Category Specific Post Popularity Prediction

Mazloom, Masoud; Pappi, Iliana N.; Worring, Marcel

doi:10.1007/978-3-319-73603-7_48

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…The results of their work states that prediction based on a specific category increases the accuracy of the prediction. Additionally, information related to the objects and scene have strong descriptive power which provides highest correlation with popularity among all the categories [62].…”

Section: Related Workmentioning

confidence: 99%

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Sabet¹

2021

Preprint

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In the last few years, social media has dominated various aspects of people’s life including social events. Users participate more and more in long-running periodical events in social media, by sharing their experiences and preferences. This information provides unprecedented opportunities allowing businesses to promote their brands coverage by using word-of-mouth (WOM), that is enabled by the user generated contents (UGCs). Studying social media content popularity by considering the societies’ behavioral patterns is, therefore, paramount. In this thesis, we inspect users’ engagement motives in long-running events by means of a comprehensive statistical analysis of fashion week events on Instagram. Additionally, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors and apply it on fashion week events. We employ two metrics for implementing a filter feature selection technique, together with an automated grid search for optimizing hyper-parameters in four regression methods: ridge, support vector regressor, gradient tree boosting and neural networks.

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Section: Related Workmentioning

confidence: 99%

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Sabet¹

2021

Preprint

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“…Besides popularity detection and popular level prediction, several attempts have been made to predict the specific popularity volume of contents spread online [14][15][16]. Wang et al [17] proposed a model based on the weak tie theory with a linear regression approach, whose goal was to accurately estimate the popularity of a given viral topic at final based on the observation of its historical characteristics.…”

Section: Popularity Volume Predictionmentioning

confidence: 99%

Prediction of online topics’ popularity patterns

Zhu

Mei

Wei

et al. 2020

Journal of Information Science

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Popularity prediction of online contents is always a tool of emergency management, business decision-making, and public opinion monitoring. Most previous work has made efforts to predict the volumes or levels of popularity, but patterns of popularity evolution are remaining largely unexplored. Actually, topic popularity patterns can offer more detailed information for event detection and early warning. In this article, we proposed an effective method to discover and predict the popularity patterns of topics on the Internet which combined clustering and classification models. This method does not rely on the early time data of topic propagation, so it can predict the future popularity pattern at the initial stage of topic releasing. First, we chose a time series clustering algorithm K-SC to obtain basic types of topic popularity patterns. Then, through acquiring and evaluating multiple features related to the topics including publisher features, outward characteristics of content and textual ones, we built the prediction model of topic popularity patterns based on machine learning methods. The experimental results show that it is suitable to cluster four basic patterns of topic popularity from the experimental data. What’s more, making use of certain initial characteristics, Decision Tree model can effectively predict the popularity pattern of a newly released topic, with an accuracy of 89.4%.

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“…We present in detail the probabilistic method for PDIP generation. In agreement with [1,28,29], the log-scaled number of likes S is considered as the ground truth for absolute image popularity, based on which we make two mild assumptions.…”

Section: Pdip Generation and Database Constructionmentioning

confidence: 99%

“…There are also several studies of IPA using other social networks. Mazloom et al [29] predicted the popularity of brand-related posts on Instagram, and later extended their method to account for category specific posts [28]. Zhang et al [46] addressed user-specific popularity prediction on Instagram with a dual-attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

Intrinsic Image Popularity Assessment

Ding

Wang

2019

Proceedings of the 27th ACM International Conference on Multimedia

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The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I 2 PA) is established. We then develop computational models for I 2 PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I 2 PA. CCS CONCEPTS• Information systems → Content analysis and feature selection; • Human-centered computing → Social media. KEYWORDSIntrinsic image popularity, learning-to-rank, deep neural networks, human behavior analysis.

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Category Specific Post Popularity Prediction

Cited by 18 publications

References 17 publications

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Social Media Posts Popularity Prediction During Long-Running Live Events A case study on Fashion Week

Prediction of online topics’ popularity patterns

Intrinsic Image Popularity Assessment

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