Dissecting AI-Generated Fake Reviews: Detection and Analysis of GPT-Based Restaurant Reviews on Social Media

Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits

Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media

Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, thus posing challenges for social media platforms to detect these machine-generated fake reviews. We propose 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 outperforms existing solutions. We further apply the model to predict non-elite reviews and identify the patterns across several dimensions, such as review, user and restaurant characteristics, and writing style. We show that social media platforms are continuously challenged by machine-generated fake reviews, although they may implement detection systems to filter out suspicious reviews.Comment: Paper submitted to KDD2023. 8 pages, 5 figure

Operationalizing mild cognitive impairment criteria in small vessel disease: The VMCI-Tuscany Study

Introduction Mild cognitive impairment (MCI) prodromic of vascular dementia is expected to have a multidomain profile. Methods In a sample of cerebral small vessel disease (SVD) patients, we assessed MCI subtypes distributions according to different operationalization of Winblad criteria and compared the neuroimaging features of single versus multidomain MCI. We applied three MCI diagnostic scenarios in which the cutoffs for objective impairment and the number of considered neuropsychological tests varied. Results Passing from a liberal to more conservative diagnostic scenarios, of 153 patients, 5% were no longer classified as MCI, amnestic multidomain frequency decreased, and nonamnestic single domain increased. Considering neuroimaging features, severe medial temporal lobe atrophy was more frequent in multidomain compared with single domain. Discussion Operationalizing MCI criteria changes the relative frequency of MCI subtypes. Nonamnestic single domain MCI may be a previously nonrecognized type of MCI associated with SVD

Cheap eats or fine diner? Discovering social media signals for restaurant price level prediction

In this paper, we discover signals from user-generated contents about post-purchase customer experience on the Yelp platform for predicting restaurant price level. We combine business, textual and visual signals extracted from a large-scale dataset with reviews and photos, by per-forming topic modeling to identify thematic content related to customer perceived experience, as well as employing an aesthetics assessment model to evaluate visual characteristics. Our results show that social media signals from reviews and photos may significantly improve the model’s predictive power and help explain the differences in customer perceived value between budget restaurants and fine-dining restaurants

Exploring food aesthetics portrayed on social media using deep learning

The authors acknowledge financial support from Fundação para a Ciência e Tecnologia (UID/ECO/00124/2019) by LISBOA-01-0145-FEDER007722 and Social Sciences Data Lab PINFRA/22209/2016Purpose The purpose of this paper is to explore and examine discrepancies of food aesthetics portrayed on social media across different types of restaurants using a large-scale data set of food images. Design/methodology/approach A neural food aesthetic assessment model using computer vision and deep learning techniques is proposed, applied and evaluated on the food images data set. In addition, a set of photographic attributes drawn from food services and cognitive science research, including color, composition and figure–ground relationship attributes is implemented and compared with aesthetic scores for each food image. Findings This study finds that restaurants with different rating levels, cuisine types and chain status have different aesthetic scores. Moreover, 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. Practical implications Restaurant owners may consider performing more proactive social media marketing strategies by posting high-quality food images. Likewise, social media platforms should incentivize their users to share high-quality food images. Originality/value The main contribution of this paper is to provide a novel methodological framework to assess the aesthetics of food images. Instead of relying on a multitude of standard attributes stemming from food photography, this method yields a unique one-take-all score, which is more straightforward to understand and more accessible to correlate with other target variables.authorsversionpublishe

The internet for everything system for the papal basilica and sacred convent of Saint Francis in Assisi, Italy

The purpose of this work is to illustrate the methodology and show the results obtained from the study and the design of the Internet of Everything system for the Papal Basilica and Sacred Convent of Saint Francis in Assisi, Italy, considering all the sub-projects that have already started and the new sub-projects that are going to start