Twitter hashtag analysis of movie premieres in February 2022 in the USA

Author: Víctor Yeste. Universitat Politècnica de Valencia.This work is an exploratory, quantitative, and not experimental study with an inductive inference type and a longitudinal follow-up. It analyzes movie data and tweets published by users using the official Twitter hashtags of movie premieres the week before, the same week, and the week after each release date.The scope of the study is the collection of movies released in February 2022 in the USA, and the object of the study includes them and the tweets that refer to the film in the 3 closest weeks to their premiere dates. The tweets recollected were classified by the week they were published, so they are classified by a time dimension called timepoint. The week before the release date has been designated as timepoint 1, the week of the release date is timepoint 2, and the week immediately afterward is timepoint 3. Another dimension that has been considered is if the movie has domestic production or not, which means that if one of the countries of origin is the United States, the movie is designated as domestic.The chosen variables are organized in two data tables, one for the movies and one for the collected tweets.Variables related to the movies:id: Internal id of the moviename: Title of the moviehashtag: Official hashtag of the moviecountries: List of countries of the movie, separated by a semicolonmpaa: Film ratings system by the Motion Picture Association of America. It is a completely voluntary rating system and ratings have no legal standing. The currently rating systems include G (general audiences), PG (parental guidance suggested), PG-13 (parents strongly cautioned), R (restricted, under 17 requires accompanying parent or adult guardian) and NC-17 (no one 17 and under admitted)(<i>Film Ratings - Motion Picture Association</i>, n.d.)genres: List of genres of the movie, e.g., Action or Thriller, separated by a semicolonrelease_date: Release date of the movie in a format YYYY-MM-DDopening_grosses: Amount of USA dollars that the movie obtained on the opening date (the first week after the release date)opening_theaters: Amount of USA theaters that released the movie on the opening date (the first week after the release date)rating_avg: Average rating of the movieVariables related to the tweets:id: Internal id of the tweetstatus_id: Twitter id of the tweetmovie_id: Internal id of the movietimepoint: Week number related to the movie premiere that the tweet was published on. “1” is the week before the movie release, “2” is the week after the movie release” and “3” is the second week after the movie release.author_id: Twitter id of the author of the tweetcreated_at: Date and time of the tweet, with format “YYYY-MM-DD HH:MM:SS”quote_count: Number of the tweet’s quotesreply_count: Number of the tweet’s repliesretweet_count: Number of the tweet’s retweetslike_count: Number of the tweet’s likessentiment: Sentiment analysis of the tweet’s content with a range from -1 (negative) to 1 (positive)This dataset has contributed to the elaboration of the book chapters:Yeste, Víctor; Calduch-Losa, Ángeles (2022). Genre classification of movie releases in the USA: Exploring data with Twitter hashtags. In <i>Narrativas emergentes para la comunicación digital (</i>pp. 1012-1044). Dykinson, S. L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). Exploratory Twitter hashtag analysis of movie premieres in the USA. In <i>Desafíos audiovisuales de la tecnología y los contenidos en la cultura digital</i> (pp. 169-187). McGraw-Hill Interamericana de España S.L.Yeste, Víctor; Calduch-Losa, Ángeles (2022). ANOVA to study movie premieres in the USA and online conversation on Twitter. The case of rating average using data from official Twitter hashtags. In <i>El mapa y la brújula. Navegando por las metodologías de investigación en comunicación</i> (pp. 151-168). Editorial Fragua.<br>

作者：维托·耶斯特（Víctor Yeste），瓦伦西亚理工大学（Universitat Politècnica de Valencia）。
本研究为探索性、定量非实验性研究，采用归纳推理范式，且具备纵向追踪设计。本研究分析两类数据：电影数据，以及用户在每部影片上映前一周、当周及后一周，使用该片官方话题标签（hashtag）发布的推文。

本研究的研究范围为2022年2月在美国上映的全部影片，研究对象包含上述影片，以及首映前后三周内提及该影片的推文。收集到的推文按发布周进行分类，即按名为"时间点（timepoint）"的时间维度划分：影片上映前一周为时间点1，上映当周为时间点2，上映后紧邻的一周为时间点3。本研究同时考量另一维度：影片是否为本土制作——若影片的任意原产国为美国，则将其归类为本土制作。

本研究选定的变量被整理为两张数据表：一张对应电影信息，另一张对应收集到的推文信息。

#### 电影相关变量：
id：影片内部编号
name：影片标题
hashtag：影片官方话题标签
countries：影片原产国列表，以分号分隔
mpaa：美国电影协会（Motion Picture Association of America, MPAA）分级体系。该分级体系为完全自愿性机制，分级结果不具备法律效力，当前可用分级包括：G（普通观众级）、PG（建议家长指导级）、PG-13（家长需强烈监督级）、R（限制级，17岁以下观众需由家长或成年监护人陪同）以及NC-17（17岁及以下观众禁止入场级）（<i>美国电影协会影片分级标准</i>，无出版日期）
genres：影片类型列表，例如动作片、惊悚片，以分号分隔
release_date：影片上映日期，格式为YYYY-MM-DD
opening_grosses：影片首映周（上映后第一周）获得的美国票房收入（单位：美元）
opening_theaters：影片首映周的上映影院数量
rating_avg：影片平均评分

#### 推文相关变量：
id：推文内部编号
status_id：推文的Twitter官方编号
movie_id：对应影片的内部编号
timepoint：推文发布时对应的影片首映周编号："1"代表影片上映前一周，"2"代表上映当周，"3"代表上映后第一周
author_id：推文作者的Twitter官方编号
created_at：推文发布的日期与时间，格式为"YYYY-MM-DD HH:MM:SS"
quote_count：该推文的引用数
reply_count：该推文的回复数
retweet_count：该推文的转发数
like_count：该推文的点赞数
sentiment：推文内容的情感分析得分，取值范围为-1（负面）至1（正面）

本数据集曾用于支撑以下图书章节的撰写：
1. Yeste, Víctor; Calduch-Losa, Ángeles (2022). 美国上映影片的类型分类：基于Twitter话题标签的数据探索. 载于<i>Narrativas emergentes para la comunicación digital</i>（pp. 1012-1044）. Dykinson, S. L.
2. Yeste, Víctor; Calduch-Losa, Ángeles (2022). 美国影片首映的Twitter话题标签探索性分析. 载于<i>Desafíos audiovisuales de la tecnología y los contenidos en la cultura digital</i>（pp. 169-187）. McGraw-Hill Interamericana de España S.L.
3. Yeste, Víctor; Calduch-Losa, Ángeles (2022). 利用美国影片首映官方Twitter话题标签数据，通过方差分析研究影片首映与Twitter在线对话：以平均评分为例. 载于<i>El mapa y la brújula. Navegando por las metodologías de investigación en comunicación</i>（pp. 151-168）. Editorial Fragua.