The Champion of Images: Understanding the role of images in the decision-making process of online hotel bookings

Images are vitally important in interesting consumers and helping them to make decisions. Images of a hotel are particularly important and were used to sell hotels even before the Internet, when travel agencies would often have brochures about hotel properties that they used to entice travelers. On many online travel agency (OTA) websites, the hotel\u27s image can take up 33% of the space on the hotel property page, but the importance of this image in the decision-making process has yet to be studied. For many OTAs, there are currently no quantitative analytic methods that help determine which image to display in this critical location. In this research, we use deep learning to extract information directly from hotel images and we apply image analytics to understand the importance of this information in the online hotel booking process. To provide managerial insights, we will combine a prediction model, with the t-distributed Stochastic Neighbor Embedding (t-SNE) to classify and understand the types of images hotels generally use as their thumbnail or champion image and what aspects of these images elicit consumers to consider and book a hotel

Text vs. Image: An application of unsupervised multi-modal machine learning to online reviews

Online user-generated reviews provide a unique view into consumer perceptions of a business. Extant research has demonstrated that text mining provides insight from textual reviews. More recently, we haven seen the adoption of image mining techniques to analyze visual content as well. With data comprising of user-generated imagery (UGI) and textual reviews, we propose to perform a combination of text- and image mining techniques to extract relevant attributes from both modalities. The analysis allows for a comparison between textual and visual content in online reviews. For the UGI analysis, we use a Deep Embedded Clustering model and for the User Generated Text Analysis we use a TF-IDF based mechanism to obtain attributes and polarities. The overall goal is to extract maximum information from text and images and compare the insights we gather from both. We analyze if any modality is self-sufficient or better than the other and also if both modalities combine to give similar or contrasting insights

Automated Detection of Skin Tone Diversity in Visual Marketing Communication

Companies invest heavily in diversity, equity, and inclusion efforts. Specifically, the representation of people in visual marketing communication is often considered a manifestation of diversity policies. We propose a standard framework built on machine learning to create novel measures quantifying skin tone dynamics. We first use the Swin Transformer to extract skin pixels from images. Next, the K-means algorithm is deployed to classify skin tone components from the extracted skin pixels, accounting for multiple people with distinct skin colors in an image. Using images posted by 34 fashion brands on Instagram and Twitter, we demonstrate a useful application of the tool. The results highlight that, in the past two years, the fashion industry has slightly increased its diversity, represented by the increased variety of skin tones of people included in social media posts. Our method allows for automated detection of objective measures of skin-tone diversity in visual marketing communications

In the Eye of the Reviewer: An Application of Unsupervised Clustering to User Generated Imagery in Online Reviews

Mining opinions from online reviews has been shown to be extremely valuable in the past decades. There has been a surge of research focused on understanding consumer brand perceptions from the textual content of online reviews using text mining methods. With the increase in smartphone usage and ease of posting images, these reviews now often contain visual content. We propose an unsupervised cluster method to understand the user-generated imagery (UGI) of online reviews in the travel industry. Using the deep embedded clustering model we group together similar UGI and examine the average review ratings of these clusters to identify imagery associated with positive and negative reviews. After training the method on the entire dataset, we map out individual hotels and their corresponding UGI to show how hotel managers can use the method to understand their performance in particular areas of customer service based on UGI. The performance in a cluster relative to the population can be a clear indicator of areas that need improvement or areas that should be highlighted in the hotel\u27s marketing efforts. Overall, we present a useful application using visual analytics for mining consumer opinions and perceptions directly from image data

Detecting fake-review buyers using network structure: Direct evidence from Amazon

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve for over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using laboratory-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake review buyers even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation