[PDF] Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp | Semantic Scholar (2024)

AiGen-FoodReview is crafted, a multi-modal dataset of 20,144 restaurant review-image pairs divided into authentic and machine-generated content, and attributes from readability and photographic theories are used to score reviews and images, demonstrating their utility as hand-crafted features in scalable and interpretable detection models, with comparable performance.

This study theoretically contributes to the marketing and machine learning lit-erature by exploring and answering the problems that arise when applying machine learning models to predicting online consumer conversion and contributes toThe online advertising literature by exploring consumer characteristics that are effective for retargeting advertisem*nts.

It is shown that products buying fake reviews are highly clustered in the product reviewer network, due to their reliance on common reviewers, which allows them to be detected with high accuracy using both supervised and unsupervised methods.

It is found that supervisory signals such as ‘brand’ promote disentanglement relative to the unsupervised approach, but surprisingly ‘price’ does not.

Analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to Dr. Oz's endorsem*nt by generating more news articles about the ingredient; on average, articles after the endorsem*nt contain higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsem*nt.

This work proposes to leverage the high-quality elite restaurant reviews verified by Yelp to generate fake reviews from the OpenAI GPT review creator and ultimately fine-tune a GPT output detector to predict fake reviews that significantly outperform existing solutions.

An algorithm to predict the effect of influencer video advertising on product sales and how various stakeholders in entertainment commerce can use m-score in a scalable way to optimize content, align incentives, and improve efficiency is developed.

The authors study the difference in the aesthetic scores between two groups of image posters: customers and restaurant owners, showing that the latter group tends to post more aesthetically appealing food images about the restaurant on social media than the former.

To demonstrate the predictive value of visual counts and image details, Kickstarter data is analyzed using flexible machine learning models (Lasso, Ridge, Bayesian Additive Regression Trees, and XGBoost) and the results highlight that visual count features are two of the top three predictors of success and highlight the ease at which researchers can incorporate some information about visual information.

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How social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines is illustrated to highlight the tight linkages between user behavior on social media and search engines.

It is argued that product reviews are multifaceted, and hence the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews.

This paper proposes an approach for firms to explore online user-generated content and “listen” to what customers write about their and their competitors' products and demonstrates this approach using two cases---sedan cars and diabetes drugs---generating market-structure perceptual maps and meaningful insights without interviewing a single consumer.

A machine learning framework that improves the quality of search results through automated personalization based on a user's search history and derives the value of different feature sets -- user-specific features contribute over 50% of the improvement and click-specific over 28%.

The authors quantify the causal impact of read-review content on sales by using supervised deep learning to tag six theory-driven content dimensions and applying a regression discontinuity in time design, and find that aesthetics and price content significantly increase conversion across almost all product categories.

The authors propose a unified framework for extracting the key latent dimensions of consumer satisfaction with quality and ascertaining the valence, labels, validity, importance, dynamics, and heterogeneity of those dimensions using unsupervised latent Dirichlet allocation.

This study proposes a unified framework for extracting the latent dimensions of consumer satisfaction with quality and ascertaining the valence, labels, validity, importance, dynamics, and heterogeneity of those dimensions using unsupervised Latent Dirichlet Allocation (LDA).

Drawing on the paradigm of search and experience goods from information economics, a model of customer review helpfulness is developed and tested and indicates that review extremity, review depth, and product type affect the perceived helpfulness of the review.

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