On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

Aloufi, Samah; Zhu, Shiai; Saddik, Abdulmotaleb El

doi:10.3390/s17030631

Cited by 25 publications

(18 citation statements)

References 51 publications

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“…Each experimental setting has been evaluated by computing the Spearman's correlation between the predicted popularity score and the Ground Truth popularity (i.e., Equation (1)). This approach represents the common way to evaluate the classic popularity prediction approaches [4], [11] and to provide a correlation estimate between the features and the value of s scale .…”

Section: Scale Estimationmentioning

confidence: 99%

“…They used sentiment ANPs features (Adjective-Noun Pairs) defined in the Visual Sentiment Ontology (VSO) [10] pointing out that those features have a strong correlation with popularity. Aloufi et al [11] evaluated several techniques to combine different features and investigated the effect of such combinations to predict different levels of interactions (i.e., number of views, number of comments and number of favorites on Flickr).…”

Section: Introductionmentioning

confidence: 99%

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Predicting Social Image Popularity Dynamics at Time Zero

2019

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This work addresses the task of forecasting the popularity achieved by images shared through social media over time. This task is known as ''Popularity Dynamic Prediction''. The work is motivated by the fact that the popularity of social images, which is usually estimated at a precise instant of the post lifecycle, could be affected by the period of the post (i.e., how old is the post). To this aim, we exploited a recently released dataset for popularity dynamic prediction that includes about 20.000 images uploaded on Flickr and their sequences of engagement scores (i.e., number of views, number of comments and number of favorites) with a daily granularity. To build such a dataset, each image and its accompanying meta-data and statistics are downloaded within a few hours from the image posting on the social platform. Then, an automatic procedure collected the daily engagement scores of each observed picture for 30 days. The paper presents an approach in which the engagement score is formulated as a composition of two information associated to the evolution over time (shape) and the order of magnitude (scale) of the sequence. The two properties are inferred individually, then the two results are combined to predict the popularity dynamics over n days. This paper presents exhaustive experiments on the addressed task, evaluating a large number of experimental settings for the predictions of popularity sequences with different length n (n = 10, 20 or 30). In all settings, the prediction performed by the proposed method can be computed before the image is posted (i.e., at time zero). INDEX TERMS Dynamic prediction, image popularity prediction, social media engagement.

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Section: Scale Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Social Image Popularity Dynamics at Time Zero

2019

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“…In order to predict image popularity on social networks, researchers have trained several predictive models using many features, including image content, temporal information, and social context [16][17][18][19][20][21][22][23]40].…”

Section: Related Work For Image Popularity Predictionmentioning

confidence: 99%

“…Gelli [44] used visual sentiments, and users' information to predict the normalized number of views of images on Flickr. Aloufi et al [40] used users' information, number of groups that users belong to, number of tags, images' colors, gists, and sentiments to predict the popularity of images on Flickr.…”

Section: Related Work For Image Popularity Predictionmentioning

confidence: 99%

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

Almgren

Kim²,

Lee

2017

Entropy

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Topological data analysis is a noble approach to extract meaningful information from high-dimensional data and is robust to noise. It is based on topology, which aims to study the geometric shape of data. In order to apply topological data analysis, an algorithm called mapper is adopted. The output from mapper is a simplicial complex that represents a set of connected clusters of data points. In this paper, we explore the feasibility of topological data analysis for mining social network data by addressing the problem of image popularity. We randomly crawl images from Instagram and analyze the effects of social context and image content on an image's popularity using mapper. Mapper clusters the images using each feature, and the ratio of popularity in each cluster is computed to determine the clusters with a high or low possibility of popularity. Then, the popularity of images are predicted to evaluate the accuracy of topological data analysis. This approach is further compared with traditional clustering algorithms, including k-means and hierarchical clustering, in terms of accuracy, and the results show that topological data analysis outperforms the others. Moreover, topological data analysis provides meaningful information based on the connectivity between the clusters.

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“…In this study, image popularity prediction on social media websites is analyzed to better understand the popularity factors for a particular image. Although this problem has recently received significant attention [14]- [17], it remains a challenging task. For example, image popularity prediction can be significantly influenced by various factors (and features), such as visual content, aesthetic quality, user, post metadata, and time; therefore, considering all this multimodal information is crucial for an efficient prediction.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media

Abousaleh

Cheng

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman's Rho, mean absolute error, and mean squared error, respectively.

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On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

Cited by 25 publications

References 51 publications

Predicting Social Image Popularity Dynamics at Time Zero

Predicting Social Image Popularity Dynamics at Time Zero

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media

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