FakeYou! -- A Gamified Approach for Building and Evaluating Resilience
Against Fake News
- URL: http://arxiv.org/abs/2003.07595v1
- Date: Tue, 17 Mar 2020 09:24:22 GMT
- Title: FakeYou! -- A Gamified Approach for Building and Evaluating Resilience
Against Fake News
- Authors: Lena Clever, Dennis Assenmacher, Kilian M\"uller, Moritz Vinzent
Seiler, Dennis M. Riehle, Mike Preuss, Christian Grimme
- Abstract summary: This work focuses on an a gamified approach to strengthen the resilience of consumers towards fake news.
The game FakeYou motivates its players to critically analyze headlines regarding their trustworthiness.
We introduce the game itself, as well as the underlying technical infrastructure.
- Score: 0.8620335948752805
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays fake news are heavily discussed in public and political debates.
Even though the phenomenon of intended false information is rather old,
misinformation reaches a new level with the rise of the internet and
participatory platforms. Due to Facebook and Co., purposeful false information
- often called fake news - can be easily spread by everyone. Because of a high
data volatility and variety in content types (text, images,...) debunking of
fake news is a complex challenge. This is especially true for automated
approaches, which are prone to fail validating the veracity of the information.
This work focuses on an a gamified approach to strengthen the resilience of
consumers towards fake news. The game FakeYou motivates its players to
critically analyze headlines regarding their trustworthiness. Further, the game
follows a "learning by doing strategy": by generating own fake headlines, users
should experience the concepts of convincing fake headline formulations. We
introduce the game itself, as well as the underlying technical infrastructure.
A first evaluation study shows, that users tend to use specific stylistic
devices to generate fake news. Further, the results indicate, that creating
good fakes and identifying correct headlines are challenging and hard to learn.
Related papers
- Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news.
Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
arXiv Detail & Related papers (2023-11-02T08:39:45Z) - Fake News Detection and Behavioral Analysis: Case of COVID-19 [0.22940141855172028]
"Infodemic" due to spread of fake news regarding the pandemic has been a global issue.
Readers could mistake fake news for real news, and consequently have less access to authentic information.
It is challenging to accurately identify fake news data in social media posts.
arXiv Detail & Related papers (2023-05-25T13:42:08Z) - Nothing Stands Alone: Relational Fake News Detection with Hypergraph
Neural Networks [49.29141811578359]
We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism.
Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
arXiv Detail & Related papers (2022-12-24T00:19:32Z) - FALSE: Fake News Automatic and Lightweight Solution [0.20999222360659603]
In this paper, R code have been used to study and visualize a modern fake news dataset.
We use clustering, classification, correlation and various plots to analyze and present the data.
arXiv Detail & Related papers (2022-08-16T11:53:30Z) - Faking Fake News for Real Fake News Detection: Propaganda-loaded
Training Data Generation [105.20743048379387]
We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda.
Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles.
Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
arXiv Detail & Related papers (2022-03-10T14:24:19Z) - Profiling Fake News Spreaders on Social Media through Psychological and
Motivational Factors [26.942545715296983]
We study the characteristics and motivational factors of fake news spreaders on social media.
We then perform a series of experiments to determine if fake news spreaders can be found to exhibit different characteristics than other users.
arXiv Detail & Related papers (2021-08-24T20:27:38Z) - Stance Detection with BERT Embeddings for Credibility Analysis of
Information on Social Media [1.7616042687330642]
We propose a model for detecting fake news using stance as one of the features along with the content of the article.
Our work interprets the content with automatic feature extraction and the relevance of the text pieces.
The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.
arXiv Detail & Related papers (2021-05-21T10:46:43Z) - User Preference-aware Fake News Detection [61.86175081368782]
Existing fake news detection algorithms focus on mining news content for deceptive signals.
We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
arXiv Detail & Related papers (2021-04-25T21:19:24Z) - How does Truth Evolve into Fake News? An Empirical Study of Fake News
Evolution [55.27685924751459]
We present the Fake News Evolution dataset: a new dataset tracking the fake news evolution process.
Our dataset is composed of 950 paired data, each of which consists of articles representing the truth, the fake news, and the evolved fake news.
We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.
arXiv Detail & Related papers (2021-03-10T09:01:34Z) - Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news.
In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities.
We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z) - Where Are the Facts? Searching for Fact-checked Information to Alleviate
the Spread of Fake News [9.68145635795782]
We propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users.
The search can directly warn fake news posters and online users about misinformation, discourage them from spreading fake news, and scale up verified content on social media.
Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets.
arXiv Detail & Related papers (2020-10-07T04:55:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.