Fake news videos are being actively produced and uploaded on YouTube to attract public attention. In this paper,we propose a topic-agnostic fake news video detection model based on adversarial learning and topic modeling. The proposed model estimates the topic distribution of a video using its title/description and comments by topic modeling and tries to identify the differences in stance by the topic distribution difference between title/description and comments. Then, it constructs an adversarial neural network to extract topic-agnostic features effectively. The proposed model can effectively detect topic changes for stance analysis and easily shift among various topics. In this study, it achieves an F1-score 2.68% point greater than previous models in fake news video detection.

Query reformulation (QR) is a key factor in overcoming the problems faced by the lexical chasm in information retrieval (IR) systems. In particular, when searching for jargon, people tend to use descriptive queries, such as "a medical examination of the colon" rather than "colonoscopy," or they often use them interchangeably. Thus, transforming users' descriptive queries into appropriate jargon queries helps to retrieve more relevant documents. In this paper, we propose a new graph-based QR system that uses a dictionary, where the model does not require human-labeled data. Given a descriptive query, our system predicts the corresponding jargon word over a graph consisting of pairs of a headword and its description in the dictionary. First, we train a graph neural network to represent the relational properties between words and to infer a jargon word using compositional information of the descriptive query's words. Moreover, we propose a graph search model that finds the target node in real time using the relevance scores of neighborhood nodes. By adding this fast graph search model to the front of the proposed system, we reduce the reformulating time significantly. Experimental results on two datasets show that the proposed method can effectively reformulate descriptive queries to corresponding jargon words as well as improve retrieval performance under several search frameworks.

We present VISE, or Vehicle Image Search Engine, to support the fast search of similar vehicles from low-resolution traffic camera images. VISE can be used to trace and locate vehicles for applications such as police investigations when high-resolution footage is not available. Our system consists of three components: an interactive user-interface for querying and browsing identified vehicles; a scalable search engine for fast similarity search on millions of visual objects; and an image processing pipeline that extracts feature vectors of objects from video frames. We use transfer learning technique to integrate state-of-the-art Convolutional Neural Networks with two different refinement methods to achieve high retrieval accuracy. We also use an efficient high-dimensional nearest neighbor search index to enable fast retrieval speed. In the demo, our system will offer users an interactive experience exploring a large database of traffic camera images that is growing in real time at 200K frames per day.