Information Warfare, Virtual Politics, and Narrative Dominance

 

By Lilian H. Hill

As the Internet becomes more advanced, it is giving rise to new challenges for democracy. Social media platforms sort users into like-minded groups, forming echo chambers that reinforce existing beliefs. Pariser (2011) states that in a world shaped by personalization, we are shown news that aligns with our preferences and reinforces our existing beliefs. Because these filters operate invisibly, we may remain unaware of what information is excluded. This dynamic contributes to the growing disconnect between individuals with differing political views, making mutual understanding more difficult. It also enables extremist groups to harness these platforms for harmful purposes. While diverse opinions are inherent to politics, social media has created a fast-paced, ever-evolving space where political discord is continuously generated (De’Alba, 2024).

Information warfare is the strategic use of information to influence, disrupt, or manipulate public opinion, decision-making, or infrastructure, often in service of political, military, or economic goals. Instead of physical force, information warfare targets the cognitive and informational environments of adversaries. Pai (2024) comments that information warfare has become central to international politics in the Information Age in which society is shaped by the creation, use, and impact of information. According to Rid (2020), information warfare aims to undermine trust between individuals and institutions. It includes tactics like propaganda, disinformation, cyberattacks, and psychological operations. In today’s digital era, state and non-state actors use social media, news platforms, and digital technologies to conduct disinformation campaigns, often blurring the lines between truth and manipulation (Pomerantsev, 2019).

Virtual politics refers to the strategic use of digital technologies, including social media, artificial intelligence, and data analytics, to manipulate political perceptions, simulate democratic engagement, and manipulate public opinion. Originally coined in the post-Soviet context, the term captured how political elites created fake parties, opposition figures, and civil society groups to manufacture the illusion of pluralism and democratic process (Krastev, 2006). Contemporary virtual politics functions through multiple mechanisms. One tactic is the creation of simulated political actors and events, where governments or interest groups establish fake NGOs, social movements, or social media accounts to fragment opposition or feign civic engagement. These simulations create an illusion of public discourse while neutralizing dissent (Krastev, 2006). A contemporary example is Russia’s promotion of fake social media accounts and organizations during the 2016 U.S. presidential election. Russian operatives created false personas, Facebook pages, Twitter accounts, and even staged events that appeared to be organized by grassroots American groups (Mueller, 2019).

Another core feature is the widespread use of disinformation and memetic warfare. Ascott (2020) notes that while internet memes may appear harmless, memetic warfare involves the deliberate circulation of false or misleading content to polarize populations or erode trust in institutions (Marwick & Lewis, 2017). A popular meme, Pepe the Frog is a green anthropomorphic frog usually portrayed with a humanoid body wearing a blue T-shirt. Originally apolitical, it expressed simple emotions like sadness and joy. The symbol was appropriated by the alt-right (alternate-right), a far-right white nationalist movement. During the 2016 U.S. presidential election, some alt-right and white nationalist groups co-opted Pepe for propaganda, using edited versions to spread hateful or extremist messages. Another common meme, the NPC Wojak is an expressionless, grey-headed figure with a blank stare, a triangular nose, and a neutral mouth. NPC is an acronym for non-player characters, a term derived from video games. The NPC Wojak meme first appeared in 2018 to mock groups seen as conformist. The NPC meme gained traction before the 2018 U.S. midterm elections amid right-wing outrage over alleged social media censorship. Conservatives used it to portray liberals as unthinking “bots,” meaning individuals who lack internal monologue, unquestioningly accept authority, engage in groupthink, or adopt positions that reflect conformity and obedience.

The most insidious aspect of virtual politics lies in data-driven psychological manipulation. Social media and other platforms collect vast amounts of personal data that is used for targeted marketing and psychological persuasion. This shift from persuasion to manipulation erodes the foundation of informed democratic decision-making. Moreover, the performative nature of online political engagement often reduces participation to reactive, emotionally charged interactions, such as likes, shares, and outrage, instead of reasoned deliberation or civic dialogue (Sunstein, 2017).

 

Narrative Dominance and Virtual Politics

Narrative dominance refers to the phenomenon in which a particular storyline, interpretation, or framework becomes the prevailing lens through which events and realities are understood and perceived. It reflects the power to shape meaning, frame discourse, and control the perceived legitimacy of knowledge or truth. A contemporary example of narrative dominance is China’s global media campaign to reshape global perception of its handling of the COVID-19 pandemic, deflect blame, criticize Western failures, spread alternative origin theories, and suppress dissenting domestic narratives (Zhou & Zhang, 2021).

 

In media, politics, and culture, dominant narratives can marginalize alternative viewpoints and solidify ideological control. In the digital age, virtual politics is a key arena in which narrative dominance is exercised and contested. Virtual politics involves the creation and circulation of curated realities that prioritize perception over policy or truth and thrive on controlling emotional responses and engagement.

 

Virtual Politics and Democracy

The consequences of information warfare, virtual politics, and narrative dominance for democracy are profound. Together, they result in diminished trust in public institutions and blur distinctions between reality and fiction. As digital platforms become the dominant venue for political communication, traditional forms of accountability —such as investigative journalism, public debate, and civic literacy —are weakened. In authoritarian regimes, virtual politics serve as a tool for controlling dissent while projecting a false image of openness. Even in democratic societies, the same tools sway elections, fragment publics, and distort political will (Bennett & Livingston, 2018). The challenge for democratic societies, then, is to develop regulatory, technological, and civic strategies to counteract the manipulative aspects of virtual politics without undermining legitimate political speech.

 

Narrative dominance in virtual politics involves creating an environment in which alternative realities are delegitimized or neglected. Narrative dominance reflects a shift from a politics of substance to a politics of spectacle and emotional resonance. Understanding this dynamic is essential for analyzing contemporary media landscapes, political behavior, and the challenges of democratic resilience in the digital era. Virtual politics is not merely about politics taking place online; it represents a fundamental transformation in how political reality is constructed, experienced, and contested. Because public life is mediated by screens, algorithms, and data, understanding the mechanics of virtual politics is critical to preserving democratic integrity and fostering genuine political engagement.

 

References

Ascott, T. (2020, February 16). How memes are becoming the new frontier of information warfare. The Strategist. https://www.aspistrategist.org.au/how-memes-are-becoming-the-new-frontier-of-information-warfare/

Bennett, W. L., & Livingston, S. (2018). The disinformation order: Disruptive communication and the decline of democratic institutions. European Journal of Communication, 33(2), 122–139. https://doi.org/10.1177/0267323118760317

De’Alba, L. M. (2024, April 15). The virtual realities of politics: Entrenched narratives and political entertainment in the age of social media. Uttryck Magazine. https://www.uttryckmagazine.com/2024/04/15/the-virtual-realities-of-politics-entrenched-narratives-and-political-entertainment-in-the-age-of-social-media/

Gerbaudo, P. (2018). The digital party: Political organisation and online democracy. Pluto Press.

Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56–59. https://doi.org/10.1109/MC.2018.3191268

Krastev, I. (2006). Virtual politics: Faking democracy in the post-Soviet world. In Post-Soviet Affairs, 22(1), 63–67.

Marwick, A., & Lewis, R. (2017). Media manipulation and disinformation online. Data & Society Research Institute. https://datasociety.net/library/media-manipulation-and-disinfo-online/

Mueller, R. S. (2019). Report on the investigation into Russian interference in the 2016 presidential election. U.S. Department of Justice.

Pariser, E. (2011). The filter bubble: What the internet is hiding from you. Penguin.

Pomerantsev, P. (2019). This is not propaganda: Adventures in the war against reality. PublicAffairs.

Rid, T. (2020). Active measures: The secret history of disinformation and political warfare. Farrar, Straus and Giroux.

Sunstein, C. R. (2017). #Republic: Divided democracy in the age of social media. Princeton University Press.

Zhou, L., & Zhang, Y. (2021). China’s global propaganda push: COVID-19 and the strategic use of narrative. Journal of Contemporary China, 30(130), 611–628.

 

 

Data Literacy and Data Justice

 

 

By Lilian H. Hill

Data literacy is a fundamental skill set that entails the ability to read, write, understand, and communicate data in context effectively. It empowers individuals and organizations to derive meaning from data, make informed decisions, and solve problems. Data literacy is an interdisciplinary competency that integrates elements of mathematics, science, and information technology. Data literacy requires understanding data sources and constructs, analytical methods, and AI techniques (Stobierski, 2021). Data literacy is not about being a data scientist; it's about having a general understanding of data concepts and how to apply them effectively. 

The rapid expansion of digital information in today’s world has triggered a significant shift in how knowledge and skills are valued, making the ability to understand, interpret, and extract meaningful insights from data a vital competency. Schenck and Duschl (2024) comment that data increasingly drive decisions across all sectors of society, and promoting data literacy has become essential to preparing individuals to participate actively and thoughtfully in the digital age. In education, this changing environment calls for a reimagined approach that goes beyond conventional literacies, positioning data literacy as a core skill necessary for future success.

Skills of Data Literacy

Building data literacy skills is an essential process in today’s data-driven world. It begins with learning the fundamentals of data, including understanding different types such as quantitative versus qualitative data, and recognizing basic statistical concepts like mean, median, standard deviation, and correlation. Familiarity with common data formats (e.g., CSV, JSON, Excel files) lays the groundwork for deeper analytical work (Mandinach & Gummer, 2016). Introductory courses from platforms like Coursera or edX, as well as open-access tutorials and videos, offer accessible entry points for building this foundational knowledge.

To apply data literacy practically, individuals should become familiar with commonly used tools. Beginners might start with spreadsheets like Microsoft Excel or Google Sheets to learn basic data manipulation and chart creation. As comfort grows, they can explore more advanced platforms such as Tableau or Power BI for data visualization or learn coding languages like Python (using libraries such as Pandas) and SQL for deeper analysis. Practicing with real-world data available from open sources like government portals or World Bank Open Data helps bridge theory and application.

A crucial next step is learning to interpret data visualizations. Charts, graphs, and dashboards are the primary means of communicating data, and understanding how to read them critically is crucial for avoiding misinterpretation. Tools such as Gapminder or data stories from Our World in Data provide engaging ways to practice understanding patterns and trends visually (Knaflic, 2015).

Equally important is the development of critical thinking skills about data itself. This means asking questions such as: Where did the data come from? Is the sample size sufficient? Is there potential for bias or missing information? Cultivating skepticism and inquiry when reviewing data sources helps prevent the spread and influence of misinformation (Bhargava et al., 2021).

Communication is another fundamental part of data literacy. It’s not enough to understand data. The ability to clearly and ethically explain insights is equally important. This involves selecting appropriate visuals, simplifying complex ideas, and telling compelling data-driven stories (Knaflic, 2015). Platforms like Flourish or Datawrapper can help users experiment with design and narrative techniques that enhance data communication.

Ultimately, data literacy must be maintained and continually updated through ongoing learning. Schenk and Duschl (2024) call for a transformative change in educational practices, recommending a move away from formal, theory-first instruction toward contextual, inquiry-based learning. This change is viewed as crucial for equipping students with the practical skills necessary to apply data literacy effectively in real-world situations. Data literacy is not only a technical skill but also a civic and ethical one, enabling people to make informed decisions and engage in democratic processes.

Data Literacy and Social Justice

One of the core connections between big data analytics and data literacy lies in the ability to manage and critically evaluate the quality and relevance of data. Big data involves massive, unstructured datasets sourced from sensors, social media, transactional records, and more. This can introduce biases, inconsistencies, and privacy risks. Data-literate individuals are better equipped to ask critical questions: Where does the data come from? Is it representative? What algorithms are being applied? Who might be harmed by this analysis? These questions are especially important in fields like healthcare, criminal justice, education, and marketing, where big data can amplify existing societal inequities if not interpreted responsibly (boyd & Crawford, 2012).

Data justice aims to ensure that data practices do not perpetuate or exacerbate structural inequities and social injustices, but instead promote human rights, dignity, and democratic participation (Dencik & Sanchez-Monedero, 2022). The increasing dependence on data-driven technologies in all aspects of social life is a driving force behind major shifts in science, government, business, and civil society. While these changes are frequently promoted for their potential to improve efficiency and decision-making, they also introduce profound societal challenges. Data justice refers to the fair and equitable treatment of individuals and communities in the collection, analysis, use, and governance of data. It emphasizes that data are not neutral. How data are gathered, interpreted, and applied often reflect existing power structures, biases, and inequalities. Data justice has emerged as a critical framework for addressing these challenges through a lens centered on social justice. For example, if a predictive policing algorithm unfairly targets neighborhoods based on biased crime data, it may lead to over-policing in communities of color. A data justice approach would question the assumptions behind the data, advocate for community oversight, and explore alternative models that prioritize community safety without reinforcing systemic bias.

Finally, data literacy supports democratic participation in a big data society. As governments and corporations increasingly rely on data to guide decisions, including pandemic response, urban planning, and surveillance, citizens need the skills to engage with data-related policies, challenge unfair uses, and advocate for transparency and accountability. Without broad-based data literacy, power becomes concentrated in the hands of a few data-literate experts and institutions, potentially reinforcing social and economic inequalities (D’Ignazio & Klein, 2020).

References

Bhargava, R., Kadouaki, R., Bhargava, E., Castro, G., & D’Ignazio, C. (2021). Data murals: Using the arts to build data literacy. The Journal of Community Informatics, 17(1), 1–15. https://doi.org/10.15353/joci.v17i1.4602

boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878

Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review, 11(1). https://doi.org/10.14763/2022.1.1615

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

Jones, B. (2025). Data literacy fundamentals: Understanding the power and value of data (2^nd ed.). Data Literacy Press.

Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.

Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice. Teachers College Press.

Schenck, K. E., & Duschl, R. A. (2024). Context, language, and technology in data literacy. Routledge Open Research, 3(19).

            (https://doi.org/10.12688/routledgeopenres.18160.1)

Stobierski, T. (2021). Data literacy: An introduction for business. Harvard Business Review Online. https://online.hbs.edu/blog/post/data-literacy

Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2). https://doi.org/10.1177/2053951717736335

 

Big Data and Job Opportunities

 

Image Credit: Alleksana on Pexels

By Lilian H. Hill

 

Big data refers to extremely large and complex datasets generated at high speed from a wide variety of sources, including social media, sensors, transactions, and mobile devices. These datasets are so vast and varied that traditional data processing tools cannot handle them efficiently; therefore, advanced technologies and analytics are required to extract meaningful insights. Due to its size and complexity, AI is being used to make sense of the data. However, Jones (2025) points out that we cannot abdicate our responsibility for making sense of data to machines. Instead, we need to identify the mistakes AI is making and the opportunities it is missing. Relating data literacy to big data underscores the importance of developing data analysis skills in today’s world. 

 

Big data is often characterized by the 5 Vs (Saeed & Husamaldin, 2021):

1. Volume: Refers to the massive amount of data generated every second from sensors, social media, transactions, and more that organizations must store, manage, and analyze.

2. Velocity: The speed at which data are generated, processed, and analyzed. Real-time or near-real-time data processing is crucial for making informed decisions promptly.

3. Variety: Describes the different types of data, including structured, semi-structured, and unstructured, such as text, images, videos, audio, and sensor data.

4. Veracity: Focuses on data quality, accuracy, and trustworthiness. Low veracity can lead to misleading insights if the data is incomplete, inconsistent, or biased.

5. Value: Emphasizes the importance of extracting meaningful and actionable insights from data to inform decisions and generate business or societal impact.

 

Some authors (Saeed & Husamaldin, 2021) refer to 8 or even 10 Vs and include:

6. Variability: Relates to data inconsistency and the changing meaning of data over time or across contexts. For example, the same word in different datasets may have different implications.

7. Visualization: Concerns how data are represented visually to enable human understanding and insight. Effective data visualization helps communicate complex patterns and support data-driven decisions.

8. Volatility: Refers to how long data remain relevant and how long it should be stored. Some data have a short shelf life and quickly lose value, requiring timely processing.

9. Validity: Refers to how accurately and appropriately data reflect what it is intended to measure or represent for a specific purpose. While it may seem like veracity, they are distinct concepts. A dataset can have high veracity, meaning it is trustworthy, yet still lack validity if it does not align with its intended application. Simply put, a dataset cannot be assumed to be suitable or reliable for decision-making without proper validation.

 

Wesson et al. (2022) propose an additional V relating to research ethics:

10. Virtuosity: Integrates frameworks of equity and justice. This includes analytical approaches to advancing equity, including social computational big data, fairness in machine learning algorithms, and data augmentation techniques. Wesson et al. (2022) emphasize the concept of data absenteeism, referring to who is left out of data collection and the role of positionality in shaping research outcomes. They further state that a fundamental aspect of any scientific endeavor is understanding both the methods used to collect or generate data and the disparities between the study population and the broader target population.

Big Data and Job Opportunities

Big Data presents both unprecedented opportunities and significant challenges. The demand for individuals who can critically and ethically navigate an information landscape characterized by its size and complexity is growing rapidly. The acceleration of digitalization has amplified the demand for digital competencies across various employment sectors. This trend is particularly evident in scientific fields, where employers increasingly seek candidates proficient in digital skills. A comprehensive analysis of 126,360 scientific job advertisements from Science Careers, spanning 2019 to 2023, highlights this shift (Zhang et al., 2024). The study reveals a consistent upward trajectory in the requirement for digital proficiencies, with higher-paying positions more frequently requiring such skills. Expertise in data analysis, statistics, and statistical software (e.g., Python, and R) has seen a growing demand, while traditional skills like data collection have become less critical.

This trend aligns with broader labor market projections. For instance, the U.S. Bureau of Labor Statistics (2025) anticipates a 36% growth in data scientist roles from 2023 to 2033, driven by the increasing reliance on data-driven decision-making across industries. Similarly, the World Economic Forum (2025) forecasts a 30-35% rise in demand for roles such as data analysts and scientists, propelled by advancements in frontier technologies. These projections underscore the crucial importance of integrating digital skills into educational curricula to equip the future workforce for the evolving demands of the scientific and technological sectors. 

Data analytics is integral to various aspects of business operations, including informed decision-making, operational efficiency, customer understanding, competitive advantage, risk management, personalization, and innovation. By aligning curricula with these industry demands, educational institutions can prepare graduates to make effective contributions to data-driven strategies and innovations in their respective fields.

 

Big Data and Job Skills

Big data amplifies the importance of statistical reasoning and computational thinking, which are essential components of advanced data literacy. Machine learning and AI techniques used to analyze big data require users to understand how models are trained, what features are prioritized, and how predictions are generated. Without this understanding, users may misinterpret automated outputs as objective truth when, in fact, they may reflect biased or flawed assumptions embedded in the data (O’Neil, 2016).

Data visualization and storytelling are essential skills when working with large datasets. Given the overwhelming volume of information, the ability to distill meaningful patterns, trends, and insights through clear visuals becomes a necessary skill for decision-making in business, policy, and research. Tools such as Tableau, Power BI, and Python libraries (e.g., Seaborn, Matplotlib) make this possible, but their effective use requires both technical proficiency and ethical awareness.

Organizations generate increasing volumes of data daily, making the roles of data analysis and analytics pivotal in effectively managing and leveraging this information. Consequently, educational programs in data analysis and analytics must evolve to align with the industry's dynamic needs and meet professional expectations (Booker et al., 2024). In conclusion, the rise of big data transforms data literacy from a helpful skill into a critical form of digital citizenship. It enables individuals not only to work with complex information but also to scrutinize how data are collected, analyzed, and used. In a world where algorithms and data models increasingly drive decisions, widespread data literacy is essential to ensure that big data serves the public good rather than undermining it.

 

References

Booker, Q. E., Rebman, C. M., Wimmer, H., Levkoff, S. B., Powell, P. & Breese, J. L. (2024). Data analytics position description analysis: Skills review and implications for data analytics curricula. Information Systems Education Journal, 22(3), 76–87.

Jones, B. (2025). Data literacy fundamentals: Understanding the power and value of data (2^nd ed.). Data Literacy Press.

Saeed, N. & Husamaldin, L. (2021). Big data characteristics (V’s) in industry. Iraqi Journal of Industrial Research, 8, 1-9. 10.53523/ijoirVol8I1ID52.

U.S. Bureau of Labor Statistics (2025, April 18). Fastest growing occupations. https://www.bls.gov/ooh/fastest-growing.htm

World Economic Forum (2025, January 7). Future of Jobs 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/

Zhang, G., Wang, L. Shang, F. & Wang, X. (2024): What are the digital skills sought by scientific employers in potential candidates? Journal of Higher Education Policy and Management, 47(1), 20-37. https://doi.org/10.1080/1360080X.2024.2374392

 

 

Information Literacy as a Career Survival Skill

 

By Lilian H. Hill

Released by the World Economic Forum on January 7, 2025, the Future of Jobs Report 2025 draws on insights from over 1,000 leading global employers, representing more than 14 million workers across 22 industry sectors and 55 economies worldwide, to explore how major global trends are shaping the future of jobs and skills. It also outlines the workforce transformation strategies these employers intend to pursue between 2025 and 2030 in response to these shifts. The World Economic Forum brings together leaders from politics, business, academia, civil society, and other sectors to shape global, regional, and industry agendas. Founded in 1971 as a non-profit organization, it operates independently and impartially, free from special interests, and is committed to the highest standards of governance, ethical conduct, and intellectual integrity (World Economic Forum, n.d.). This is the fifth edition of the Future of Jobs report published since 2016. 

The Future of Jobs Report 2025 explains how information literacy is understood in today’s rapidly evolving and uncertain labor market. Traditionally, information literacy referred to the ability to locate, evaluate, and effectively use information. The report emphasizes the need for a more comprehensive and adaptable understanding that incorporates digital discernment, data literacy, and critical thinking in an increasingly automated and data-driven world. AI literacy, data analytics, cybersecurity, and creative problem-solving are critical skillsets in demand, and employees will need to make adaptability a core strength.

As roles in artificial intelligence (AI), data analysis, sustainability, and digital transformation become more prominent, individuals must develop the ability to critically assess the accuracy, credibility, and implications of the information they consume, particularly in a media environment where misinformation, technological hype, and biased algorithms are widespread. The image below displays the top 10 skills identified by the Future of Jobs 2025 report as desirable from now until 2030.

 

The report emphasizes that skills-based hiring is on the rise, with employers placing more value on demonstrable competencies than on formal credentials. This trend reinforces the importance of self-directed learning, where workers are expected to acquire and apply new knowledge to remain competitive continually. With nearly 40% of current job skills expected to shift by 2030, the ability to access and interpret relevant information becomes a crucial career survival skill, enabling individuals to identify trusted educational platforms, evaluate online learning resources, and stay current with evolving industry standards.

The growing complexity and opacity of emerging technologies heighten the need for technological and ethical literacy. As AI, big data, and algorithmic decision-making become embedded in areas such as hiring, education, policing, and healthcare, the public must be equipped to ask critical questions: Who designed these systems? What data were they trained on? Who is accountable if things go wrong? Information literacy thus plays a central role in helping individuals and communities not only use digital tools but also critically examine their fairness, transparency, and societal impact.

The report reinforces the role of information literacy in civic engagement. As AI and other technologies increasingly influence public decision-making, including resource allocation, predictive policing, and climate-related infrastructure planning, citizens must be able to participate meaningfully in public consultations, policy debates, and democratic processes. This requires the ability to interpret technical and policy-related information, challenge unjust practices, and propose alternatives rooted in equity and inclusion. In this way, information literacy supports informed citizenship in a digital democracy.

Finally, the report emphasizes the urgent need to bridge the digital divide, noting that without equitable access to learning tools and information resources, existing inequalities are likely to persist and widen. Individuals from underserved communities face disproportionate barriers to acquiring in-demand skills, which can perpetuate cycles of economic exclusion. Embedding information literacy into education systems, workforce development programs, and community initiatives is essential to ensure that everyone, not just the digitally privileged, can participate in and benefit from the changing world of work.

In summary, the Future of Jobs Report 2025 frames information literacy as a multidimensional and indispensable skill in the era of rapid technological change. It is no longer sufficient to know how to find information; individuals must be able to evaluate its quality, apply it to real-world challenges, and use it to advocate for fair and ethical practices. Information literacy is thus positioned as a foundational competency for lifelong learning, career resilience, civic empowerment, and social equity in the digital age.

References

World Economic Forum (n.d.) Our Mission. https://www.weforum.org/about/world-economic-forum/

World Economic Forum (2025, January 7). Future of Jobs 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/