Author: Sourojit Ghosh
As a creative writer myself, I’ve always been anxious about getting reviews on the content I put out there. As I’m sure others who publish any form of writing can attest to, reviews form an integral part of our development as writers. However, I also find myself paying attention to not just what a review says, but also how it is said. Specifically, the emotions expressed in a review often shape my interpretation of it.
With that in mind, we at the University of Washington Human-Centered Data Science Lab (UW-HDSL) are interested in researching the emotions present in the multitude of reviews by the fanfiction community. By investigating a correlation between the lengths of reviews and the emotions expressed in them, we aim to understand the growth of relationships between members of the community as they share likes and dislikes.
Introduction
Our previous research with the fanfiction community has found widespread encouragement for budding relationships in its distributed-mentoring setting. The members of the community, mostly young adults from all over the world, are incredibly expressive in their words and often eager to support each other in the writing process. Most of the reviews we have seen in the community are rife with emotion, with the words jumping off the page with their expressiveness. This collectively supportive environment not only seeks to bring out the best in each individual but also to form meaningful relationships that extend beyond that of anonymous writers and readers of fanfiction.
Methods and Findings
For this exploration, we examined 1000 reviews of various fanfiction stories published on the site. We decided to classify them as exhibiting one of 11 emotions: Like, Joy/Happiness, Anticipation/Hope, Dislike, Discomfort/Disgust, Anger/Frustration, Sadness, Surprise, Confused, Unknown, and No Emotion. Figure 1 shows an example of a review coded in this way using TextPrizm, a web tool developed by members of the UW-HDSL.
Figure 1: An example of a review being coded for emotions
By coding these reviews for emotions, we are trying to gain a better understanding of the trends in emotions expressed by reviewers across the community. By identifying such trends, we hope to learn how relationships are formed between users sharing common interests and having similar reactions to certain content.
Figures 2 and 3 display our preliminary results so far. Figure 2 represents the number of reviews being classified as having each emotion, while Figure 3 shows the average lengths of reviews in the dataset expressing each emotion.
Figure 2: A bar graph showing the no. of reviews each emotion was assigned to.
Figure 3: A bar graph showing the average no. of words in a review expressing each emotion.
The high number of reviews expressing Joy / Happiness and Like is an encouraging indication of the fact that most users took adequate time to express their positivity and support towards the writers. Another emerging trend can be seen in the reviews marked as No Emotion. This small number of reviews averaging at about 80 words per review was found to contain thoughtful discussions on global issues like religious tensions and sexual violence. While the previously discussed reviews highlight the positivity inherent in the community, these reviews remind us of the incredible maturity and depth of thought that the members also possess, a fact even more inspiring given that the community is mostly comprised of young adults.
Conclusion and Future Work
This initial examination of a small set of reviews offers some insight into the correlations between emotions and review length. An exploration of a larger set of reviews may offer some basis for providing statistically significant findings along the lines of the currently observed trends and can provide further insight into the ways in which reviews are integral in the process of users on relationship building on Fanfiction.net.
We would love to hear from you, members of the fanfiction community, about what you think of our work and how you view the emotions expressed in reviews of your writing. At the same time, we would also be interested in knowing if you express certain emotions in your reviews more extensively than others! If you have any questions or concerns about our data, feel free to respond to this post or send up an ask, and we would be happy to get back to you. And, as always, stay tuned for our future work with your wonderful fanfiction community!
Acknowledgments
We are incredibly grateful to Dr. Cecilia Aragon and undergraduate researcher Niamh Froelich at the UW Human-Centered Data Science Lab for the initial ideas behind the project, their insightful feedback, and constant support throughout the process. We are also grateful for the fantastic Fanfiction.net community, which continues to prosper each day and exist as a positively supportive environment for budding and seasoned writers alike.
