How users employ various popular tags to annotate resources in social tagging: An empirical study

Pan, Xuwei; He, Shenglan; Zhu, Xiaomin; Fu, Qingmiao

doi:10.1002/asi.23478

Cited by 6 publications

(5 citation statements)

References 49 publications

(69 reference statements)

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“…Recruitment for depth interviews on social media Social tagging, a prominent feature of Web 2.0 technology, is an increasingly popular phenomenon for categorizing and describing content on social networks using usergenerated keywords in the form of hashtags (#) (Lin and Chen, 2012;Pan et al, 2016). Hashtags are a practical means to reach a large group of like-minded individuals and provide invaluable insights into the social interpretation, mental representation and knowledge structure of specific content (Fu et al, 2010).…”

Section: Ethical Considerationsmentioning

confidence: 99%

How to make a collaborative videography using Phygital affordances to study sensitive topics

Cavusoglu,

Belk

2024

QMR

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Purpose The physical filmmaking landscape has been transformed by the emergence of digital platforms that foster interaction and dialogue. The accessibility and affordability of mobile production tools have empowered anyone with a mobile phone to become a media content creator. Accordingly, this paper aims to present a multi-method approach for creating phygital projects that involve people as active participants rather than mere subjects who collaborate with the researchers to tell their stories. Design/methodology/approach Research participants can embrace diverse roles, serving as co-researchers, content creators, curators and collaborators. The authors use various engagement strategies with the research participants, who are often marginalized or underrepresented marginalized or underrepresented, to encourage their participation and give them agency and creative control. Thus, we also use a participatory action research approach to help advocate for the participants’ facial equality concerns. Findings Collaborative videography embraces the mosaic of voices expressing intricate social issues. In this project, research participants with “facial differences” explain their experiences in facing society. Originality/value By experimenting with participatory frameworks and combining physical interactions (such as in-person meetings) with digital platforms like Zoom and social media, the authors suggest a multi-method approach that honors the authentic stories of the research participants, effectively engages the audience and explains how phygital research methodologies can be used in interpretive consumer research, particularly in co-creating films that capture strong visuals.

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Section: Ethical Considerationsmentioning

confidence: 99%

How to make a collaborative videography using Phygital affordances to study sensitive topics

Cavusoglu,

Belk

2024

QMR

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“…In proposed social approach, learners are encouraged to provide and define the high-level context to interpret the data from sensed devices, so a simple way is needed to provide users to facilitate their participations. Social tagging provides users equal rights to freely assign arbitrary keywords (i.e., tags) to various resources, in which users, resources and tags are connected together by tagging or post actions (Pan, He, Zhu, & Fu, 2016). If we take the low-level context and the high-level context as resources and tags respectively, the tagging actions done by learners is just similar to generating new information (i.e.…”

Section: Concept Modelmentioning

confidence: 99%

A Social Approach to High-level Context Generation for Supporting Context-aware M-Learning

Pan

Ling

Zhu

et al. 2017

EURASIA J MATH SCI T

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In m-learning environments, context-awareness is for wide use where learners' situations are varied, dynamic and unpredictable. We are facing the challenge of requirements of both generality and depth in generating and processing high-level context. In this paper, we present a social approach which exploits social dynamics and social computing for generating high-level context. It is a novel and generic paradigm where the crowds of learners in m-learning environments directly engage in creating contents about high-level context and interactions by social tagging, and these contents and interactions are further explored to discover more implicit and complex high-level contextual information. We present the concept model, the context representation, the context matrix, and the context retrieval method. We evaluate our approach by a social simulation based experiment. The experimental results demonstrate that the context retrieval performance is improved in both the accuracy and the diversity, and validate that the proposed social approach is effective for generating high-level context.

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“…Lin et al (2015) examined how image descriptions influence users' tagging behavior. Pan et al (2016) pointed out that usage patterns and regularities of tags with varying popularity were correlated to both user activity and resource interest.…”

Section: Introductionmentioning

confidence: 99%

“…Robu et al (2009) used data from Del.icio.us to empirically examine the tag distributions over time and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. Pan et al (2016) examined how users employ various popular tags to annotate resources in social tagging system. Klašnja-Milićević et al (2018) examined tag characteristics of learners in Protus in order to understand the personalities of learner tags and learner's tagging behavior.…”

Section: Introductionmentioning

confidence: 99%

Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users

Yin

Zhou

2018

Inf Syst Front

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With the development of social interaction techniques and social tagging mechanisms, online academic community as a new platform has greatly changed the way users organize and share knowledge. The large amount of social tagging data occurred on online academic community provides us a channel to systematically understand users' tagging behavior. Based on data collected from a specific online academic community, this research first classifies users into two categories: active and inactive users. After that, growth models (damped exponential model, normal model and fluctuating model) are employed to investigate tagging behavior for both active and inactive users. Factors that might influence the likelihood of the growth models are also identified based on multinomial logistic regression. This research expands our understanding on users' tagging behavior and factors that may affect their tagging behavior in the context of online academic community.

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How users employ various popular tags to annotate resources in social tagging: An empirical study

Cited by 6 publications

References 49 publications

How to make a collaborative videography using Phygital affordances to study sensitive topics

How to make a collaborative videography using Phygital affordances to study sensitive topics

A Social Approach to High-level Context Generation for Supporting Context-aware M-Learning

Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users

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