1. Authors: S. Volkova, J. JanG, Presenter: Maria Glenski
Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media
Authors: S. Volkova, J. JanG, Presenter: Maria Glenski
Data Sciences and Analytics, National Security Directorate, Pacific Northwest National Laboratory
WWW Track on Journalism, Misinformation and Fact-Checking, April 25th, Lyon, France
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

2. Deceptive News Shared Online
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

3. WWW Track on Journalism, Misinformation and Fact-Checking
Contributions
Recent work:
Psycholinguistic analysis across deception types in the news pages (Rashkin et al., 2017)
Predicting credibility of PolitiFact statements (Rashkin et al., 2017; Wang et al., 2017) and analyzing credibility of tweets (Mitra et al., 2017)
Models to classify deceptive news types on Twitter (Volkova et al., 2017)
Our approach:
Focusing on deception types and deception strategies – misleading and falsification
Verifying model generalizability across domains: news pages, tweets and summary statements
Qualitatively analyze writers’ intent behind misinformation:
psycholinguistic signals
moral foundations
connotations
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

4. Deception Types and Strategies
Disinformation: false facts to deliberately deceive the audience
VS.
Misinformation is conveyed in the honest but mistaken belief that the relayed incorrect facts are true
Propaganda: a form of persuasion to influence audiences via controlled transmission of deceptive, selectively omitting, and one-sided messages
Hoax: type of misinformation that aims to deliberately deceive the reader
Misleading: topic changes, irrelevant information, and equivocations
Falsification: with contradictions or distortions
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

5. WWW Track on Journalism, Misinformation and Fact-Checking
Task Definition
Build generalizable predictive models to differentiate between deception types and strategies in news across domains
Deception strategies: misleading vs. falsification
Deception types: propaganda vs. hoax vs. disinformation
More Intent to Deceive
Less Intent to Deceive
Falsification
Misleading
Over time, connections between accounts within similarity network appear and disappear
Amount of similarity between accounts is also temporally dynamic
More Intent to Deceive
Less Intent to Deceive
Disinformation
Propaganda
Hoax
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

6. Datasets: Deception Strategies
Domains
Misleading
Falsification
Summaries
616
1,376
News Pages 81 85
Tweets 96
109
Confirmed cases of disinformation summaries from the European Union’s East Strategic Communications Task Force:
Falsification: unprovable, no evidence, no proof, no supporting evidence,
Crowdsourcing: pairwise inter-annotator agreement kappa is 0.64 (5 annotators)
Followed URLs in disinformation summaries to collect the original news pages
Queried Twitter public API using SVO and timestamps to extract unique disinfo tweets
Parsed summaries, news pages and tweets using SyntaxNet to extract SVO tuples
Understand agents and themes of deception
Contrast connotations/perspectives across deception types
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

7. Datasets: Deception Types
Domains
Propaganda
Hoaxes
Disinformation
News Pages
17,872
5,297
166
Tweets
3,834
453
205
Collecting propaganda and hoax news pages and tweets:
Downloaded 17,872 propaganda (ActivistPost), 5,297 hoax (DCGazette) news pages
Collected the corresponding propaganda and hoax tweets using public Twitter API
Collecting disinformation news pages and tweets:
Followed URLs in disinformation summaries to collect the original news pages
Queried Twitter public API using SVO and timestamps to extract disinformation tweets
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

8. Predictive Models and Signals
Machine learning models: MaxEntropy and RandomForest1
Neural network models: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)2
Predictive signals:
Content: TFIDF, dimensionality reduction, GloVe embeddings3
Style, syntax, complexity and readability: Automated Readability Index (ARI), Flesch-Kincaid readability tests, Coleman-Liau index4
Biased language: intensifiers, dramatic adverbs, assertive, imperative, report verbs
Moral foundations: care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, purity and degradation
Psycholinguistic signals: imperative commands, personal pronouns, emotional language, quotations, and inclusions
What is being discussed
How the
content is being discussed
Lexicons
How emotional, subjective the discussion is
1https://nlp.stanford.edu/projects/glove/
2http://scikit-learn.org/stable/
3https://keras.io/
4https://github.com/nltk/nltk_contrib/tree/master/nltk_contrib/readability
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

9. WWW Track on Journalism, Misinformation and Fact-Checking
Deception Strategy Classification Results: Misleading vs. Falsification
The best models are LSTM and MaxEntropy
Falsification strategy is easier to identify than misleading strategy
Deceptive strategies are easier to predict in tweets than in summaries and news
Predictive signals: connotations (summaries), moral foundations, biased language and psycholinguistic cues (news pages), syntax and connotations (tweets)
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

10. WWW Track on Journalism, Misinformation and Fact-Checking
Deception Type Classification Results: Propaganda vs. Disinformation vs. Hoax
The best performing model is LSTM
Disinformation is easier to predict than propaganda or hoaxes
Deceptive news types – disinformation, propaganda, and hoaxes, unlike deceptive strategies, are more salient, and easier to identify in tweets than in news pages
Predictive signals: content (summaries and tweets)
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

11. Connotation Analysis: Background
Identify writers’ intent behind digital misinformation by analyzing psycholinguistic signals – moral foundations and connotations extracted from different types of deceptive news
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

12. Connotation Analysis Results: Disinformation
Writer → agent
Writer → theme
Implications:
Quantitatively demonstrate how agents and themes of strategic deception vary across deception types
Qualitatively identify the hidden agenda of content
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

13. Linguistic Realizations of Deception
Misleading vs. Falsification
Significant differences in subjective language and moral foundations
Misleading statements are more subjective than falsified statements in summaries and news pages but not tweets
Falsified compared to misleading statements include more:
Harm+ and Ingroup+ signals in tweets
Affect terms in tweets
Tweets
Implications:
Build models for factuality assessment without external knowledge
Improve fact-checking systems by going beyond fake news classification
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

14. Summary and Future Work
Predictive signals:
Content + moral foundations and connotations are more predictive of deception strategies than style and syntax
Content is the most predictive of deception types
Predictive models:
LSTMs achieve higher performance compared to ML models
Deception types:
Disinformation is less difficult to predict compared to hoaxes and propaganda
Deception strategies:
Falsification strategy is easier to infer than misleading strategy
Content is the most predictive of misleading strategy across all domains
How emotional and subjective the discussion is
What is being discussed
Domains:
Deception types, unlike deception strategies, are easier to identify in tweets than in news pages
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

15. WWW Track on Journalism, Misinformation and Fact-Checking
Future Work
Multilingual, multimodal (text and images) deception classification
Misinformation propagation and influence (deception types, languages)
Reactions to deceptive news across platforms: Reddit and Twitter
References:
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. S. Volkova, K. Shaffer, J. Jang and N. Hodas. ACL 2017.
Truth of Varying Shades: On Political Fact-Checking and Fake News. H. Rashkin, E. Choi, J. Jang, Y. Choi, and S. Volkova. EMNLP 2017.
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models. M. Glenski, E. Ayton, D. Arendt and S. Volkova. Proceedings of Google Clickbait Workshop
Domains:
Deception types, unlike deception strategies, are easier to identify in tweets than in news pages
WWW Track on Journalism, Misinformation and Fact-Checking
November 19, 2018

16. Svitlana Volkova Senior Research Scientist
Data Sciences and Analytics Group
Computational and Statistical Analytics Division