Modeling and predicting the popularity of online news based on temporal and content-related features

Canneyt, Steven Van; Leroux, Philip; Dhoedt, Bart; Demeester, Thomas

doi:10.1007/s11042-017-4348-z

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…Expression on social media posts has been studied for various platforms, such as Facebook (Van Canneyt et al 2018;Srinivasan et al 2013;, Instagram (Jaakonmäki, Müller, and vom Brocke 2017;Ferrara, Interdonato, andTagarelli 2014), Twitter (Van Canneyt et al 2018;Bandari, Asur, and Huberman 2012;, YouTube (Ma, Yan, and Chen 2017;Vallet et al 2015), and Reddit (Stoddard 2015). However, the previous work has mostly focused on views, likes, shares, and comments on the same platform, with little focus on how these engagement metrics compare across platforms and how public the user engagement is.…”

Section: Related Work User Engagement Modelingmentioning

confidence: 99%

View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

Aldous

Jansen

2019

ICWSM

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We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and platforms. Finally, we show that one can predict if an article will be publicly shared to another platform by individuals with precision of approximately 80%. This research has implications for news organizations desiring to increase and to prioritize types of user engagement.

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Section: Related Work User Engagement Modelingmentioning

confidence: 99%

View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

Aldous

Jansen

2019

ICWSM

View full text Add to dashboard Cite

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“…Platform recommendation algorithms also favor videos released in specified timeframes. Canneyt et al found that the time when information was posted on Twitter and Facebook largely determined its initial popularity [17].…”

Section: Related Work 21 Factors Influencing Popularitymentioning

confidence: 99%

DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Ding,

Hou,

Liu

et al. 2023

Electronics

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Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset.

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“…They extracted features of historical diffusion and multimedia meta information, and proposed a concept drift-based mechanism to conduct popularity prediction. Canneyt et al [40] firstly analyzed view patterns of online news on multiple platforms, and then predicted future popularity by the combination of features related to content, meta-data, and the temporal behavior. Rizoiu et al [41] developed a Hawkes intensity process, which links exogenous inputs from Twitter, and endogenous responses within YouTube, to predict the popularity of online contents.…”

Section: Related Workmentioning

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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Modeling and predicting the popularity of online news based on temporal and content-related features

Cited by 16 publications

References 21 publications

View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Predicting Platform Preference of Online Contents Across Social Media Networks

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