A Spatio-Temporal Category Representation for Brand Popularity Prediction

Overgoor, Gijs; Mazloom, Masoud; Worring, Marcel; Rietveld, Robert; Dolen, Willemijn van

doi:10.1145/3078971.3078998

Cited by 11 publications

(14 citation statements)

References 20 publications

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“…The authors outlined a set of post features which have a positive impact on indicators such as the number of likes and comments, including image vividness and post interactivity. In the same line, several other works investigated similar indicators with multiple social features on different networks like Facebook [20,32], Twitter [1] and Instagram [28,31].…”

Section: Social Media Marketing and Computational Marketingmentioning

confidence: 99%

“…The dataset is not suitable for our task, since it only contains images with a brand logo, when one of the main points of our work is that a more complex set of brand associations exists beyond the product. Other works that study the popularity of brands [1,31,32] use datasets where the task of discovering content for brands may be applied, however they are private, made ad-hoc for the popularity task or publicly unavailable. We hence decided to build our own dataset.…”

Section: Existing Datasetsmentioning

confidence: 99%

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Beyond the Product

Gelli

Uricchio

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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Brands and organizations are using social networks such as Instagram to share image or video posts regularly, in order to engage and maximize their presence to the users. Differently from the traditional advertising paradigm, these posts feature not only specific products, but also the value and philosophy of the brand, known as brand associations in marketing literature. In fact, marketers are spending considerable resources to generate their content in-house, and increasingly often, to discover and repost the content generated by users. However, to choose the right posts for a brand in social media remains an open problem. Driven by this real-life application, we define the new task of content discovery for brands, which aims to discover posts that match the marketing value and brand associations of a target brand. We identify two main challenges in this new task: high inter-brand similarity and brand-post sparsity; and propose a tailored content-based learning-to-rank system to discover content for a target brand. Specifically, our method learns fine-grained brand representation via explicit modeling of brand associations, which can be interpreted as visual words shared among brands. We collected a new large-scale Instagram dataset, consisting of more than 1.1 million image and video posts from the history of 927 brands of fourteen verticals such as food and fashion. Extensive experiments indicate that our model can effectively learn fine-grained brand representations and outperform the closest state-of-the-art solutions.

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Section: Social Media Marketing and Computational Marketingmentioning

confidence: 99%

Section: Existing Datasetsmentioning

confidence: 99%

Beyond the Product

Gelli

Uricchio

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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“…In the field of spatial dimensions, Overgoor focused on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a period of time [34]. Wang presented a spatio-temporal mapping system for visualizing a summary of geo-tagged social media as tags in a cloud, and it is associated with a web page by detecting spatio-temporal events [35].…”

Section: Spatio-temporal Patterns Of Popularity Dynamicsmentioning

confidence: 99%

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019

J Big Data

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BackgroundWith the rapid development of mobile Internet technology, human society has entered an era of high interconnection between human and information. Especially the emergence of online social networks, such as Weibo in China, LinkedIn, Twitter, Face-book and so on, because of its high interaction and strong group participation, makes many related information converging and merging, leading to the emergence of hot topics on the Internet. These hot topics are the aggregation of information about current events (e.g., natural disasters, sports news, celebrity news), which have geotags-timestamp information. These metadata (geotags and timestamp) are embedded within the content of the message, so that the analysis of an event can be performed by applying a spacetime classification. In addition, the hot topic is the sprout of the network public opinion. Network public opinion has an important impact on the stability of the country and society. Therefore, the popularity of hot topics is studied from the spatial and temporal dimension can better understand the dissemination of public opinion. This paper uses the data of Sina Weibo, one of the largest micro-blogging system in China (https ://weibo .com/), to study the following two questions: (1) study the classification problem of hot topic on Weibo, cluster the popularity time series by clustering algorithm;(2) study the spatial distribution characteristics of hot topic on Weibo based on location label data. AbstractIn this paper, a quantitative temporal and spatial analysis of the dynamics of hot topics popularity in Micro-blogging system was provided. Firstly, the popularity time series of 1167 hot topics were counted and calculated by Excel. Secondly, based on MAT-LAB software,the popularity time series were clustered into six clusters by K-spectral centroid (K-SC) clustering algorithm. Thirdly, we analyzed temporal patterns and spatial patterns of popularity dynamics of topics by statistical methods. The results show that temporal popularity of micro-blogging topics is rapidly dying, and the distribution of popularity is subject to the power law form. In addition, most of the Micro-blogging topics are global topic. Our results can provide a literature reference for studying the influence of online hot topics and the evolution of public opinion. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Predicting how popular a post will be among other users in the user's network or in public, has become interesting for marketing and business [16], political and economic sciences [12] and decision-making strategies of campaigns targeting on social media crowds [11]. Moreover, predicting post popularity is important for the self-evolution of the social media [8].…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches have been proposed to predict the popularity of post focusing on the effect of visual low-and high-level contents [4,9,14,16,6,15,13], textual contents such as tweets, user's tags and comments [18,1,7], and visual contents along with the Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Category Specific Post Popularity Prediction

Mazloom

Pappi

Worring

2018

Lecture Notes in Computer Science

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Social media have become dominant in everyday life during the last few years where users share their thoughts and experiences about their enjoyable events in posts. Most of these posts are related to different categories related to: activities, such as dancing, landscapes, such as beach, people, such as selfie, and animals such as pets. While some of these posts become popular and get more attention, others are completely ignored. In order to address the desire of users to make popular posts, several researches have studied post popularity prediction. Existing works focus on predicting the popularity without considering the category type of the post. In this paper we propose category specific post popularity prediction using visual and textual content for action, scene, people and animal categories. In this way we aim to answer the question What makes a post belonging to specific action, scene, people or animal category popular?. To answer to this question we perform several experiments on a collection of 65K posts crawled from Instagram related to four categories: action, scene, people, and animal.

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A Spatio-Temporal Category Representation for Brand Popularity Prediction

Cited by 11 publications

References 20 publications

Beyond the Product

Beyond the Product

Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

Category Specific Post Popularity Prediction

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