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Fake News Detection

22 papers with code · Natural Language Processing

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Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web

WS 2018 DeFacto/WebCredibility

To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.

FAKE NEWS DETECTION SUBJECTIVITY ANALYSIS WEB CREDIBILITY

Improving Generalizability of Fake News Detection Methods using Propensity Score Matching

28 Jan 2020Arstanley/fakenews_pscore_match

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public.

FAKE NEWS DETECTION

CSI: A Hybrid Deep Model for Fake News Detection

20 Mar 2017soorism/CSI-Code

Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.

FAKE NEWS DETECTION

Stance Detection Benchmark: How Robust Is Your Stance Detection?

6 Jan 2020UKPLab/mdl-stance-robustness

Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.

FAKE NEWS DETECTION MULTI-TASK LEARNING STANCE DETECTION

Localization of Fake News Detection via Multitask Transfer Learning

21 Oct 2019jcblaisecruz02/Tagalog-fake-news

In this paper, we show that Transfer Learning (TL) can be used to train robust fake news classifiers from little data, achieving 91% accuracy on a fake news dataset in the low-resourced Filipino language, reducing the error by 14% compared to established few-shot baselines.

FAKE NEWS DETECTION LANGUAGE MODELLING TRANSFER LEARNING

Learning from Fact-checkers: Analysis and Generation of Fact-checking Language

5 Oct 2019nguyenvo09/LearningFromFactCheckers

In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e. g., snopes. com and politifact. com) and automatic detection systems have been developed in recent years.

FAKE NEWS DETECTION TEXT GENERATION

A Benchmark Study on Machine Learning Methods for Fake News Detection

12 May 2019Tawkat/Fake-News-Detection

The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts.

FAKE NEWS DETECTION

The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News

The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018 2018 nguyenvo09/CombatingFakeNews

To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities.

FAKE NEWS DETECTION RECOMMENDATION SYSTEMS

A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features

23 Oct 2017clickbait-challenge/snapper

The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge.

CLICKBAIT DETECTION FAKE NEWS DETECTION FEATURE SELECTION

Fake News Detection on Social Media using Geometric Deep Learning

10 Feb 2019kc-ml2/ipam-2019-dgl

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

COMMON SENSE REASONING FAKE NEWS DETECTION GRAPH CLASSIFICATION