The Impact of Social Media on the Publishing Industry: A Case Study of Author Colleen Hoover
Bibliographic record
Abstract
Thanks to the BookTok community, adult romance author Colleen Hoover has taken the world by storm. Anyone that has had access to TikTok within the past year has probably had a Colleen Hoover book appear in video on their “For You Page,” the page on TikTok where users can scroll through content that follows an algorithm which learns about the user’s specific interests. However, Hoover has been writing and publishing novels since 2012. She had already published more than a dozen novels by 2020, yet her popularity has only grown since she made her appearance on social media. Her book entitled It Ends with Us has exponentially went up in sales in 2021, five years after its initial release. By the end of the book’s first month on the market, it had sold 21,000 copies. The author noticed a bump in sales in the last quarter of 2020, and by 2021, that bump in sales became an exponential growth in sales: 308,000 copies sold in only one year. Many critics believe that this growth in sales can contribute its success to TikTok, and specifically BookTok. Now, in 2022, over four million copies of It Ends with Us have been sold, and 20 million of Hoover’s books have been sold across the globe. This research will take a look at how social media has a direct effect on the publishing industry and the consumption of literature, specifically looking into the phenomenon of Colleen Hoover. She has learned how to use her presence on social media platforms for marketing, while also simultaneously having her books marketed by her within the different social media communities. Social media is not going away anytime soon, so also having authors learn to use social media to their advantage can also help them to accelerate their rate of sales of their novels as well. Learning to analyze behaviors of social media platforms, learning current trends to make the author’s books more relevant to their markets, and interacting with audiences and fans can be quite beneficial to all authors. Colleen Hoover has made her social media presence quite prevalent, so taking a look into the tactics she uses, the posts she makes, and the events she holds to interact with her fans can be fascinating to study and learn how to use for other authors as well.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".