“I Love Talking To You Guys About These Books”
Bibliographic record
Abstract
Across social media platforms, communities have formed around shared interests such as reading. A prominent online community is BookTube, a reader community that has formed on YouTube, promoting reader discourse with audience members from around the world. BookTube represents a broader community among readers that is facilitated by YouTube as an interactive media platform. Content creators on BookTube, also known as BookTubers, create both long- and short-form content related to books and reading, such as full book reviews, book recommendations, and bookstore vlogs (video blogs). Especially in long-form content like bookstore vlogs, BookTubers build and reinforce their individual communities, in many cases by establishing parasocial relationships with viewers. As is common practice in online communities on platforms like YouTube, BookTubers directly address audience members to give the impression of mutual participation in a book-shopping trip, reading challenge, or other reading-related practice. Considering the extent to which BookTubers can influence the reading choices or practice of fellow readers and prospective readers alike, especially since popular BookTubers’ videos regularly receive between hundreds of thousands or millions of views, there is room to further study the application and implications of this common community-building approach. This paper will present an analysis of bookstore vlogs from BookTube channels that reflect broader trends in community engagement and contemporary online reader communities. The sample of twenty-seven videos studied for this project were uploaded to YouTube over a six-month period, between September 2022 and February 2023. These videos were manually coded using qualitative content analysis, focusing on ways in which BookTubers directly address readers to promote engagement with both their content and their individual communities. This paper highlights specific BookTubers as case studies within the full sample of videos to represent themes that emerged from the analysis of the videos. Through viewer address, these BookTubers engage viewers in their discussions of books and considerations about reading-related practices, such as by promoting dialogue in comments and going book-shopping “together” with viewers in bookstore vlogs, thereby cultivating reciprocity between the content creators and their fellow readers. This paper contributes to the ongoing scholarly conversation on social media influence and emerging discussions of BookTube and its counterparts on other social media platforms (e.g., TikTok’s #BookTok and Instagram’s Bookstagram) by identifying themes related to the language used by BookTubers to address viewers and invite their ongoing participation and involvement, adding to the growing body of work on online community formation, parasocial relationships, and interactivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".