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Title: Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media

Abstract

Deceptive information in online news and social media has had dramatic effect on our society in recent years. This study is the first to gain deeper insights into writers' intent behind digital misinformation by analyzing psycholinguistic signals: moral foundations and connotations extracted from different types of deceptive news ranging from strategic disinformation to propaganda and hoaxes. To ensure consistency of our findings and generalizability across domains, we experiment with data from: (1) confirmed cases of disinformation in news summaries, (2) {propaganda}, hoax, and disinformation news pages, and (3) social media news. We first contrast lexical markers of biased language, syntactic and stylistic signals, and connotations across deceptive news types including disinformation, propaganda, and hoaxes, and {deceptive} strategies including misleading or falsification. We then incorporate these insights to build machine learning and deep learning predictive models to infer deception strategies and deceptive news types. Our experimental results demonstrate that unlike earlier work on deception detection, content combined with biased language markers, moral foundations, and connotations leads to better predictive performance of deception strategies compared to syntactic and stylistic signals (as reported in earlier work on deceptive reviews). Falsification strategy is easier to identify than misleading strategy. Disinformation is more difficult tomore » predict than to propaganda or hoaxes. Deceptive news types (disinformation, propaganda, and hoaxes), unlike deceptive strategies (falsification and misleading), are more salient, and thus easier to identify in tweets than in news reports. Finally, our novel connotation analysis across deception types provides deeper understanding of writers' perspectives and therefore reveals the intentions behind digital misinformation.« less

Authors:
;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1435892
Report Number(s):
PNNL-SA-132310
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: The World Wide Web Conference (WWW 2018): Journalism, Misinformation and Fact Checking, April 23-27, 2018, Lyon, France
Country of Publication:
United States
Language:
English
Subject:
natural language processing; machine learning; disinformation; misinformation; deception; social media analysis

Citation Formats

Volkova, Svitlana, and Jang, Jin Yea. Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media. United States: N. p., 2018. Web.
Volkova, Svitlana, & Jang, Jin Yea. Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media. United States.
Volkova, Svitlana, and Jang, Jin Yea. Fri . "Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media". United States. doi:.
@article{osti_1435892,
title = {Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media},
author = {Volkova, Svitlana and Jang, Jin Yea},
abstractNote = {Deceptive information in online news and social media has had dramatic effect on our society in recent years. This study is the first to gain deeper insights into writers' intent behind digital misinformation by analyzing psycholinguistic signals: moral foundations and connotations extracted from different types of deceptive news ranging from strategic disinformation to propaganda and hoaxes. To ensure consistency of our findings and generalizability across domains, we experiment with data from: (1) confirmed cases of disinformation in news summaries, (2) {propaganda}, hoax, and disinformation news pages, and (3) social media news. We first contrast lexical markers of biased language, syntactic and stylistic signals, and connotations across deceptive news types including disinformation, propaganda, and hoaxes, and {deceptive} strategies including misleading or falsification. We then incorporate these insights to build machine learning and deep learning predictive models to infer deception strategies and deceptive news types. Our experimental results demonstrate that unlike earlier work on deception detection, content combined with biased language markers, moral foundations, and connotations leads to better predictive performance of deception strategies compared to syntactic and stylistic signals (as reported in earlier work on deceptive reviews). Falsification strategy is easier to identify than misleading strategy. Disinformation is more difficult to predict than to propaganda or hoaxes. Deceptive news types (disinformation, propaganda, and hoaxes), unlike deceptive strategies (falsification and misleading), are more salient, and thus easier to identify in tweets than in news reports. Finally, our novel connotation analysis across deception types provides deeper understanding of writers' perspectives and therefore reveals the intentions behind digital misinformation.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 27 00:00:00 EDT 2018},
month = {Fri Apr 27 00:00:00 EDT 2018}
}

Conference:
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