Tags, Algorithms, and Visibility

For this week, I want to continue exploring my overall topic, Social Media Tools and Higher Education, through the lens of tags, hashtags, algorithms, and visibility. This week’s readings helped me think more specifically about how information is organized and made visible in online spaces. In higher education, this matters because students do not simply use social media tools; they also depend on these tools to find resources, build professional identities, join communities, and access opportunities.

One important concept this week is tagging. Tags and hashtags are lightweight organizing systems that help people connect posts, resources, conversations, and communities. In higher education, tags can support learning in many ways. For example, students in a course could use hashtags such as #InternationalStudents, #HigherEducation, #CareerDevelopment, #DigitalLiteracy, or #StudentBelonging to organize blog posts, discussion board responses, or shared resources. Tags can help students see patterns in their own learning and find peers with similar interests. Raman et al. (2020) discuss hashtags as an easy entry point for enhancing online discussions, suggesting that tagging can lower the barrier to student participation and connect ideas.

At the same time, tags are not automatically effective. Dennen, Bagdy, and Cates (2018) show that tagging practices in online learning environments require attention to both approach and accuracy. If students use tags that are too general, inconsistent, misspelled, or overly personal, the tag system may become difficult to use. For example, one student might use #intlstudent, another might use #internationalstudent, and another might use #studyabroad, even though they are discussing similar experiences. Without some guidance, students may create what feels like a messy collection of labels rather than a useful learning system. This made me think that instructors should not simply tell students to “use tags” in the practice of higher education. They should also model effective tagging and explain how tags can support: 

• Learning
• Searching
• Reflection
• Assessment.

Algorithms add another layer to this issue. Bucher (2017) argues that people develop an “algorithmic imaginary,” meaning that users form ideas about how algorithms work and adjust their behavior accordingly. This is very relevant to social media tools in higher education. On LinkedIn, students and professionals may carefully consider when to post, which hashtags to use, what wording sounds professional, and which types of content will gain visibility. On TikTok, Instagram, or YouTube, educational creators may adjust their content to satisfy platform algorithms. As a result, users may not only communicate with people but also perform for algorithms.

This creates both opportunities and problems for higher education. On the positive side, algorithms can help students discover useful resources, professional communities, and learning opportunities they may not have found on their own. For example, a student interested in international higher education might begin following one professional organization on LinkedIn and then receive recommendations for related scholars, conferences, webinars, and career pathways. However, algorithms can also narrow what students see. They may amplify popular content over accurate content, emotional content over nuanced content, or already-visible voices over marginalized voices.

This week’s readings made me think more critically about the relationship between tags and algorithms. Tags are more visible and user-controlled. Students can intentionally choose tags to organize and connect their learning. Algorithms are more hidden and platform-controlled. They may help students discover information, but students usually do not fully know why certain content appears in their feeds. For higher education, I think the best approach is to teach students to use tags intentionally while also developing critical awareness of algorithms. Students should understand that what they see online is not simply “what exists,” but what platforms choose to make visible.

References

Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30-44. https://doi.org/10.1080/1369118x.2016.1154086 

Dennen, V. P., Bagdy, L. M., & Cates, M. L. (2018). Effective tagging practices for online learning environments: An exploratory study of approach and accuracy. Online Learning, 22(3), 103-120.

Raman, P., Avery, T., Brett, C., & Hewitt, J. (2020). Exploring the use of# Hashtags as an easy entry solution to enhance online discussionsLinks to an external site. International Journal of E-Learning & Distance Education/Revue internationale du e-learning et la formation à distance, 35(1).