665 citations · 4.5k across the 236 of their papers we have counts for
20 papers · 1 filter
Image Credibility Analysis with Effective Domain Transferred Deep Networks
Zhiwei Jin, Juan Cao, Jiebo Luo +1
Numerous fake images spread on social media today and can severely jeopardize the credibility of online content to public. In this paper, we employ deep networks to learn distinct…
When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment
Honglin Zheng, Tianlang Chen, Jiebo Luo
Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increa…
Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S. Presidential Primaries Using Sparse Learning
Yu Wang, Yang Feng, Xiyang Zhang +1
In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016,…
Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach
Yu Wang, Yang Feng, Xiyang Zhang +1
Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being fr…
Image Based Appraisal of Real Estate Properties
Quanzeng You, Ran Pang, Liangliang Cao +1
Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. T…
When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features
Kuan-Ting Chen, Jiebo Luo
With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the…
Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning
Yu Wang, Haofu Liao, Yang Feng +2
We study to what extend Chinese, Japanese and Korean faces can be classified and which facial attributes offer the most important cues. First, we propose a novel way of obtaining l…
Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data
Yuncheng Li, LiangLiang Cao, Jiang Zhu +1
Composing fashion outfits involves deep understanding of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., Jewelry, Bag, Pants, Dress). In…
Using Social Media to Promote STEM Education: Matching College Students with Role Models
Ling He, Lee Murphy, Jiebo Luo
STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has…
Will Sanders Supporters Jump Ship for Trump? Fine-grained Analysis of Twitter Followers
Yu Wang, Yang Feng, Xiyang Zhang +2
In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three cand…
Tracking Illicit Drug Dealing and Abuse on Instagram using Multimodal Analysis
Xitong Yang, Jiebo Luo
Illicit drug trade via social media sites, especially photo-oriented Instagram, has become a severe problem in recent years. As a result, tracking drug dealing and abuse on Instagr…
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Quanzeng You, Jiebo Luo, Hailin Jin +1
Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of v…
Voting with Feet: Who are Leaving Hillary Clinton and Donald Trump?
Yu Wang, Yuncheng Li, Quanzeng You +3
From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boas…
Understanding Illicit Drug Use Behaviors by Mining Social Media
Yiheng Zhou, Numair Sani, Chia-Kuei Lee +1
Drug use by people is on the rise and is of great interest to public health agencies and law enforcement agencies. As found by the National Survey on Drug Use and Health, 20 millio…
TGIF: A New Dataset and Benchmark on Animated GIF Description
Yuncheng Li, Yale Song, Liangliang Cao +4
With the recent popularity of animated GIFs on social media, there is need for ways to index them with rich metadata. To advance research on animated GIF understanding, we collecte…
To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter
Yu Wang, Jiebo Luo, Richard Niemi +1
In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the…
Image Captioning with Semantic Attention
Quanzeng You, Hailin Jin, Zhaowen Wang +2
Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connect…
Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities
Nabil Hossain, Tianran Hu, Roghayeh Feizi +3
Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those…
Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter
Yu Wang, Jiebo Luo, Richard Niemi +2
In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted m…
Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
Yu Wang, Yuncheng Li, Jiebo Luo
In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016,…