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
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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