Published on Wed Apr 08 2020

Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets

Yue Zhou, Yan Zhang, JingTao Yao

Satirical news detection is an important yet challenging task to prevent the spread of misinformation. Many feature based and end-to-end neural nets based on satirical news detection systems have been proposed and delivered promising results. We apply a game-theoretic rough set model to detect satirical news.

1
1
0
Abstract

Satirical news detection is an important yet challenging task to prevent spread of misinformation. Many feature based and end-to-end neural nets based satirical news detection systems have been proposed and delivered promising results. Existing approaches explore comprehensive word features from satirical news articles, but lack semantic metrics using word vectors for tweet form satirical news. Moreover, the vagueness of satire and news parody determines that a news tweet can hardly be classified with a binary decision, that is, satirical or legitimate. To address these issues, we collect satirical and legitimate news tweets, and propose a semantic feature based approach. Features are extracted by exploring inconsistencies in phrases, entities, and between main and relative clauses. We apply game-theoretic rough set model to detect satirical news, in which probabilistic thresholds are derived by game equilibrium and repetition learning mechanism. Experimental results on the collected dataset show the robustness and improvement of the proposed approach compared with Pawlak rough set model and SVM.

Wed Oct 02 2019
NLP
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues.
0
0
0
Thu May 13 2021
NLP
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles
In this work, we introduce a corpus for satire detection in Romanian news. We gather 55,608 public news articles from multiple real and satirical news sources. We conduct experiments with two state-of-the-art deep neural models.
5
0
0
Mon Sep 04 2017
NLP
Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Not everyone can recognize the satirical cues and therefore believe the news as true news. Existing works only consider document-level features to detect the satire, which could be limited.
0
0
0
Sat Jul 04 2020
NLP
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
Satirical news is regularly shared in modern social media because it is smartly embedded. But it can be harmful to society because it can sometimes be mistaken as factual news. In satirical news, the lexical and pragmatical attributes of the context are the key factors in amusing the readers.
0
0
0
Tue Nov 19 2019
Machine Learning
Automatic Detection of Satire in Bangla Documents: A CNN Approach Based on Hybrid Feature Extraction Model
Research to detect satirical news spread in online news portals as well as social media. Using standard CNN architecture we could detect whether a Bangla text document is satire or not.
0
0
0
Thu Feb 28 2019
NLP
Adversarial Training for Satire Detection: Controlling for Confounding Variables
The automatic detection of satire vs. regular news is relevant for downstream applications. Recent approaches build upon corpora which have been labeled automatically based on article sources. We propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source.
0
0
0