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Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?

2023 | HICSS | Citations: 0
Authors: Rossi, S.; Kwon, Y.; Auglend, O.; Mukkamala, R.; Rossi, M.; Thatcher, J.

Abstract: In recent years, deep learning methods have become increasingly capable of gene ... Expand

Abstract: In recent years, deep learning methods have become increasingly capable of generating near photorealistic pictures and humanlike text up to the point that humans can no longer recognize what is realand what is AI-generated. Concerningly, there is evidence that some of these methods have already been adopted to produce fake social media profiles and content. We hypothesize that these advances have made detecting generated fake social media content in the feed extremely difficult, if not impossible, for the average user of social media. This paper presents the results of an experiment where 375 participants attempted to label real and generated profiles and posts in a simulated social media feed. The results support our hypothesis and suggest that even fully-generated fake profiles with posts written by an advanced text generator are difficult for humans to identify. Collapse

Semantic filters: StyleGAN

Topics: social media Twitter social network armed conflict website

Methods: experiment deep learning survey descriptive statistic stochastic model
Learning Faces to Predict Matching Probability in an Online Matching Platform

2022 | International Conference on Information Systems | Citations: 0
Authors: Kwon, Soonjae; Park, Sunghyuk; Lee, Gene Moo; Lee, Dongwon

Abstract: With the increasing use of online matching platforms, predicting matching proba ... Expand

Abstract: With the increasing use of online matching platforms, predicting matching probability between users is crucial for efficient market design. Although previous studies have constructed various visual features to predict matching probability, facial features, which are important in online matching, have not been widely used. We find that deep learning-enabled facial features can significantly enhance the prediction accuracy of a user's partner preferences from the individual rating prediction analysis in an online dating market. We also build prediction models for each gender and use prior theories to explain different contributing factors of the models. Furthermore, we propose a novel method to visually interpret facial features using the generative adversarial network (GAN). Our work contributes the literature by providing a framework to develop and interpret facial features to investigate underlying mechanisms in online matching markets. Moreover, matching platforms can predict matching probability more accurately for better market design and recommender systems. Collapse

Semantic filters: StyleGAN

Topics: online dating shapley additive explanation online labor market Google+ user behavior

Methods: StyleGAN deep learning generative adversarial network grounded theory artificial neural network

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Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?

Learning Faces to Predict Matching Probability in an Online Matching Platform