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Characterizing Teacher Connections in Online Social Media: A Case Study on Pinterest

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Jiliang TangAuthors Info & Claims

L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale

Pages 249 - 252

https://doi.org/10.1145/3386527.3405941

Published: 12 August 2020 Publication History

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Abstract

Increasingly many teachers are turning to online social media to supplement educational resources and meet students' needs in the classrooms. The diffusion of information from online social media to the classroom is significantly faster than traditional curriculum-based approaches. However, this is contingent upon how well teachers across an online social media network are connected. To understand this, we perform a thorough and large-scale investigation of teacher connections in online social media, which is lacking in the literature. To make this feasible, we construct a large dataset of teachers on Pinterest, an image-based popular online social media. Our dataset includes 540 teachers across 5 states and 48 districts, thousands of connections they have established (either with their peers or some other Pinterest users), and all the resources they have shared in their accounts. Then, taking into account some crucial teacher-related attributes (e.g., their districts, grade levels, etc), we characterize direct and indirect teacher connections. Moreover, we compare the physical (face to face) and virtual (Pinterest) network of our surveyed teachers using several graph-related metrics. The finding in this study can serve as a basis to investigate teachers on social media in a deeper manner.

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The diffusion of information from online social media to the classroom is significantly faster than traditional curriculum-based approaches. However, this is contingent upon how well teachers across an online social media network are connected. To understand this, we perform a large-scale investigation of teacher connections in online social media. To make this feasible, we construct a large dataset of teachers on Pinterest, an image-based popular online social media. Our dataset includes 540 teachers across 5 states and 48 districts, thousands of connections they have established (either with their peers or some other Pinterest users), and all the resources they have shared in their accounts. Then, taking into account some crucial teacher-related attributes (e.g., their districts, grade levels, etc.), we characterize direct and indirect teacher connections. Moreover, we compare the physical (face to face) and virtual (Pinterest) network of our surveyed teachers using several graph-related metrics.

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Cited By

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Farokhi SFoumani AQi XDerr TKarimi H(2025)EDGE-UP: Enhanced Dynamic GNN Ensemble for Unfollow Prediction in Online Social NetworksSocial Networks Analysis and Mining10.1007/978-3-031-78541-2_2(20-39)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-78541-2_2
Grabosch A(2024)Lehrer:innen als Influencer:innenTeachers as InfluencersZeitschrift für Bildungsforschung10.1007/s35834-024-00459-0Online publication date: 11-Dec-2024
https://doi.org/10.1007/s35834-024-00459-0
Chen ZTorphy Knake KKarimi HDonzella N(2023)Building a nationally representative sample of teachers’ online and offline: the Public Instructional Network of School ResourcesJournal of Research on Technology in Education10.1080/15391523.2023.2266060(1-25)Online publication date: 12-Dec-2023
https://doi.org/10.1080/15391523.2023.2266060
Show More Cited By

Index Terms

Characterizing Teacher Connections in Online Social Media: A Case Study on Pinterest
1. Applied computing
  1. Education
2. Information systems
  1. World Wide Web

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Published In

L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale

August 2020

442 pages

ISBN:9781450379519

DOI:10.1145/3386527

General Chair:
David Joyner
Georgia Tech
,
Program Chairs:
Rene Kizilcec
Cornell University
,
Susan Singer
Rollins College

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2020

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William T. Grant Foundation
Defense Advanced Research Projects Agency
National Science Foundation
Center for Business and Social Analytics at MSU 2017 and 2020

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L@S '20

L@S '20: Seventh (2020) ACM Conference on Learning @ Scale

August 12 - 14, 2020

Virtual Event, USA

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Overall Acceptance Rate 117 of 440 submissions, 27%

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Farokhi SFoumani AQi XDerr TKarimi H(2025)EDGE-UP: Enhanced Dynamic GNN Ensemble for Unfollow Prediction in Online Social NetworksSocial Networks Analysis and Mining10.1007/978-3-031-78541-2_2(20-39)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-78541-2_2
Grabosch A(2024)Lehrer:innen als Influencer:innenTeachers as InfluencersZeitschrift für Bildungsforschung10.1007/s35834-024-00459-0Online publication date: 11-Dec-2024
https://doi.org/10.1007/s35834-024-00459-0
Chen ZTorphy Knake KKarimi HDonzella N(2023)Building a nationally representative sample of teachers’ online and offline: the Public Instructional Network of School ResourcesJournal of Research on Technology in Education10.1080/15391523.2023.2266060(1-25)Online publication date: 12-Dec-2023
https://doi.org/10.1080/15391523.2023.2266060
Miller CMorewood A(2022)Social MediaResearch, Practice, and Innovations in Teacher Education During a Virtual Age10.4018/978-1-6684-5316-2.ch013(255-270)Online publication date: 18-Nov-2022
https://doi.org/10.4018/978-1-6684-5316-2.ch013
Karimi HKnake KFrank K(2022)Teachers in Social Media: A Gender-aware Behavior Analysis2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020354(1842-1849)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020354
Southerton ELee V(2021)Current Approaches in Teacher Learning on Digital Social PlatformsHandbook of Research on Transforming Teachers’ Online Pedagogical Reasoning for Engaging K-12 Students in Virtual Learning10.4018/978-1-7998-7222-1.ch030(624-641)Online publication date: 25-Jun-2021
https://doi.org/10.4018/978-1-7998-7222-1.ch030
Karimi HTang JWeiss XHuang J(2021)Automatic Identification of Teachers in Social Media using Positive Unlabeled Learning2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671476(643-652)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671476
Karimi HTorphy KDerr TFrank KTang J(2020)Understanding and Promoting Teacher Connections in Online Social Media: A Case Study on Pinterest2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)10.1109/TALE48869.2020.9368377(536-541)Online publication date: 8-Dec-2020
https://doi.org/10.1109/TALE48869.2020.9368377

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Recommendations

The Social Structuration of Six Major Social Media Platforms in the United Kingdom: Facebook, LinkedIn, Twitter, Instagram, Google+ and Pinterest

Uses and gratifications of social networking sites for bridging and bonding social capital

Social media and online political participation

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