Abstract

Title: Binge-watching, Audience Participation, and Effects on the Body

Abstract: The dataset, as it stands, will be a collection of tweets referencing the viewership practice of binge-watching television serials. The project will use the Twitter keyword search feature and the following keyword data: binge, binge watch, and bingeing. These key terms will be accompanied by a hashtag for a given popular series on cable or streaming services. For example, a search can look something like “binge #euphoria”. The “binge” term designates for the search function that I am interested in tweets or profile names that include the word “binge”. The “#euphoria” or whatever television series I put beside the hashtag indicates that I am interested in tweets that discuss binge-watching in relationship to the given television series. Ideally, the collection and scrapping process for the dataset would be completed using Python or an alternative Twitter scrapping program. Yet, the scope of this project is limited. I will, then, manually collect all instances of “binge, binge watch, or bingeing” that appear within a tweet and the given television series associated with the particular binge-watching instance.

I have selected four television/streaming series to analyze binge-watching patterns on social media. Those programs are Peacemaker, Bridgerton, Yellowjackets, and Euphoria. I have selected these four programs based on articles from the review aggregate website Rotten Tomatoes. The specific article that has provided the basis for this project may be found here. From this article I have selected four television shows from the “Popular Shows Available on Streaming” header. I intend for this project to serve as a model for future investigations into the relationship between binge-watching and social media platforms. As such, I have devised a basic formula, as insinuated above, that will provide the rubric for reproducibility. That is, “binge(/binge-watching/bingeing) #x”. The #x is indicative of the given show that a researcher is interested in exploring using the Twitter search function.

I am only interested in tweets that meet the basic criteria of: having binge in the content of the tweet and which use the hashtag for the selected program. Researchers who attempt to use the exact search criteria I have defined above will, most likely, be given an altogether different set of tweets from my archive. The (theoretically) temporally infinite possibility of twitter conversations will push older or less popular tweets further down a timeline, and more recent and/or more popular tweets will appear first. This project, then, has a high degree of variability based on the researchers geographic location and the time at which future projects are conducted. The project, in part, is viewed as the platform various topic modelling inquiries into the conversation in, and around, binge-watching as a consumptive habit.

Metadata

Assigned Number: Each tweet, as they are collected, will be assigned a number. For example, the fifth tweet that I select will be assigned the numeric value 5. This number is to simplify the analysis process, as researchers can compile the number associated with their tweets of interest to easily find them within the dataset. It is also an arbitrary number to keep myself accountable and to keep the number of tweets collected easily organized.

Tweet: This metadata field will contain the tweet, in full.

Date/Time: Date and Time information associated with tweet.

Screen Name: Twitter user’s screen name.

ReTweet Count: Number of times the tweet has been retweeted.

“Like” Count: Number of time the tweet has been “liked” by other users.

Comment Count: Number of times the tweet has been commented on.

Source Link: All source tweets will be linked in this tab for researchers to read comments posted under the tweet or to verify sources.

Audience(s)

• Researchers interested in exploring binge-watching habits and patterns. Further, researchers who are interested in analyzing the conversation around binge-watching; especially, the relation to sleep, productivity discourse, and conversations of the body.
• Television scholars
• Digital archivists and historians
• The dataset can function as a pedagogical tool for students interested in navigating a comparatively small dataset dealing with popular television and social media discourse.

Questions

• What key terms often appear around binge-watching discourse?
  • For example, “sleep,” “work,” “day off,” “again”.
• Along similar lines of the previous question, what is the relationship between binge-watching, forgoing sleep, and productivity?
• Do social media users view binge-watching as a form of escapism?
• Binge-watching and repetition? Not only do binge-watchers discuss “bingeing” a television series, but some users also discuss re-bingeing programs after completion, or returning to programs on a yearly basis for a binge. What is the relationship between binge-watching and repetitive action?
• Where does the enjoyment in bingeing lie?
• How do twitter uses characterize bodily affect? Are there specific elements of the body that are singled out by users or is the language more general?
• How do twitter users define a binge?

Related Projects

Twitter Dataset - #AvengersEndgame Apple Twitter Sentiment Game of Thrones S8 (Twitter)

The Avengers Endgame twitter dataset focuses on a collection of tweets that were posted immediately after the release of Marvel’s Avengers Endgame. Tweets that contained the #AvengersEndgame were scrapped and added into the dataset. The dataset is time-stamped and contains the number of retweets and favorites.

The Apple Twitter Sentiment database follows much of the same basic structure as the Avenger Endgame tweet dataset but adds a sentiment metric. The researchers have not indicated the valuation of the 1-3 labels, nor have they provided a codebook to determine how sentiment (positive, neutral, or negative) is determined. Yet, the attempt to assign a sentiment value is interesting. Although, I believe that for my dataset it would be redundant to assign sentiments to binge-watching phenomenon, as most twitter users discussing bingeing-as-habit do so because they actively binge-watch programs.

Finally, the Game of Thrones S8 twitter dataset is a compilation of tweets produced immediately after the airing of individual episodes during the release of Game of Thrones Season 8. Again, this is a rather simple dataset. It is primarily an aggregate of Tweets regarding a particular topic.

Although I have offered three datasets above, they are ultimately limited by the fact that they are not traditionally “scholarly” datasets. Due to a lack of centralized space for Digital Humanities research projects, I encountered a difficulty in locating datasets that dealt, specifically, with Twitter analytics and television viewing patterns. It is possible that this dearth of information is indicative of a lack of research in this field, but it is this researchers humble estimation that he simply does not know how to find DH research projects.

I will update this page after speaking with Professor Thomas.