Purpose

The purpose of this article is to explore how participants with different motivations (educational or leisure), familiarity with the medium (newbies and active Twitter users), and participating instructions respond to a highly structured digital social reading (DSR) activity in terms of intensity of engagement and social interaction.

Design/methodology/approach

A case study involving students and teachers of 211 Italian high school classes and 242 other Twitter users, who generated a total of 18,962 tweets commenting on a literary text, was conducted. The authors performed both a quantitative analysis focusing on the number of tweets/retweets generated by participants and a network analysis exploiting the study of interactions between them. The authors also classified the tweets with respect to their originality, by using both automated text reuse detection approaches and manual categorization, to identify quotations, paraphrases and other forms of reader response.

Findings

The decoupling (both in space and time) of text read (in class) and comments (on Twitter) likely led users to mainly share text excerpts rather than original personal reactions to the story. There was almost no interaction outside the classroom, neither with other students nor with generic Twitter users, characterizing this project as a shared experience of “audiencing” a media event. The intensity of social interactions is more related to the breadth of the audience reached by the user-generated content and to a strong retweeting activity. In general, better familiarity with digital (social) media is related to an increase in the level of social interaction.

Originality/value

The authors analyzed one of the largest educational social reading projects ever realized, contributing to the still scarce empirical research about DSR. The authors employed state-of-the-art automated text reuse detection to classify reader response.

中文翻译：

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读者通过 Twitter 上的数字社交阅读参与：TwLetteratura 案例研究

目的

本文的目的是探讨具有不同动机（教育或休闲）、对媒体的熟悉程度（新手和活跃的 Twitter 用户）以及参与说明的参与者如何在强度方面对高度结构化的数字社交阅读 (DSR) 活动做出反应的参与和社会互动。

设计/方法/途径

进行了一项案例研究，涉及 211 个意大利高中班级的学生和教师以及 242 名其他 Twitter 用户，他们共生成了 18,962 条推文评论文学文本。作者进行了一项定量分析，重点关注参与者生成的推文/转推数量，以及一项利用对他们之间互动的研究的网络分析。作者还通过使用自动文本重用检测方法和手动分类，根据推文的原创性对推文进行分类，以识别引文、释义和其他形式的读者反应。

发现

文本阅读（在课堂上）和评论（在 Twitter 上）的分离（在空间和时间上）可能导致用户主要分享文本摘录，而不是对故事的原始个人反应。课堂之外几乎没有互动，既不与其他学生互动，也不与普通 Twitter 用户互动，将这个项目描述为“听众”媒体活动的共享体验。社交互动的强度更多地与用户生成的内容所覆盖的受众广度和强大的转发活动有关。一般来说，对数字（社交）媒体的更熟悉​​与社交互动水平的提高有关。

原创性/价值

作者分析了有史以来最大的教育性社会阅读项目之一，为仍然稀缺的 DSR 实证研究做出了贡献。作者采用最先进的自动文本重用检测来对读者反应进行分类。