Style in the Age of Instagram

Park, Jae-Hyuk; Ciampaglia, Giovanni Luca; Ferrara, Emilio

doi:10.1145/2818048.2820065

Cited by 37 publications

(16 citation statements)

References 37 publications

(31 reference statements)

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“…Quantifying and forecasting success refers to the broader body of work that attempts to discover the patterns and performance trajectories that correlate with certain desirable outcomes: from forecasting highly-cited academic authors and papers [20,38] to predicting future Nobel Prize winners [25], from uncovering successful fund-raising campaigns [27], to early identifying the next top model [29], or movie box office hit [11]. The new field of Science of Success brings a strong data-driven perspective on applied forecasting problems set in the real world.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we aim to determine whether it is possible to model and predict these career trajectories under the emerging framework of Science of Success [9,14]: recent work studying how careers in different fields, as well as individual and team success, can be predicted early by leveraging records of performance from digital traces. This data-driven framework has been applied to domains as diverse as education and academia [17,21,35], (e)sports [7,8,34,39,42], social media [3,13,24,36], culture [2,40], and even the entertainment industry [29,33].…”

Section: Introductionmentioning

confidence: 99%

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Mining and Forecasting Career Trajectories of Music Artists

Arakelyan¹,

Martin²,

Ferrara³

et al. 2018

Proceedings of the 29th on Hypertext and Social Media

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Many musicians, from up-and-comers to established artists, rely heavily on performing live to promote and disseminate their music. To advertise live shows, artists often use concert discovery platforms that make it easier for their fans to track tour dates. In this paper, we ask whether digital traces of live performances generated on those platforms can be used to understand career trajectories of artists. First, we present a new dataset we constructed by crossreferencing data from such platforms. We then demonstrate how this dataset can be used to mine and predict important career milestones for the musicians, such as signing by a major music label, or performing at a certain venue. Finally, we perform a temporal analysis of the bipartite artist-venue graph, and demonstrate that high centrality on this graph is correlated with success.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining and Forecasting Career Trajectories of Music Artists

Arakelyan¹,

Martin²,

Ferrara³

et al. 2018

Proceedings of the 29th on Hypertext and Social Media

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous works attempted to predict the popularity of Instagram posts by using surrogate signals such as number of likes or followers (Almgren et al, 2016;De et al, 2017). Others used social media data in order to model the popularity of fashion industry icons (Park et al, 2016). A thorough inspection of clothing styles around the world has also been conducted (Matzen et al, 2017).…”

Section: Related Work and Motivationmentioning

confidence: 99%

EmojiGAN: learning emojis distributions with a generative model

Mazoure

Doan

Ray³

2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Generative models have recently experienced a surge in popularity due to the development of more efficient training algorithms and increasing computational power. Models such as adversarial generative networks (GANs) have been successfully used in various areas such as computer vision, medical imaging, style transfer and natural language generation. Adversarial nets were recently shown to yield results in the image-to-text task, where given a set of images, one has to provide their corresponding text description. In this paper, we take a similar approach and propose a image-to-emoji architecture, which is trained on data from social networks and can be used to score a given picture using ideograms. We show empirical results of our algorithm on data obtained from the most influential Instagram accounts.

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“…Similar studies have focused on other cultural products, from music to movies, like using on-line reviews to forecast motion pictures sales [10], predicting the success of music and movie products by analyzing blogs [11], predicting success within the fashion industry using social media such as Instagram [12]. Nevertheless, the early-prediction of success is of great importance in cultural products.…”

Section: Introductionmentioning

confidence: 99%

Success in books: predicting book sales before publication

et al. 2019

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Reading remains a preferred leisure activity fueling an exceptionally competitive publishing market: among more than three million books published each year, only a tiny fraction are read widely. It is largely unpredictable, however, which book will that be, and how many copies it will sell. Here we aim to unveil the features that affect the success of books by predicting a book's sales prior to its publication. We do so by employing the Learning to Place machine learning approach, that can predicts sales for both fiction and nonfiction books as well as explaining the predictions by comparing and contrasting each book with similar ones. We analyze features contributing to the success of a book by feature importance analysis, finding that a strong driving factor of book sales across all genres is the publishing house. We also uncover differences between genres: for thrillers and mystery, the publishing history of an author (as measured by previous book sales) is highly important, while in literary fiction and religion, the author's visibility plays a more central role. These observations provide insights into the driving forces behind success within the current publishing industry, as well as how individuals choose what books to read.

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Style in the Age of Instagram

Cited by 37 publications

References 37 publications

Mining and Forecasting Career Trajectories of Music Artists

Mining and Forecasting Career Trajectories of Music Artists

EmojiGAN: learning emojis distributions with a generative model

Success in books: predicting book sales before publication

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