Situation and Behavior Understanding by Trope Detection on Films

Chang, Chen-Hsi; Su, Hung-Ting; Hsu, Jui-Heng; Wang, Yu-Siang; Chang, Yu‐Cheng; Liu, Zhe Yu; Chang, Ya-Liang; Cheng, Wen-Fang; Wang, Ke-Jyun; Hsu, Winston H.

doi:10.1145/3442381.3449806

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…This information was not readily available for all sneaker items in our dataset, yet future research can combine such heterogeneous meta features for analysis. One can use sentence embedding from language models, such as BERT, to process text-based user reviews or product descriptions [7,35,44]. Another approach is to collaborate with the fashion industry of various other product types, extending the application of the proposed model.…”

Section: Discussionmentioning

confidence: 99%

Using Web Data to Reveal 22-Year History of Sneaker Designs

Park

Song

Han

et al. 2022

Proceedings of the ACM Web Conference 2022

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Web data and computational models can play important roles in analyzing cultural trends. The current study presents an analysis of 23,492 sneaker images and metadata collected from a global reselling shop, StockX.com. Based on data encompassing 22 years from 1999 to 2020, we propose a sneaker design index that helps track changes in the design characteristics of sneakers using a contrastive learning method. Our data suggest that sneaker designs have been employing brighter colors and lower hue and saturation values over time. We also observe how popular brands have continued to build their unique identities in shape-related design space. The embedding analysis also predicts which sneakers will likely see a high premium in the reselling market, suggesting viable algorithmdriven investment and design strategies. The current work is one of the first publicly available studies to analyze product design evolution over a long historical period and has implications for the novel use of Web data to understand cultural patterns that are otherwise difficult to assess.

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Section: Discussionmentioning

confidence: 99%

Using Web Data to Reveal 22-Year History of Sneaker Designs

Park

Song

Han

et al. 2022

Proceedings of the ACM Web Conference 2022

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“…This involves seeking annotations from a broader array of sources, including non-Western references, to ensure a richer and more culturally varied narrative landscape. This could be accomplished by incorporating data from tvtropes.org in different languages or by developing automated methods for trope detection, an active area of research [21,69]. Several other potential approaches can be considered to address biases.…”

Section: Inclusiveness and Awarenessmentioning

confidence: 99%

TaleStream: Supporting Story Ideation with Trope Knowledge

Chou,

Siu,

Lipka

et al. 2023

Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology

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Figure 1: Our system allows users to generate story ideas by providing a set of inputs, which can include tropes and text, e.g., the Starfish Aliens trope and "Falling in love?" (left). Leveraging trope knowledge extracted from tvtropes.org, our suggestion algorithm automatically surfaces relevant tropes as story ideas with additional information (middle). Users can add some of these suggestions to their canvas, e.g., Humanity is Infectious and Interspecies Romance (right). Through iteration with the system, users can ideate and develop their stories.

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Computational Support for Trope Analysis of Textual Narratives

Chaudhary

Jhala

2022

Interactive Storytelling

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Situation and Behavior Understanding by Trope Detection on Films

Cited by 4 publications

References 20 publications

Using Web Data to Reveal 22-Year History of Sneaker Designs

Using Web Data to Reveal 22-Year History of Sneaker Designs

TaleStream: Supporting Story Ideation with Trope Knowledge

Computational Support for Trope Analysis of Textual Narratives

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