Twitter content classification

Dann, Stephen

doi:10.5210/fm.v15i12.2745

Cited by 103 publications

(109 citation statements)

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“…Yet social penetration theory (Altman & Taylor, 1973) and theories of social exchange (Homans, 1958;Thibaut & Kelly, 1959) suggest that superficial disclosures may have important consequences in personal relationships. Moreover, scholars have noted the potential of new communication technologies for sharing trivial content (Miller, 2008;Tong & Walther, 2011), and recent content analyses have demonstrated that superficial information is routinely disclosed using several different technologies (Dann, 2010;Lee, 2011;Oulasvirta, Lehtonen, Kurvinen, & Raento, 2010;Rettie, 2009). This study attempts to better understand the implications of superficial disclosure in personal relationships by examining the outcomes associated with receiving superficial self-disclosures from friends via five communication technologies.…”

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confidence: 99%

Self-disclosure and new communication technologies

Rains

Brunner

Oman

2014

Journal of Social and Personal Relationships

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The reported study examined the implications of receiving superficial self-disclosures from a friend. A total of 199 adults reported on communication episodes initiated by a friend during the previous 7 days via five communication technologies and completed measures of liking, relationship satisfaction, and willingness to provide social support to their friend. The results revealed significant interactions between the total volume of self-disclosures received and proportion of superficial disclosures for liking and relationship satisfaction. Among respondents who received a relatively greater volume of self-disclosures, the proportion of superficial disclosures received was inversely associated with relationship satisfaction and liking. Perceived costs mediated the preceding relationships.

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confidence: 99%

Self-disclosure and new communication technologies

Rains

Brunner

Oman

2014

Journal of Social and Personal Relationships

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“…Enhancements to the algorithms and reclassification of the entire database were conducted as needed. The development of the fitness tweet classification model was based on available macro topic classification models where tweets were categories into broad categories of content, based on prior literature [18], and sourced from other works [8,11,13,19]. These prior literatures indicated four major categories for sharing on Twitter-Conversational, Pass-Along, News, and Status-categories that are consistent with Java's [11] research on the primary purposes of tweets.…”

Section: Original Researchmentioning

confidence: 86%

Twitter classification model: the ABC of two million fitness tweets

Vickey

Ginis

Dąbrowski

2013

Translational Behavioral Medicine

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The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, locationbased workouts, and overall workout sentiment. KEYWORDSPhysical activity, Twitter, Mobile fitness apps, Online social network INTRODUCTION Technology, health, and physical activity As much as technology has enriched society and expanded global communication, it can be argued that it has also negatively affected overall global health by lowering opportunities for physical activity [1] and by contributing to an overall secular decline in physical activity participation rates [2]. At the same time, research also indicates that there is a potential for technologies to be used as a means for improving health and increasing physical activity [3].According to a report issued by mobihealthNews, more than 13,000 health and fitness apps were available via iTunes by August 2012 [4]. The use of smartphones in supporting health behavior change via mobile fitness apps is encouraging. Aside from expanded opportunities for users to access health information, mobile devices are becoming more persuasive behavior change tools, allowing for the facilitation of ongoing collection of personal data and the opportune timing of feedback and education to elicit a change in behavior [5]. The most recent health applications have been smartphone applications for

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“…The size of Twitter's user community (at time of writing it has 313 million active users (Twitter 2016), more than double that of Tumblr (Flynn 2016)) has meant much published microblogging research is focussed on it: Williams et al (2013) identified over 1000 papers published by the close of 2011. Classifications of research approaches to Twitter have emerged which can help the community situate future work (Cormode et al 2010, Dann 2010, Cheong and Ray 2011, Barnes and Bohringer 2011, Williams et al 2013 Despite the significant size of its online communities (Tumblr 2016b, see also Anderson (2015, p. 161) is only able to identify two "scholarly resources" , Chang et al 2014, when as our findings will show, there were actually many more in circulation at the time of writing. Hillman et al (2014b) claim "We also found that nobody has studied Tumblr fandoms before", when other research was published on Tumblr fandoms devoted to film ) and television (Booth 2013) iii .…”

Section: Literature Reviewmentioning

confidence: 71%

What people study when they study Tumblr

Attu

Terras

2017

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FindingsThe majority of published work relating to Tumblr concentrates on conceptual issues, followed by aspects of the messages sent. This has evolved over time. Perceived benefits are the platform's long-form text posts, ability to track tags, and the multimodal nature of the platform. Severe research limitations are caused by the lack of demographic, geo-spatial, and temporal metadata attached to individual posts, the limited API, restricted access to data, and the large amounts of ephemeral posts on the site. Research limitations/implicationsThis study focuses on Tumblr: the applicability of the approach to other media is not considered. We focus on published research and conference papers: there will be book content which was not found using our method. Tumblr as a platform has falling user numbers which may be of concern to researchers. Practical implicationsWe identify practical barriers to research on the Tumblr platform including lack of metadata and access to big data, explaining why Tumblr is not as popular as Twitter in academic studies. Social implications Page 1 of 55Journal of Documentation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Originality/valueThere has not yet been an overarching study to look at the methods and purpose of those who study Tumblr. We identify Tumblr related research papers from the first appearing in July 2011 until July 2015. Our classification derived here provides a framework that can be used to analyse social media research, and in which to positionTumblr related work, with recommendations on benefits and limitations of the platform for researchers.

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Twitter content classification

Cited by 103 publications

References 0 publications

Self-disclosure and new communication technologies

Self-disclosure and new communication technologies

Twitter classification model: the ABC of two million fitness tweets

What people study when they study Tumblr

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