The Language of Deceivers

Shafqat, Wafa; Lee, Seunghun; Malik, Sehrish; Kim, Hyunchul

doi:10.1145/2872518.2889356

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…Burfoot and Baldwin (2009) used a support vector machine algorithm (SVM) to automatically classify the content's lexical and semantic features to differentiate between the actual and satire contents. In their works, Ott et al 2011, Shafqat et al (2016), Zhang and Guan (2008), Warkentin et al (2010), Toma and Hancock (2010) tried to do an automatic detection of deceptive content. They explored different domains such as online dating, crowd founding platforms, consumer reviews websites, and online advertising.…”

Section: Related Workmentioning

confidence: 99%

Linguistic-Based Detection of Fake News in Social Media

Mahyoob¹,

Algaraady²,

Alrahaili³

2020

IJEL

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The tremendous growth and impact of fake news as a hot research field gained the public’s attention and threatened their safety in recent years. However, there is a wide range of developed fashions to detect fake contents, either those human-based approaches or machine-based approaches; both have shown inadequacy and limitations, especially those fully automatic approaches. The purpose of this analytic study of media news language is to investigate and identify the linguistic features and their contribution in analyzing data to detect, filter, and differentiate between fake and authentic news texts. This study outlines promising uses of linguistic indicators and adds a rather unconventional outlook to prior literature. It utilizes qualitative and quantitative data analysis as an analytic method to identify systematic nuances between fake and factual news in terms of detecting and comparing 16 attributes under three main linguistic features categories (lexical, grammatical, and syntactic features) assigned manually to news texts. The obtained datasets consist of publicly available right documents on the Politi-fact website and the raw (test) data set collected randomly from news posts on Facebook pages. The results show that linguistic features, especially grammatical features, help determine untrustworthy texts and demonstrate that most of the test news tends to be unreliable articles.

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Section: Related Workmentioning

confidence: 99%

Linguistic-Based Detection of Fake News in Social Media

Mahyoob¹,

Algaraady²,

Alrahaili³

2020

IJEL

View full text Add to dashboard Cite

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“…As the first piece of information that potential guests see, Airbnb hosts utilize their titles as promotional messages to emphasize the strengths of their property and stand out from the competition. The content analysis was performed from the property titles using LIWC, which are widely used language analysis tool in psychology research by calculating values to quantify the linguistic cues (Boyd & Schwartz, 2021; Shafqat et al, 2016; Tausczik & Pennebaker, 2010). LIWC provides the property titles on four different dimensions (scale ranging from zero to 100): Analytical thinking (Are the descriptions written in a formal, logical, or hierarchical way?…”

Section: Methodsmentioning

confidence: 99%

The Influence of Linguistic Styles in Property Titles on Room Rates: A Hedonic Pricing Model

Yoonjoung Heo,

Park

2023

Cornell Hospitality Quarterly

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Consumers are drawn to the inherent quality of a product or service and how it is portrayed and described. This study investigated the impact of language used in property titles on pricing strategies and financial performance in peer-to-peer (P2P) accommodations by adopting the Language Expectancy Theory (LET) as a theoretical framework. The findings of this study demonstrate the significance of linguistic styles in property titles for determining P2P room rates, rental volume, and overall performance. Property titles that exhibit formal, logical, and hierarchical thinking are perceived as more sincere, personalized, and informative, resulting in higher rates, more significant rental volumes, and improved overall performance. On the contrary, properties with titles that convey expertise, and confidence, or adopt a positive and upbeat style tend to put lower room rates and yield lower performance yet generate higher volumes. This study expands the application of LET to P2P communication between an Airbnb host and a potential guest.

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“…Researchers have also shown their interest in automatic detection of deceptive content for the domains such as consumer review websites, online advertising, online dating, etc. [16,21,22].…”

Section: Related Workmentioning

confidence: 99%

Fighting an Infodemic: COVID-19 Fake News Dataset

Patwa

Sharma

Pykl

et al. 2021

Communications in Computer and Information Science

249

117

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Along with COVID-19 pandemic we are also fighting an 'infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We perform a binary classification task (real vs fake) and benchmark the annotated dataset with four machine learning baselines -Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.32% F1-score with SVM on the test set. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection.

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The Language of Deceivers

Cited by 12 publications

References 3 publications

Linguistic-Based Detection of Fake News in Social Media

Linguistic-Based Detection of Fake News in Social Media

The Influence of Linguistic Styles in Property Titles on Room Rates: A Hedonic Pricing Model

Fighting an Infodemic: COVID-19 Fake News Dataset

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