FauxBuster: A Content-free Fauxtography Detector Using Social Media Comments

et al. 2020

Soc. Netw. Anal. Min.

Self Cite

Online social media has been a popular source for people to consume and share news content. More recently, the spread of misinformation online has caused widespread concerns. In this work, we focus on a critical task of detecting fauxtography on social media where the image and associated text together convey misleading information. Many efforts have been made to mitigate misinformation online, but we found that the fauxtography problem has not been fully addressed by existing work. Solutions focusing on detecting fake images or misinformed texts alone on social media often fail to identify the misinformation delivered together by the image and the associated text of a fauxtography post. In this paper, we develop FauxWard, a novel graph convolutional neural network framework that explicitly explores the complex information extracted from a user comment network of a social media post to effectively identify fauxtography. FauxWard is content-free in the sense that it does not analyze the visual or textual contents of the post itself, which makes it robust against sophisticated fauxtography uploaders who intentionally craft image-centric posts by editing either the text or image content. We evaluate FauxWard on two real-world datasets collected from mainstream social media platforms (i.e., Reddit and Twitter). The results show that FauxWard is both effective and efficient in identifying fauxtography posts on social media.

Section: Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

FauxWard: a graph neural network approach to fauxtography detection using social media comments

Shang

et al. 2020

Soc. Netw. Anal. Min.

Self Cite

“…The second category is commonly referred to as "image forgery detection" schemes. For example, Zhang et al developed a novel fauxtography detector that can effectively track down misleading images on social media (e.g., Twitter, Reddit) [28]. Huynh-Kha et al developed an image forgery detection scheme that can detect whether an image is manually edited by copy-move, splicing or both in the same image [29].…”

Section: Misinformation Detectionmentioning

confidence: 99%

Towards reliable online clickbait video detection: A content-agnostic approach

Shang

Knowledge-Based Systems

Wang

et al. 2019

Self Cite

Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerged as a critical problem on online video sharing platforms. Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos. In this paper, we develop a novel content-agnostic scheme, Online Video Clickbait Protector (OVCP), to effectively detect clickbait videos by exploring the comments from the audience who watched the video. Different from existing solutions, OVCP does not directly analyze the content of the video and its pre-click information (e.g., title and thumbnail). Therefore, it is robust against sophisticated content creators who often generate clickbait videos that can bypass the current clickbait detectors. We evaluate OVCP with a realworld dataset collected from YouTube. Experimental results demonstrate that OVCP is effective in identifying clickbait videos and significantly outperforms both state-of-the-art baseline models and human annotators.

“…To address these challenges, we map the meta-path collections of both images and poems into a homogeneous latent feature subspace with much lower dimensions. In particular, we embed each meta-path using the auto-encoding technique [29]. It consists of an encoder that maps an input vector X into a latent subspace Z and a decoder that uses the latent representation Z to recover the original input.…”

Section: Semantic Enriched Meta Path Ranking (Sempr) Modulementioning

confidence: 99%

Through the eyes of a poet

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Zhi

et al. 2019

Self Cite

With the increasing popularity of portable devices with cameras (e.g., smartphones and tablets) and ubiquitous Internet connectivity, travelers can share their instant experience during the travel by posting photos they took to social media platforms. In this paper, we present a new image-driven poetry recommender system that takes a traveler's photo as input and recommends classical poems that can enrich the photo with aesthetically pleasing quotes from the poems. Three critical challenges exist to solve this new problem: i) how to extract the implicit artistic conception embedded in both poems and images? ii) How to identify the salient objects in the image without knowing the creator's intent? iii) How to accommodate the diverse user perceptions of the image and make a diversified poetry recommendation? The proposed iPoemRec system jointly addresses the above challenges by developing heterogeneous information network and neural embedding techniques. Evaluation results from real-world datasets and a user study demonstrate that our system can recommend highly relevant classical poems for a given photo and receive significantly higher user ratings compared to the state-of-the-art baselines.