A layer-wise deep stacking model for social image popularity prediction

Lin, Zehang; Huang, Feitao; Li, Yukun; Yang, Zhenguo; Wenyin, Liu

doi:10.1007/s11280-018-0590-1

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…However, finetuning self-supervised language modeling system [36] revolutionized the field recently, language modeling enables systems to learn embedding in a contextualized method, and it yielded even better results on a variety of tasks. We built our self-attention model based on the multi-modal BiTransformer method [14], which enhances the strength of text-only self-supervised representations with the power of state-ofthe-art CNN architectures.…”

Section: Related Workmentioning

confidence: 99%

“…The architecture of the network follows similarly to [14], stacks multiple regression models in multiple layers, which enables the different models to complement and reinforce each other. We define an ensemble regressor [7] network F θ such that:…”

Section: Ensemble Regressor Modelmentioning

confidence: 99%

“…Other factors such as real-life events are tough to include in a prediction model. Recent SMP methods attempt to tackle these complex factors by adding more modalities [4,5,7,12,17], such as images [14,39], relationship networks [25], temporal context [13], tags, and categories.…”

Section: Introductionmentioning

confidence: 99%

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Social Media Popularity Prediction Based on Multi-Modal Self-Attention Mechanisms

Lin

Ople

et al. 2022

IEEE Access

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Popularity prediction using social media is an important task because of its wide range of real-world applications such as advertisements, recommendation systems, and trend analysis. However, this task is challenging because social media is affected by multiple factors that cannot be easily modeled (e.g. quality of content, relevance to viewers, real-life events). Usually, other methods adopt the greedy approach to include as many modalities and factors as possible into their model but treat these features equally. To solve this phenomenon, our proposed method leverages the self-attention mechanism to effectively and automatically fuse different features to achieve better performance for the popularity prediction of a post, where the features used in our model can be mainly categorized into two modalities, semantic (text) and numeric features. With extensive experiments and ablation studies on the training and testing data of the challenging ACM Multimedia SMPD 2020 Challenge dataset, the evaluation results demonstrate the effectiveness of the proposed approach as compared with other methods.

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Section: Related Workmentioning

confidence: 99%

Section: Ensemble Regressor Modelmentioning

confidence: 99%

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Social Media Popularity Prediction Based on Multi-Modal Self-Attention Mechanisms

Lin

Ople

et al. 2022

IEEE Access

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“…Some model it as a binary classification problem (Artzi, Pantel, and Gamon 2012;Backstrom et al 2013). Other formulations include regression (Tsur and Rappoport 2012), multilabel classification (Weng, Menczer, and Ahn 2014), cascades size prediction (Cheng et al 2014;Kobayashi and Lambiotte 2016), self-exciting point process (Mishra, Rizoiu, and Xie 2016), or Hawkes process modeling (Zhao et al 2015;Rizoiu et al 2018) and modeling on social media is also a fruitful research (Liao et al 2019;Mishra 2019;Lin et al 2019).…”

Section: Related Workmentioning

confidence: 99%

Cannot Predict Comment Volume of a News Article before (a few) Users Read It

Shen

Mukherjee

et al. 2021

ICWSM

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Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that user-to-user interaction factors contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health).

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“…Popularity of online contents and user-generated-contents including news, products, and Youtube videos have been analyzed and predicted in [33,35,37,38,49]. Along with the improvement of SNSs, social popularity prediction of the posted images [3,7,16,17,25,28,29,44,48,51] and micro-videos [2,6,19] are focused by both academics and industries. Most of these popularity prediction are conducted by data-driven feature-based learning phases, and some researches embedded them with temporal models representing the variations on time information [31,38,48].…”

Section: Social Popularity Predictionmentioning

confidence: 99%

User-Aware Folk Popularity Rank

Wang

Zhang

Yamasaki

2019

Proceedings of the 27th ACM International Conference on Multimedia

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In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.

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A layer-wise deep stacking model for social image popularity prediction

Cited by 12 publications

References 36 publications

Social Media Popularity Prediction Based on Multi-Modal Self-Attention Mechanisms

Social Media Popularity Prediction Based on Multi-Modal Self-Attention Mechanisms

Cannot Predict Comment Volume of a News Article before (a few) Users Read It

User-Aware Folk Popularity Rank

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