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Deceptive News Prediction Clickbait Score Inference

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1 Deceptive News Prediction Clickbait Score Inference
Multimodal Prediction of Suspicious News Types on Twitter Ellyn Ayton1, Maria Glenski2, Dustin Arendt3 and Svitlana Volkova4 1Western Washington University, 2University of Notre Dame 3Visual Analytics, 4Data Sciences and Analytics, National Security Directorate NOTE: Print this poster file at 100% SCALE to result in a physical print measuring 48” wide x 36” tall. All type size notations shown above are based on the final printed size of the poster. • Contact Digital Duplicating ( , to order poster printing and finishing services for your completed poster design. • Remember to have your poster cleared for public display/distribution through the ERICA Information Release system ( • Sidebar “About PNNL” box is considered optional, and can be removed if space is needed for technical content. Deceptive News Prediction Clickbait Score Inference Motivation Research Questions Clickbait content in social networks exploit and deceive to achieve a hidden purpose: Can we automatically detect clickbait score of a Twitter post? Opinion manipulation Which social media signals (tweet or article content, images, linguistic cues) are most predictive of a click baiting post? Web traffic attraction Attention redirection Motivation Research Questions 62% of American adults get news from social media Can we improve accuracy of existing text models by including images? 64% of U.S. adults said that “made-up news” has caused a “great deal of confusion” about the facts of current events5 Can we measure what social media signals (tweet content, linguistic cues, or images) yield better prediction performance? Develop neural network models that learn jointly from image and text representations of retweets from news accounts to classify posts as deceptive (propaganda, clickbait, disinformation) vs. verified. Develop and evaluate the predictive power of neural network models that rely on image and text representations extracted from tweets to predict the clickbait score for a given tweet from 0 to 1. Intent to Deceive No Intent to Deceive Suspicious (Propaganda + Disinformation + Clickbait) Verified Not Click baiting Slightly Considerably Heavily Click baiting 1 Dataset #posts # Clickbait: # Not 2k Labeled 2,495 1:2.23 20k Labeled 19,538 1:3.10 Unlabeled 80,012 ?:? Twitter News Data Twitter Clickbait Data News Types Tweets Sampled Propaganda deliberately spreads misinformation 111,997 29,797 Disinformation is false information spread to deceive 283,350 5,312 Clickbait take bits of true stories but insinuate details 3,400 1,124 Verified sources: mainstream news media 104,034 102,634 Clickbait challenge: Model Clickbait Score Dense Layer (100 units) Linguistic Cues (+ Image Features) . . . (100 units) Convolutional Layer (100 units) Embedding Layer (200 units) Input Word Sequences Activation Layer (sigmoid) Tensor Concatenation Image Representations ImageOnly2K: 2,048-dim representation from images ResNet architecture (He at al., 2016)3 ImageNet weights (Deng et al., 2013)4 Image tags using TensorFlow Object Detection API1 Model Designed three model architectures: Single input Two inputs with one merge layer Three inputs with two merge layers Initialized embeddings using GloVe2 Trained LSTMs with dropout and hidden dimension of 200 Optimized for binary cross entropy using Adam Deceptive News Verified News Linguistic Cues Moral Foundation Theory: Harm, Care, Loyalty, Betrayal, Authority dimensions Assertive, Hedging, Implicative, Report Verbs Subjective (sentiment) and Biased Language Clickbait Image Analysis Image tags are extracted using TensorFlow 10,270 (47%) clickbait posts out of 22,033 images 2,057 clickbait images, 7,431 non-clickbait images File Name // File Date // PNNL-SA-##### 1TensowFlow Object Detection API: GloVe Embeddings: Resnet: He, Kaiming, et al. "Deep residual learning for image recognition. CVPR ImageNet: Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database. CVPR 2009. 5Volkova et al., Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. ACL Glenski et al., Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models. Clickbait Challenge This research was conducted under the High-Performance Analytics Program and the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy


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