The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Willemijn Van Dolen - One of the best experts on this subject based on the ideXlab platform.
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a spatio Temporal Category representation for brand popularity prediction
International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
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ICMR - A Spatio-Temporal Category Representation for Brand Popularity Prediction
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
Gijs Overgoor - One of the best experts on this subject based on the ideXlab platform.
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a spatio Temporal Category representation for brand popularity prediction
International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
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ICMR - A Spatio-Temporal Category Representation for Brand Popularity Prediction
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
Masoud Mazloom - One of the best experts on this subject based on the ideXlab platform.
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a spatio Temporal Category representation for brand popularity prediction
International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
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ICMR - A Spatio-Temporal Category Representation for Brand Popularity Prediction
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
Marcel Worring - One of the best experts on this subject based on the ideXlab platform.
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a spatio Temporal Category representation for brand popularity prediction
International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
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ICMR - A Spatio-Temporal Category Representation for Brand Popularity Prediction
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
Robert Rietveld - One of the best experts on this subject based on the ideXlab platform.
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a spatio Temporal Category representation for brand popularity prediction
International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
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ICMR - A Spatio-Temporal Category Representation for Brand Popularity Prediction
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 2017Co-Authors: Gijs Overgoor, Masoud Mazloom, Marcel Worring, Robert Rietveld, Willemijn Van DolenAbstract:Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new Category based popularity prediction method by incorporating the spatio-Temporal dimension in the representation. In particular, we focus on brands as a specific Category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing Category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.
