An Investigation of Twitter and Facebook Abandonment

Guo, Yan Ru; Hoe-Lian, Dion; Rashid, Nor Aishah Binte Mohamed; Han, Pyae Pyae; Sun, Shengbo

doi:10.1109/itng.2014.10

Cited by 14 publications

(1 citation statement)

References 21 publications

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“…Both previous work [2] and our data analysis show that the average number of posts per user on Twitter is obviously higher than Facebook. However, surveys suggest that information overload is higher on Facebook than Twitter [16], [17]. Hence, Facebook should have less capacity about posts, and most users should publish a smaller number of posts on Facebook than Twitter.…”

Section: Introductionmentioning

confidence: 99%

Predicting Platform Preference of Online Contents Across Social Media Networks

et al. 2019

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Currently, many professional users tend to promote their websites and brands via multiple online social networks. During activities of information dissemination, the users are confronted with the problem of platform selection. For a post, its platform selection should be based on platform preference, which refers to the platform in which the post can obtain more engagement. In this paper, we focus on this problem by proposing a model to predict platform preference. Specifically, we build a content similarity-based Multi-Task Learning model to predict platform preference of posts. This model takes user specific characters into account and incorporates the regularization term under our validated hypothesis about content similarity. Based on data from Twitter and Facebook, the experiments reveal this model significantly outperforms a number of the baselines. The prediction of platform preference can provide insight for users conducting platform selection to obtain more engagement. INDEX TERMS Social media, popularity prediction, multi-task learning, Twitter, Facebook. YUXIA XUE received the M.S. degree from the University of Electronic Science and Technology of China. She is currently a Lecturer with Henan University. Her research interests include online social networks, intelligence analysis, and knowledge management. CHUNJING XIAO received the Ph.D. degree from the University of Electronic Science and Technology of China. He was a Visiting Scholar with the Department of Electrical Engineering and Computer Science, Northwestern University. He is currently an Associate Professor with the School of Computer and Information Engineering, Henan University. His research interests include online social networks, information retrieval, machine learning, wireless networks, and the Internet of Things.

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Section: Introductionmentioning

confidence: 99%

Predicting Platform Preference of Online Contents Across Social Media Networks

et al. 2019

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Digital Domesticity

Kennedy¹,

Arnold²,

Gibbs³

et al. 2020

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This book, Digital Domesticity: Media, Materiality, and Home Life, is concerned with the home, but it is not bounded by the home. While the home provides a necessary anchor point for our empirical and theoretical work, we are well aware that the home is not self-contained but is a node in multiple commercial, cultural, and technical networks, all of which interact, and all of which have local implications and global reach. The home’s socio-technical ecology operates in recursive relations with these much larger ecologies, none of which can be ignored if the home is to be understood. This book unearths this digital domesticity through accounts of evolving socio-technical relations as they unfold in processes of adopting and adapting to innovations; using, maintaining, and neglecting the complex of technologies in the home; and confronting the obsolescence of particular technologies and failure of systems of consumer technologies.

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