Filters: Keyword is fake news  [Clear All Filters]
Peter Dizikes.  2019.  Want to squelch fake news? Let the readers take charge MIT News.

An MIT study suggests the use of crowdsourcing to devalue false news stories and misinformation online. Despite differences in political opinions, all groups can agree that fake and hyperpartisan sites are untrustworthy.

Casey Newton.  2019.  People older than 65 share the most fake news, a new study finds. The Verge.

This article pertains to cognitive security. Older users shared more fake news than younger ones regardless of education, sex, race, income, or how many links they shared. In fact, age predicted their behavior better than any other characteristic -- including party affiliation.

Tanmoy Chakraborty, Sushil Jajodia, Jonathan Katz, Antonio Picariello, Giancarlo Sperli, V. S. Subrahmanian.  2019.  FORGE: A Fake Online Repository Generation Engine for Cyber Deception. IEEE.

Today, major corporations and government organizations must face the reality that they will be hacked by malicious actors. In this paper, we consider the case of defending enterprises that have been successfully hacked by imposing additional a posteriori costs on the attacker. Our idea is simple: for every real document d, we develop methods to automatically generate a set Fake(d) of fake documents that are very similar to d. The attacker who steals documents must wade through a large number of documents in detail in order to separate the real one from the fakes. Our FORGE system focuses on technical documents (e.g. engineering/design documents) and involves three major innovations. First, we represent the semantic content of documents via multi-layer graphs (MLGs). Second, we propose a novel concept of “meta-centrality” for multi-layer graphs. Our third innovation is to show that the problem of generating the set Fake(d) of fakes can be viewed as an optimization problem. We prove that this problem is NP-complete and then develop efficient heuristics to solve it in practice. We ran detailed experiments with a panel of 20 human subjects and show that FORGE generates highly believable fakes.

[Anonymous].  2019.  Peering under the hood of fake-news detectors. Science Daily.

MIT researchers conducted a study in which they examined automated fake-news detection systems. The study highlights the need for more research into the effectiveness of fake-news detectors.

Mikaela Ashburn.  2019.  Ohio University study states that information literacy must be improved to stop spread of ‘fake news’. Ohio University News.

A study done by researchers at Ohio University calls for the improvement of information literacy as it was found that most people do not take time to verify whether information is accurate or not before sharing it on social media. The study uses information literacy factors and a theoretical lens to help develop an understanding of why people share "fake news" on social media.

Jeff Grabmeier.  2019.  Tech fixes can’t protect us from disinformation campaigns. Ohio State News.

Experts at Ohio State University suggest that policymakers and diplomats further explore the psychological aspects associated with disinformation campaigns in order to stop the spread of false information on social media platforms by countries. More focus needs to be placed on why people fall for "fake news".

[Anonymous].  2019.  Sprawling disinformation networks discovered across Europe ahead of EU elections. Homeland Security News Wire.

A U.K.-based global citizen activist organization, called Avaaz, conducted an investigation, which revealed the spread of disinformation within Europe via Facebook ahead of EU elections. According to Avaaz, these pages were found to be posting false and misleading content. These disinformation networks are considered to be weapons as they are significant in size and complexity.

Ian Bogost.  2019.  Facebook’s Dystopian Definition of ‘Fake’. The Atlantic.

Facebook's response to a altered video of Nancy Pelosi has sparked a debate as to whether social media platforms should take down videos that are considered to be "fake". The definition of "fake" is also discussed.

[Anonymous].  2019.  Can AI help to end fake news? Horizon Magazine.

Artificial intelligence (AI) has been used in the generation of deep fakes. However, researchers have shown that AI can be used to fight misinformation.

Nicole Lee.  2019.  Google’s new curriculum teaches kids how to detect disinformation. Engadget.

The curriculum includes "Don't Fall for Fake" activities that are centered around teaching children critical thinking skills. This is so they'll know the difference between credible and non-credible news sources.

Balouchestani, Arian, Mahdavi, Mojtaba, Hallaj, Yeganeh, Javdani, Delaram.  2019.  SANUB: A new method for Sharing and Analyzing News Using Blockchain. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :139–143.
Millions of news are being exchanged daily among people. With the appearance of the Internet, the way of broadcasting news has changed and become faster, however it caused many problems. For instance, the increase in the speed of broadcasting news leads to an increase in the speed of fake news creation. Fake news can have a huge impression on societies. Additionally, the existence of a central entity, such as news agencies, could lead to fraud in the news broadcasting process, e.g. generating fake news and publishing them for their benefits. Since Blockchain technology provides a reliable decentralized network, it can be used to publish news. In addition, Blockchain with the help of decentralized applications and smart contracts can provide a platform in which fake news can be detected through public participation. In this paper, we proposed a new method for sharing and analyzing news to detect fake news using Blockchain, called SANUB. SANUB provides features such as publishing news anonymously, news evaluation, reporter validation, fake news detection and proof of news ownership. The results of our analysis show that SANUB outperformed the existing methods.
Devin Coldewey.  2019.  To Detect Fake News, This AI First Learned to Write it. Tech Crunch.

Naturally Grover is best at detecting its own fake articles, since in a way the agent knows its own processes. But it can also detect those made by other models, such as OpenAI's GPT2, with high accuracy.

[Anonymous].  2019.  Millions of fake businesses list on Google Maps. WARC News.

Google handles more than 90% of the world's online search queries, generating billions in advertising revenue, yet it has emerged that ad-supported Google Maps includes an estimated 11 million falsely listed businesses on any given day.

[Anonymous].  2019.  What is digital ad fraud and how does it work? Cyware.

Ad fraud is becoming more common among websites. Ad fraud can help fraudsters to generate revenue for themselves through fake traffic, fake clicks and fake installs. It can also help the cybercriminals to deploy malware on users' computers.

[Anonymous].  2018.  Disinformation, 'Fake News' and Influence Campaigns on Twitter. Knight Foundation.

The Knight Foundation performed an analysis on the spread of fake news via Twitter before and after the 2016 U.S. election campaign. Evidence suggests that most accounts used to spread fake or conspiracy news during this time were bots or semi-automated accounts.

Dorje Brody, David Meier.  2018.  Mathematicians to Help Solve the Fake News Voting Conundrum. University of Surrey News.

Mathematicians revealed a mathematical model of fake news. This model can be used to help lawmakers mitigate the impact of fake news.

Filippo Menczer.  2018.  Study: Twitter bots played disproportionate role spreading misinformation during 2016 election. News at IU Bloomington.

Twitter bots played a significant role in the spread of misinformation during the 2016 U.S. presidential election. People often deem messages trustworthy when they appear to be shared by many sources. The research behind this discovery highlights the amplification of misinformation through the use of bots.

Rada Mihalcea.  2018.  Fake news detector algorithm works better than a human. University of Michigan News.

Researchers at the University of Michigan developed an algorithm-based system that can identify fake news stories based on linguistic cues. The system was found to be better at finding fakes than humans.

Aborisade, O., Anwar, M..  2018.  Classification for Authorship of Tweets by Comparing Logistic Regression and Naive Bayes Classifiers. 2018 IEEE International Conference on Information Reuse and Integration (IRI). :269–276.

At a time when all it takes to open a Twitter account is a mobile phone, the act of authenticating information encountered on social media becomes very complex, especially when we lack measures to verify digital identities in the first place. Because the platform supports anonymity, fake news generated by dubious sources have been observed to travel much faster and farther than real news. Hence, we need valid measures to identify authors of misinformation to avert these consequences. Researchers propose different authorship attribution techniques to approach this kind of problem. However, because tweets are made up of only 280 characters, finding a suitable authorship attribution technique is a challenge. This research aims to classify authors of tweets by comparing machine learning methods like logistic regression and naive Bayes. The processes of this application are fetching of tweets, pre-processing, feature extraction, and developing a machine learning model for classification. This paper illustrates the text classification for authorship process using machine learning techniques. In total, there were 46,895 tweets used as both training and testing data, and unique features specific to Twitter were extracted. Several steps were done in the pre-processing phase, including removal of short texts, removal of stop-words and punctuations, tokenizing and stemming of texts as well. This approach transforms the pre-processed data into a set of feature vector in Python. Logistic regression and naive Bayes algorithms were applied to the set of feature vectors for the training and testing of the classifier. The logistic regression based classifier gave the highest accuracy of 91.1% compared to the naive Bayes classifier with 89.8%.

Maria Temming.  2018.  People are bad at spotting fake news. Can computer programs do better? Science News.

This article pertains to cognitive security. To help sort fake news from truth, programmers are building automated systems that judge the veracity of online stories.