Automatic deception detection: Methods for finding fake news

Conroy, Niall J.; Rubin, Victoria L.; Chen, Yimin

doi:10.1002/pra2.2015.145052010082

Cited by 677 publications

(332 citation statements)

References 15 publications

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“…Conroy et al used linguistic approach (Both words and syntax) as well as network approach which says that message metadata or structured knowledge network queries can be harnessed to provide aggregate deception measures. They used Centering Resonance Analysis(CRA), a mode of networkbased text analysis [3]. Gupta et al presented a path breaking work by characterizing and identifying fake images on Twitter during Hurricane Sandy.…”

Section: IImentioning

confidence: 99%

A Survey on Identification of Fake News

Gupta¹

2018

IJRASET

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Fake News is a major problem faced by many agencies and organisations which may seem like a corporate issue but is a threat to the very idea of democracy and every citizen's right to information. It is one of the major false practice issue growing at an enormous rate on online social media platforms disrupting the journalistic practices of spreading the truth to the people unaware of the background of news. Stanford University, in one of its recent studies, has provided a definition of fake news, "We define fake news to be news articles that are intentionally and verifiably false, and could mislead readers". News sensationalism, falsification, and exaggeration are the first steps in spreading these pieces of propaganda. It is one of the biggest problems for news organisations which are trying to report truth to the average citizen but are slowed down in the process due to some culprits for their own gains and compromising all the journalistic effort put in by all the people behind the news cycle. Machine Learning and Data Science are contemporary fields of computer science which are able handle complex problems and are tackling those problems head on. Ours is an effort to use these vast technologies to solve the problem of fake news on social platforms like Twitter.

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Section: IImentioning

confidence: 99%

A Survey on Identification of Fake News

Gupta¹

2018

IJRASET

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“…Rubin, Chen, and Conroy (2015) identified three types of fake news in their work -serious fabrications, large-scale hoaxes, and humorous fake news. Conroy, Rubin, and Chen (2015) were one of the first researchers to use network analysis in fake news detection while Mukherjee and colleagues (2013) used words and the respective part-of-speech tags, together with bigrams to achieve a 68.1% accuracy on Yelp review classification. Shu and colleagues (2017) provided a detailed overview of the recent approaches towards fake news detection and similar problems.…”

Section: Introductionmentioning

confidence: 99%

Towards automatic fake news classification

Ghosh

Shah

2018

Proc. Assoc. Info. Sci. Tech.

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The interaction of technology with humans has many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Many research studies have been conducted to tackle the detection and spreading of fake news, which is misinformation that looks genuine. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We propose a generalized method based on Deep Neural Networks to detect if a given claim is fake or genuine. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main submodules. The first submodule uses the claim to retrieve relevant articles from the knowledge base which can then be used to verify the truth of the claim. It also uses word‐level features for prediction. The second submodule uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on benchmark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives.

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“…Aforementioned hoax analysis can be categorized into two major approaches: linguistic and network. Here, we discuss hoax within online news text that put more weights on the linguistic aspect [11]. Linguistic approaches concern with texts as a bag of equally significant words, syntax structure like noun and verb phrases within the texts, and semantic analysis to recognize any contradictions on other texts with the similar topics of allegedly hoax texts.…”

Section: Introductionmentioning

confidence: 99%

Keynote Speaker II

Purnomo

Sumpeno

Setiawan

et al. 2017

Procedia Computer Science

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Biomedical engineering research trend can be healthcare models with unobtrusive smart systems for monitoring vital signs and physical activity. Detecting infant facial cry because of inability to communicate pain, recognizing facial emotion to understand dysfunction mechanisms through micro expression or transform captured human expression with motion device into three-dimensional objects are some of the applied systems. Nowadays, collaborated with biomedical research, mining and analyzing social network can improve public and private health care sectors as well such as research health news shared on social media about pharmaceutical drugs, pandemics, or viral outbreaks. Due to the vast amount of shared news, there is an urgency to select and filter information to prevent the spread of hoax or fake news. We explored in depth some steps to classify hoaxes written as news articles. This discussion also encourages on how technologies of social network analysis could be used to make new kinds improvement in health care sectors. Then close with a description of limitless future possibilities of biomedical engineering research in social media. AbstractBiomedical engineering research trend can be healthcare models with unobtrusive smart systems for monitoring vital signs and physical activity. Detecting infant facial cry because of inability to communicate pain, recognizing facial emotion to understand dysfunction mechanisms through micro expression or transform captured human expression with motion device into three-dimensional objects are some of the applied systems. Nowadays, collaborated with biomedical research, mining and analyzing social network can improve public and private health care sectors as well such as research health news shared on social media about pharmaceutical drugs, pandemics, or viral outbreaks. Due to the vast amount of shared news, there is an urgency to select and filter information to prevent the spread of hoax or fake news. We explored in depth some steps to classify hoaxes written as news articles. This discussion also encourages on how technologies of social network analysis could be used to make new kinds improvement in health care sectors. Then close with a description of limitless future possibilities of biomedical engineering research in social media.

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Automatic deception detection: Methods for finding fake news

Cited by 677 publications

References 15 publications

A Survey on Identification of Fake News

A Survey on Identification of Fake News

Towards automatic fake news classification

Keynote Speaker II

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