Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph

Niu, Xiang; Li, Lusong; Mei, Tao; Shen, Jialie; Xu, Ke

doi:10.1109/icme.2012.43

Cited by 17 publications

(12 citation statements)

References 11 publications

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“…Niu et al [16] introduce a weighted bipartite graph model, called Incomplete Network-based Inference (INI), to predict image popularity based on network relationships. Cha et al [4] study how the popularity of pictures evolves over time, showing that even popular photos propagate slowly and that they do not spread widely.…”

Section: Popularity Prediction On Social Networkmentioning

confidence: 99%

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"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

McParlane

Moshfeghi

Jose

2014

Proceedings of International Conference on Multimedia Retrieval

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Predicting popular content is a challenging problem for social media websites in order to encourage user interactions and activity. Existing works in this area, including the recommendation approach used by Flickr (called "interestingness 1 "), consider only click through data, tags, comments and explicit user feedback in this computation. On image sharing websites, however, many images are annotated with no tags and initially, an image has no interaction data. In this case, these existing approaches fail due to lack of evidence. In this paper, we therefore focus on image popularity prediction in a cold start scenario (i.e. where there exist no, or limited, textual/interaction data), by considering an image's context, visual appearance and user context. Specifically, we predict the number of comments and views an image has based on a number of new features for this propose. Experimenting on the MIR-Flickr 1M collection, we are able to overcome the problems associated with popularity prediction in a cold start, achieving accuracy of up to 76%.

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Section: Popularity Prediction On Social Networkmentioning

confidence: 99%

“…More recently, work has attempted to predict the popularity of images on Flickr 2 [16]. However, this approach, as well as the others mentioned, rely on the interactions of users (e.g.…”

Section: Introductionmentioning

confidence: 97%

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

McParlane

Moshfeghi

Jose

2014

Proceedings of International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

show abstract

“…Previous work on analyzing engagement on Instagram [5,13,17,18,24,26,27] treated images posted on different time periods the same way. However, as an account gets older and gathers more followers, the average number of likes for new photos posted goes up.…”

Section: Definition Of Engagement On Instagrammentioning

confidence: 99%

“…Zhu et al [2], show that photos using filters are more likely to be viewed and commented on, while Bakhshi et al [1], show that photos with faces attract more likes and comments. Several works have attempted to predict engagement using various combinations of text-based, network-based, and user-based features [5,6,10,13,17,18,24,26,27]. However, all of these works use a large dataset, containing many users.…”

Section: Introductionmentioning

confidence: 99%

Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks

Wang

Liu

Vosoughi

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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Instagram has become a great venue for amateur and professional photographers alike to showcase their work. It has, in other words, democratized photography. Generally, photographers take thousands of photos in a session, from which they pick a few to showcase their work on Instagram. Photographers trying to build a reputation on Instagram have to strike a balance between maximizing their followers' engagement with their photos, while also maintaining their artistic style. We used transfer learning to adapt Xception, which is a model for object recognition trained on the Ima-geNet dataset, to the task of engagement prediction and utilized Gram matrices generated from VGG19, another object recognition model trained on ImageNet, for the task of style similarity measurement on photos posted on Instagram. Our models can be trained on individual Instagram accounts to create personalized engagement prediction and style similarity models. Once trained on their accounts, users can have new photos sorted based on predicted engagement and style similarity to their previous work, thus enabling them to upload photos that not only have the potential to maximize engagement from their followers but also maintain their style of photography. We trained and validated our models on several Instagram accounts, showing it to be adept at both tasks, also outperforming several baseline models and human annotators.

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“…Totti et al [42] predicted the popularity of images using aesthetics and users' information on Pinterest; they measured popularity using the number of repins. Niu et al [43] predicted the popularity of images on Flickr using network-based features, such as centrality analysis; the number of views was used as a popularity measurement. Gelli [44] used visual sentiments, and users' information to predict the normalized number of views 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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Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph

Cited by 17 publications

References 11 publications

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

"Nobody comes here anymore, it's too crowded"; Predicting Image Popularity on Flickr

Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks

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

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