnat, T. D., Vacca, P., Czerniak, C., Ball, S., Burzo, S., Zecchin, T., . . . Nguyen, T. (2021). Infodemic signal detection during the COVID-19 pandemic: Development of a methodology for identifying potential information voids in online conversations. JMIR Infodemiology, 1(1), e30971. https://doi.org/10.2196/30971
Ragini, J. R., Anand, P. M. R., & Bhaskar, V. (2018). Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management, 42, 13-24. https://doi.org/10.1016/j.ijinfomgt.2018.05.004
Ramírez, M., & García-Peñalvo, F. (2018). Co-creacion e innovacion abierta: Revision sistematica de literatura. Comunicar (Huelva, Spain), 26(54), 9-18. https://doi.org/10.3916/C54-2018-01
Rasmussen, J., & Ihlen, Ø. (2017). Risk, crisis, and social media. Nordicom Review, 38(2). https://doi.org/10.1515/nor-2017-0393
Renn, O. (1991). Risk communication and the social amplification of risk. Communicating Risks to the Public, 287-324. https://doi.org/10.1007/978-94-009-1952-5_14
Rusho, M. A., Ahmed, M. A., & Sadri, A. M. (2021). Social media response and crisis communications in active shootings during COVID-19 pandemic. Transportation Research Interdisciplinary Perspectives, 11. https://doi.org/10.1016/j.trip.2021.100420
Sabariego Puig, M., Vilà Baños, R., & Sandín Esteban, M. P. (2014). El análisis cualitativo de datos con ATLAS. ti. REIRE. Revista d’Innovació i Recerca En Educacion, 7(2), 119-133.
Sadri, A. M., Hasan, S., Ukkusuri, S. V., & Cebrian, M. (2018). Crisis communication patterns in social media during Hurricane Sandy. Transportation Research Record, 2672(1), 125-137.
Sánchez, D. E., Méndez, C. A., Brun, G., Traub-Muñoz, M., & Barros, L. C. (2023). Artificial intelligence fuzzy system for the design of confinements during quarantines. Revista Medica De Chile, 151(2), 197-205.
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1), 54. https://doi.org/10.1186/s41239-021-00292-9
Shanahan, E. A., Reinhold, A. M., Raile, E. D., Poole, G. C., Ready, R. C., Izurieta, C., . . . King, H. (2019). Characters matter: How narratives shape affective responses to risk communication. PLOS One, 14(12). https://doi.org/10.1371/journal.pone.0225968
Siegel, Z. S., & Kulp, S. A. (2021). Superimposing height-controllable and animated flood surfaces into street-level photographs for risk communication. Weather and Climate Extremes, 32. https://doi.org/10.1016/j.wace.2021.100311
Silva-Torres, J. J., Martínez-Martínez, L., & Cuesta-Cambra, U. (2020). Design of a visual attention model for communication campaigns: The case of COVID-19. Profesional De La Informacion, 29(6). https://doi.org/10.3145/epi.2020.nov.27
Sixto-García, J., García-Orosa, B., González-Lois, E., & Pascual-Presa, N. (2024). Transparencia en YouTube para la comunicación del riesgo del radón. Revista Latina de Comunicación Social, (83). https://doi.org/10.4185/rlcs- 2025-2266
Solmaz, B., Urhan, B., Tarakci, H. N., & Gazaz, D. C. (2022). Sustainability in the age of pandemic: A study on proactive crisis communication in social media by Republic of Turkey ministry of culture and tourism. Turkiye Iletisim Arastirmalari Dergisi-Turkish Review of Communication Studies, (39), E132-E153. https://doi.org/10.17829/turcom.1019836
Stieglitz, S., Hofeditz, L., Brünker, F., Ehnis, C., Mirbabaie, M., & Ross, B. (2022). Design principles for conversational agents to support emergency management agencies. International Journal of Information Management, 63. https://doi.org/10.1016/j.ijinfomgt.2021.102469
Sung, J. J., Stewart, C. L., & Freedman, B. (2020). Artificial intelligence in health care: Preparing for the fifth industrial revolution. Medical Journal of Australia, 213(6), 253-255.
Tecuci, G. (2012). Artificial intelligence. Wiley Interdisciplinary Reviews. Computational Statistics, 4(2), 168-180.
Terren, L. & Borge, R. (2021). Echo chambers on social media: A systematic review of the literature. Review of Communication Research, 9, 99-118. https://doi.org/10.12840/ISSN.2255-4165.028
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
Unlu, A., Truong, S., Tammi, T., & Lohiniva, A. L. (2023). Exploring political mistrust in pandemic risk communication: Mixed-method study using social media data analysis. Journal of Medical Internet Research. https://doi.org/10.2196/50199
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
Vaughan, E., & Tinker, T. (2009). Effective health risk communication about pandemic influenza for vulnerable populations. American Journal of Public Health, 99(S2). https://doi.org/10.2105/AJPH.2009.162537
Vizoso, Á, & Pérez-Seijo, S. (2024). Digitalización, política e inteligencia artificial: Revisión sistematizada de la producción científica. Más Poder Local, (56), 64-82. https://doi.org/10.56151/maspoderlocal.219
Wang, H., Xiong, L., Guo, J., Lu, M., & Meng, Q. (2023). Predicting the antecedents of discontinuous usage intention of mobile government social media during public health emergencies. International Journal of Disaster Risk Reduction, 87. https://doi.org/10.1016/j.ijdrr.2023.103582
Wang, Y. (2022). Emergency risk communication: A structural topic modelling analysis of the UK government's COVID-19 press briefings. NJES Nordic Journal of English Studies, 21(2), 226-251.
Weeks, R., Sangha, P., Cooper, L., Sedoc, J., White, S., Gretz, S., . . . Bar-Zeev, N. (2023). Usability and credibility of a COVID-19 vaccine chatbot for young adults and health workers in the United States: Formative mixed methods study. Jmir Human Factors. https://doi.org/10.2196/40533
Wirz, C. D., Xenos, M. A., Brossard, D., Scheufele, D., Chung, J. H., & Massarani, L. (2018). Rethinking social amplification of risk: Social media and Zika in three languages. Risk Analysis, 38(12), 2599-2624. https://doi.org/10.1111/risa.13228
Xu, M., Wei, Z., & Wu, J. (2022). How emotional communication happens in social media: Predicting “Arousal- homophily-echo” emotional communication with multi-dimensional features. Telematics and Informatics Reports, 8. https://doi.org/10.1016/j.teler.2022.100019
Yan, B., Mai, F., Wu, C. J., Chen, R., & Li, X. L. (2023). A computational framework for understanding firm communication during disasters. Information Systems Research. https://doi.org/10.1287/isre.2022.0128
Yin, F. L., Wu, Z. L., Xia, X. Y., Ji, M. Q., Wang, Y. Y., & Hu, Z. W. (2021). Unfolding the determinants of COVID- 19 vaccine acceptance in China. Journal of Medical Internet Research, 23(1). https://doi.org/10.2196/26089
Yoo, W. (2019). How risk communication via Facebook and Twitter shapes behavioral intentions: The case of fine dust pollution in South Korea. Journal of Health Communication, 24(7-8), 663-673. https://doi.org/10.1080/10810730.2019.1655607
Zander, K. K., Garnett, S. T., Ogie, R., Alazab, M., & Nguyen, D. (2023). Trends in bushfire related tweets during the Australian ‘Black Summer’ of 2019/20. Forest Ecology and Management. https://doi.org/54510.1016/j.foreco.2023.121274
Zander, K. K., Nguyen, D., Mirbabaie, M., & Garnett, S. T. (2023). Aware but not prepared: Understanding situational awareness during the century flood in Germany in 2021. International Journal of Disaster Risk Reduction, 96. https://doi.org/10.1016/j.ijdrr.2023.103936
Zerfass, A., Hagelstein, J., & Tench, R. (2020). Artificial intelligence in communication management: A cross- national study on adoption and knowledge, impact, challenges and risks. Journal of Communication Management, 24(4), 377-389.
Zhang, H., Zhang, D., Wei, Z., Li, Y., Wu, S., Mao, Z., . . . Zhang, B. (2023). Analysis of public opinion on food safety in Greater China with big data and machine learning. Current Research in Food Science, 6, 100468.
Zhu, R. L., & Hu, X. (2023). The public needs more: The informational and emotional support of public communication amidst the covid-19 in China. International Journal of Disaster Risk Reduction, 84. https://doi.org/10.1016/j.ijdrr.2022.103469
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