The Impact of Social Media on the Publishing Industry: A Case Study of Author Colleen Hoover
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,018 | 0,007 |
| Communication savante | 0,015 | 0,012 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».