A Stylometric Inquiry into Hyperpartisan and Fake News

@inproceedings{Potthast2018ASI,
  title={A Stylometric Inquiry into Hyperpartisan and Fake News},
  author={Martin Potthast and Johannes Kiesel and K. Reinartz and Janek Bevendorff and Benno Stein},
  booktitle={ACL},
  year={2018}
}
We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299 fake news articles identified are also hyperpartisan. We show how a style analysis can distinguish hyperpartisan news from the mainstream (F1 = 0.78), and satire from both (F1… Expand
Inroduction on Recent Trends and Perspectives in Fake News Research
  • 2021
Fake news, especially on social media, is now viewed as one of the main digital threats to democracy, journalism, and freedom of expression [1, 4, 11, 15]. Our economies are not immune to the spreadExpand
Fake News: A Survey of Research, Detection Methods, and Opportunities
TLDR
This survey comprehensively and systematically reviews fake news research and identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news. Expand
Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors
The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of methods developed toExpand
Detecting Fake News Spreaders on Twitter from a Multilingual Perspective
  • Inna Vogel, M. Meghana
  • Computer Science
  • 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)
  • 2020
TLDR
This paper proposes an approach that is able to identify possible fake news spreaders on social media as a first step towards preventing fake news from being propagated among online users and results indicate that language-independent features can be used to distinguish between possible fake News spreaders and users who share credible information. Expand
Understanding Fake News Consumption: A Review
Combating the spread of fake news remains a difficult problem. For this reason, it is increasingly urgent to understand the phenomenon of fake news. This review aims to see why fake news is widelyExpand
Can We Spot the "Fake News" Before It Was Even Written?
TLDR
Media profiles are developed that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame ofReporting, and stance with respect to various claims and topics in the Tanbih news aggregator. Expand
On the Coherence of Fake News Articles
TLDR
While the relative coherence shortfall of fake news articles as compared to legitimate ones form the main observation from this study, several aspects of the differences are analyzed and outline potential avenues of further inquiry. Expand
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
TLDR
This work proposes a multi-task ordinal regression framework that models the two problems of trustworthiness estimation and political ideology detection of entire news outlets, as opposed to evaluating individual articles, and shows sizable performance gains by the joint models over models that target the problems in isolation. Expand
Capturing the Style of Fake News
TLDR
This study gathers a corpus of 103,219 documents from 223 online sources labelled by media experts, devise realistic evaluation scenarios and design two new classifiers: a neural network and a model based on stylometric features. Expand
Fake News, Conspiracies and Myth Debunking in Social Media - A Literature Survey Across Disciplines
Since the 2016 U.S. presidential election, the problem of fake news on social media gained renewed public attention. Consequently, a huge amount of research literature concerning this matter isExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 41 REFERENCES
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News
TLDR
Overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real, leading to the conclusion that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims. Expand
Collective attention in the age of (mis)information
TLDR
This work studies how Facebook users consumed different information at the edge of political discussion and news during the last Italian electoral competition and reveals that users which are prominently interacting with conspiracists information sources are more prone to interact with intentional false claims. Expand
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
TLDR
Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text. Expand
Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News
Satire is an attractive subject in deception detection research: it is a type of deception that intentionally incorporates cues revealing its own deceptiveness. Whereas other types of fabricationsExpand
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
TLDR
This work builds predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news – satire, hoaxes, clickbait and propaganda, and shows that neural network models trained on tweet content and social network interactions outperform lexical models. Expand
Towards News Verification: Deception Detection Methods for News Discourse
News verification is a process of determining whether a particular news report is truthful or deceptive. Deliberately deceptive (fabricated) news creates false conclusions in the readers’ minds.Expand
From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles
TLDR
This work wants to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. Expand
Detecting Hoaxes, Frauds, and Deception in Writing Style Online
TLDR
It is shown that using a large feature set, it is possible to distinguish regular documents from deceptive documents with 96.6% accuracy (F-measure) and an analysis of linguistic features that can be modified to hide writing style is presented. Expand
Prominent Features of Rumor Propagation in Online Social Media
TLDR
A new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time, and key structural and linguistic differences in the spread of rumors and non-rumors are identified. Expand
Fake News Detection Through Multi-Perspective Speaker Profiles
TLDR
A novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection outperforms the state-of-the-art method by 14.5% and proves that speaker profiles provide valuable information to validate the credibility of news articles. Expand
...
1
2
3
4
5
...