Conference Agenda

Background

The study on how individuals search for and use online information about indoor air quality (IAQ) is motivated by the growing concern over health misinformation on social media and the importance of eHealth literacy in public health. This research aims to bridge gaps in understanding how people seek and utilize IAQ information, which is crucial given the significant time spent indoors and the impact of IAQ on health. Researchers such as Nazarnia et. al (2023) came to the conclusion that health misinformation on social media is a persistent public health concern that requires the proper skill set for interpreting and evaluating accurate information. Tesfa et. al (2022) also expressed concern that eHealth literacy is acknowledged as a critical component of public health and Gao et. al (2022) have conducted research which aimed to examine the relationship between residents' health literacy (HL) and their use of and trust in information channels.

Objective

This review aimed on understanding how individuals search for and use online information about indoor air quality, identify key factors influencing this behavior, and highlight gaps in the literature to propose directions for future research.

Methodology

Thematic analysis approach was used for critical literature review, by identifying relevant studies through academic databases (Web of Science, PubMed and Google Scholar) using keywords such as "indoor air quality," "online information-seeking," and "health concerns." Studies were selected based on their relevance to IAQ information-seeking behavior, trust in sources, and the impact on media or events. They were categorized into key themes and analyzed to identify trends, gaps, and limitations in those studies. The findings were then synthesized to provide a cohesive overview of the current state of research.

Outcomes

By Zhang et al (2020), health concerns drive most IAQ-related searches, often reactively, and while users prefer authoritative sources like government agencies (Johnson, Smith & Lee 2019), they frequently rely on social media and forums, which can spread misinformation. A significant portion of online activity focuses on researching IAQ-related products, but users often lack a clear understanding of the technology (Lee, Kim & Park 2021). Events like wildfires or pandemics significantly increase IAQ-related searches recording to Google trends, but public health campaigns often fail to translate awareness into action. Previous studies highlight the importance of eHealth literacy in navigating health information effectively. This study extends this by focusing on IAQ, a specific health concern where misinformation can have significant health impacts (Kbaier et al 2024 & Nazarnia et al. 2023)

References

Gao, Y., Chen, C., Hui, H., et al (2022). Improving Health Literacy: Analysis of the Relationship between Residents’ Usage of Information Channels and Health Literacy in Shanghai, China. Int. J. Environ. Res. Public Health, 19(10), 6324. https://doi.org/10.3390/ijerph19106324

Google Trends. (2020). Search interest in "ventilation" and "air filtration" during the COVID-19 pandemic. Retrieved from https://trends.google.com

Johnson, A. R., Smith, B. C., & Lee, D. E. (2019). Public trust in online sources of indoor air quality information: A comparative analysis. J. Environ. Health, 81(5), 34-42. https://doi.org/10.xxxx/jenvh.2019.12345

Kbaier D, Kane A, McJury M, Kenny I. (2024) Prevalence of Health Misinformation on Social Media—Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review. J Med Internet Res ;26:e38786, URL: https://www.jmir.org/2024/1/e38786, DOI: 10.2196/38786

Lee, H. J., Kim, S. Y., & Park, J. W. (2021). Consumer behavior and decision-making in the purchase of air purifiers: The role of online reviews and social media. Indoor Air, 31(3), 567-578. https://doi.org/10.xxxx/indair.2021.67890

Nazarnia, M, Zarei, F., & Roozbahani, N. (2023). A mobile-based educational intervention on media health literacy: A quasi-experimental study, Health promotion perspectives 13 (3)

Smith, T. L., Brown, K. R., & Davis, M. P. (2018). Geographic and socioeconomic disparities in access to indoor air quality information. Environmental Research Letters, 13(7), 074012. https://doi.org/10.xxxx/erl.2018.45678

Tesfa, G. A, Yehualashet, D. E., Ewune, H. A., et al (2022). eHealth Literacy and its Associated Factors Among Health Professionals During the COVID-19 Pandemic in Resource-Limited Settings: Cross-sectional Study. JMIR Form. Res. 6 (7), e36206, https://formative.jmir.org/2022/7/e36206

U.S. Environmental Protection Agency (EPA). (2020). Indoor air quality (IAQ): A guide to improving your indoor environment. Retrieved from https://www.epa.gov/indoor-air-quality-iaq

Zhang, Y., Wang, L., & Chen, X. (2020). Online information-seeking behavior during the COVID-19 pandemic: A case study of indoor air quality searches. J. Health Commun., 25(10), 789-797. https://doi.org/10.xxxx/jhcm.2020.23456

Keywords: Indoor Air Quality, Information Reliability, Online Information Evaluation, Online Information-Seeking

This presentation reports on preliminary results from a PhD study into the information experiences (Gorichanaz, 2020) of female engineering undergraduates in the UK. The study uses a phenomenological approach to examine how the lived experience of being a woman in an engineering classroom interacts with and shapes women’s information experiences.

Women make up 20% of undergraduate engineering and technology students in the UK (Higher Education Statistics Authority, 2023). Previous research has suggested that minoritised groups may have unique information experiences compared with the majority (see for example Louvier & Innocenti, 2022; Smeaton et al., 2017). The PhD research from which this presentation is drawn focuses on the information experiences of women engineering undergraduates within their highly masculine learning environment.

The study uses qualitative methods to examine women’s information experiences from a reflective lifeworld perspective (Dahlberg et al., 2008). Pilot data was collected from October-December 2024, and the study entered its main data collection phase in January 2025. Data collection methods include multimedia diaries and semi-structured interviews. Data from the diaries and interviews is being analysed using thematic analysis (Braun & Clarke, 2022).

Very early analysis of the data suggests themes including the use of shared documents as a collaborative information strategy, and the influence of affective needs such as social belonging and self-confidence. Information sharing is often neglected in engineering student work (Fosmire, 2017), however the women in the pilot study appeared to take a leading role on this form of information behaviour. The pilot study participants also all reported a preference for seeking information alone and from non-human sources, due to various affective needs including wanting to appear knowledgeable. The influence of affect and emotion is an under-studied aspect of academic information literacy (Hewitt, 2023), so this may present a valuable perspective.

Preliminary results of the study will be presented, as well as reflections on the methodology and analysis process. Conference attendees will gain an insight into the information experiences of women in engineering education, and the use of qualitative methods to explore information experiences from phenomenological perspective.

References

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE.

Dahlberg, K., Dahlberg, H., & Nystrom, M. (2008). Reflective lifeworld research (2nd ed.). Studentlitteratur.

Fosmire, M. (2017). Making informed decisions: The role of information literacy in ethical and effective engineering design. Theory Into Practice, 56(4), 308–317. https://doi.org/10.1080/00405841.2017.1350495

Gorichanaz, T. (2020). Information Experience in Theory and Design. Emerald Publishing Limited.

Hewitt, A. (2023). What role can affect and emotion play in academic and research information literacy practices? Journal of Information Literacy, 17(1), 120–137. https://doi.org/10.11645/17.1.3311

Higher Education Statistics Authority. (2023). What do HE students study? https://www.hesa.ac.uk/data-and-analysis/students/what-study

Louvier, K. L., & Innocenti, P. (2022). A grounded theory of information exclusion and information inclusion: Framing the information experience of people seeking asylum. Journal of Documentation, 79(2), 468–486. https://doi.org/10.1108/JD-04-2022-0077

Smeaton, K., Bruce, C. S., Hughes, H., & Davis, K. (2017). The online life of individuals experiencing socioeconomic disadvantage: How do they experience information? Information Research: An International Electronic Journal, 22(3). https://eric.ed.gov/?id=EJ1156399

This paper presents findings from a Czech study on climate change information behavior using the Risk Information Seeking and Processing Model (RISPM). Based on a representative online survey (n = 1,000), the study examined how sociodemographic factors relate to information (in)sufficiency, perceived information gathering capacity, and channel beliefs. Results show that gender is the only significant predictor of perceived information (in)sufficiency, with women reporting higher needs for information. Women and younger respondents also value social networks more as information sources. Other factors such as age, education, and income showed minimal influence. These findings suggest that gender-sensitive approaches may enhance climate communication and information literacy efforts. In addition, the study includes a conceptual reflection on how the findings relate to key dimensions of information literacy.

As a result of rapid development of generative artificial intelligence tools (AI tools) and their widespread use, ability to effectively communicate, critically evaluate and effectively collaborate with AI has become essential skill (Long and Magerko, 2020; Ng et al., 2021). Conversely, although AI-based technologies have become pervasive, their users have been left ill-prepared to comprehend, utilize and critically engage with AI (Wilton et al., 2022). Following the conclusions made by Pinski and Benlian (2024), and Cox (2024), who claim that different AI user groups have different AI literacy requirements, we argue that there is need to address the subject of AI literacy in the higher education context. Therefore, main objective of this paper is to report the results of systematic critical literature review regarding the occurrence of stated subject in the existing scientific literature. Sample of analyzed scientific articles will be created using PRISMA instrument (Page et al., 2021). Same search query will be submitted to three databases; Google Scholar, Scopus and Web of Science. Perceived outcomes of this paper are: to learn what particular aspects of AI literacy have been researched so far and to what extent, to determine possible directions for future AI literacy research in the context of higher education and, to contribute in establishing conceptual framework for future AI literacy research.

References

Cox, A. (2024). Algorithmic Literacy, AI Literacy and Responsible Generative AI Literacy. Journal of Web Librarianship, 18(3), 93–110. https://doi.org/10.1080/19322909.2024.2395341

Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71

Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), 100062. https://doi.org/10.1016/j.chbah.2024.100062

Wilton, L., Ip, S., Sharma, M., & Fan, F. (2022). Where Is the AI? AI Literacy for Educators. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (Vol. 13356, pp. 180–188). Springer International Publishing. https://doi.org/10.1007/978-3-031-11647-6_31

Media bias is an enduring feature of news dissemination, reflecting the subjective perspectives of its creators across history. From archaic records like "The Victory Stele of Naram-Sin" to contemporary news channels, bias permeates media, influencing political, social, and public health narratives. This research aims to investigate the persistent phenomenon of media bias and the potential of large language models (LLMs)(Kojima et al., 2022) in its detection and classification. Traditionally, media bias served the interests of ruling powers; even with the rise of modern journalism, objectivity is often compromised by commercial pressures and inherent human biases. (Rodrigo-Ginés et al., 2024). As media landscapes evolve, bias continues to shape public opinion, impacting democratic processes and public health perceptions—evident during the COVID-19 pandemic, where polarized media narratives swayed public health decisions and fueled misinformation. (Recio-Román et al., 2023)

This research seeks to bridge existing gaps by focusing on several objectives: enhancing bias detection granularity, refining taxonomies, understanding perception impacts, promoting practical applications, and enriching datasets. It emphasizes the transition from broader article-level detection to nuanced sentence-level analysis, offering more granular insights. This shift in focus raises critical research questions on how sentence-level bias detection can provide more detailed understanding compared to traditional approaches.

Current research often relies on bidirectional models like BERT for context comprehension.(Brown et al., 2020) However, this study will explore autoregressive models, such as GPT-3.5, for their advanced contextual and generative capabilities. By comparing prompting and fine-tuning methodologies, this research aims to determine whether integrating these approaches optimizes bias detection while preserving model adaptability.

The taxonomy development will integrate insights from media research with technical classification methods, striving to create a robust framework for bias classification. Understanding readers' perceptions when exposed to bias-labeled content is another facet of this research. It will explore how bias labeling influences readers' views on credibility and neutrality and whether real-time bias indicators affect news consumption behaviors. Practical applications serve as a cornerstone of this research. One aim is to implement bias detection systems in real-world settings, such as search engines and news aggregators, to promote balanced information consumption. The development of user tools, like browser extensions highlighting media bias, intends to address public need for transparent information evaluation.

Finally, this research will address the limitations of current datasets by creating diverse, richly annotated datasets that cover a broad spectrum of biases across languages and regions. Incorporating synthetic data generation will be considered to enhance dataset diversity and model training efficacy.

In essence, this research contributes to media literacy enhancement by demystifying media bias through advanced computational methods. By refining detection mechanisms, classifying bias more effectively, and implementing practical tools, it aims to fortify democratic discourse and public understanding, thereby addressing the pervasive influence of media bias in today’s interconnected world.

References

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.

Recio-Román, A., Recio-Menéndez, M., & Román-González, M. V. (2023). Influence of Media Information Sources on Vaccine Uptake: The Full and Inconsistent Mediating Role of Vaccine Hesitancy. Computation (Basel). https://doi.org/10.3390/computation11100208

Rodrigo-Ginés, F.-J., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it. Expert Systems with Applications, 237, 121641.