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This post shares the chronological list of all her books, followed by the full list sorted by standalone novels and book series by Colleen Hoover, summaries of all of the books to help you decide which one to read first or next, and answers frequently asked questions about Colleen Hoover books (like which Colleen Hoover book to read first).
Started reading Colleen Hoover books this past November 2022. I read them all and passed them on to a friend here in MI. She gave them back and I sent them to a friend in VT. We love them. My friend in VT is passing them around to her friends. I also found out my granddaughter in TX reads them too. Keep writing and thank you.
I'm a reader of 100+ books per year, had a minor in English literature, and I've been on The Today Show's Read with Jenna Book Club, Oprah's Book Club, Reese's Book Club, and Buzzfeed, and my essay about The Rory Gilmore Book Club was published in the book But I'm a Gilmore!
Love to binge CoHo books? This guide to Hopeless: the Colleen Hoover series of coming-of-age romance fiction books tells you everything you need to know before you dive in and/or read your next novel.
Get answers to frequently asked questions about the Hopeless Colleen Hoover series, including the series order, then get a summary of the storyline and characters in each of the four books in the series. Lastly, get a printable PDF of all Colleen Hoover books so you can track your reading.
We further examine this pattern across 7,226 books, 6,087 movie synopsis, and 1,109 movie scripts. We find that despite the association between females and emotional words (see S1 Fig for this association), when male and female characters are present in the same context (which is a set of sequential sentences, see the definition in Method), the happiness score of female characters is significantly higher than that in the contexts without the presence of male characters (S2 Fig). Meanwhile, within the context of character occurrence, the increase in the happiness score is higher for female characters than for male characters (Fig 4). These findings reveal a constructed, asymmetrical emotional dependency of females on males, robust against the publication time (S7 Fig) and genres (S8 Fig) of stories and the direction of emotion (S3 Fig).
Our study, while primarily focuses on designing and testing existing assumptions on gender stereotypes, also aims to contribute to the theories on gender stereotypes in several dimensions. 1) Interacting vs. separated gender roles. The analysis of the relationships between genders is critical to reveal stereotypical expectations, as gender roles emerge from the interactions with the other gender. 2) Visible vs. hidden stereotypes. Some gender inequalities and stereotypes are more noticeable than others, such as inequalities in voting rights, working salaries, and educational opportunities. These apparent inequalities may distract social attention and make hidden stereotypes in paradigms, language, and communication even less noticeable . 3) Social reproduction of stereotypes. There are both causes and consequences of stereotypical narratives. Stereotypes reduce the complexity of stories and make them more relatable and memorable; however, the flat characters may project into reality. Gender stereotypes, constructed and weaved into the moral tales from movies and books, may maintain gender inequality though these morality norms and reproduce gender inequality as a social fact . For example, when children are exposed to stereotyped narratives, they may fill themselves into stereotypical roles . A study on the impact of Disney movies shows that children who associate beauty to popularity for movie characters tend to apply the same principle in real lives .
The limitations of the current study are noted and should be aware of in future research designs. The natural language processing models used to identify the leading characters their gender (www.nltk.org/book/ch02.html) may miss the uncommon names of characters or misidentify characters genders. Also, there is an unexplained variance between machine-labeled versus human-labeled happiness scores for words (Pearson correlation coefficient equals 0.53 with a P-value < 0.001). In general, sentiment scores for words have limitations in analyzing narratives as a fixed score, since they can not capture the variance of sentiments of the same word across contexts.
We collect three datasets for this present research, including movie synopses, movie scripts, and books (Fig 8). We collect the movie synopsis data from the IMDB website (www.imdb.com). We select the movies with user ratings, plot synopsis, release year, and genre. And we get 16,255 movies for further data filtering. We choose 6,087 movie synopses with more than five sentences and both female and male characters in the analysis. Second, we also collect the movie script data from the IMSDB website (www.imsdb.com), which is the largest database of online movie scripts. There are 1,109 movie scripts after filtering out those in which only one gender of characters are identified. The metadata of the movie scripts, such as the release year and genre, is also collected. Third, in addition to the two movie datasets, we also collect the data of more than 40 thousand English books from the Gutenberg Project (www.gutenberg.org), including the text of story, publication time, and genre. In the data filtering of books, only 7,226 books belonging to the genre "language and literature" and containing both female and male characters are selected. All the code and data are available from
Fig 9 compares the length of stories in sentence across three datasets. Since the users of IMDB website create the movie synopses, the variance in story length is much more significant than that in movie scripts and books, as the scripts and books are typically from a smaller group of authors. Because the length of dialogues is usually short, the average number of words per sentence for the movie script data is much smaller than the other two datasets. Given the different number of sentences in three datasets, we segment movie synopses by sentences, while segment movie scripts and books by paragraphs. Since the sentence is the primary unit of narrative, this method of story segmentation helps us understand the variance of sentiment in stories.
The distribution of the number of sentences of 6,087 movie synopses (a), 1,109 movie scripts (b), and 7,226 books (c). d, The number of words per sentence across three datasets.