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Articles 1 - 4 of 4
Full-Text Articles in Science and Technology Studies
Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels
Finding Truth In Fake News: Reverse Plagiarism And Other Models Of Classification, Matthew Przybyla, David Tran, Amber Whelpley, Daniel W. Engels
SMU Data Science Review
As the digital age creates new ways of spreading news, fake stories are propagated to widen audiences. A majority of people obtain both fake and truthful news without knowing which is which. There is not currently a reliable and efficient method to identify “fake news”. Several ways of detecting fake news have been produced, but the various algorithms have low accuracy of detection and the definition of what makes a news item ‘fake’ remains unclear. In this paper, we propose a new method of detecting on of fake news through comparison to other news items on the same topic, as …
Enhancing Trust In The Cryptocurrency Marketplace: A Reputation Scoring Approach, Dan Freeman, Tim Mcwilliams, Sudip Bhattacharyya, Craig Hall, Pablo Peillard
Enhancing Trust In The Cryptocurrency Marketplace: A Reputation Scoring Approach, Dan Freeman, Tim Mcwilliams, Sudip Bhattacharyya, Craig Hall, Pablo Peillard
SMU Data Science Review
Trust is paramount for the effective operation of any monetary system. While the distributed architecture of blockchain technology on which cryptocurrencies operate has many benefits, the anonymity of users on the blockchain has provided criminal users an opportunity to hide both their identities and illicit activities. In this paper, we present a scoring mechanism for cryptocurrency users where the scores represent users’ trustworthiness as safe or risky transactors in the cryptocurrency community. In order to distinguish law-abiding users from potential threats in the Bitcoin marketplace, we analyze historical thefts to profile transactions, classify them into risky and non-risky categories using …
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …
On Identifying Factors Affecting Ethical Practices In Data Science Domains, Yanqin Wang, Earl Shaw, Brian Kruse, Mehdi Ghods
On Identifying Factors Affecting Ethical Practices In Data Science Domains, Yanqin Wang, Earl Shaw, Brian Kruse, Mehdi Ghods
SMU Data Science Review
In data science domains, ethics and ethical approaches are important to minimize adverse effects that may arise in data collection, analysis, and storage. What factors are influential for ethical practices in data science? In this research study, we designed a survey to capture an assessment of ethical concerns and practices from those currently active in the field by soliciting the attitudes/feelings of data science students and practitioners via the questionnaire. We analyzed the extent of their attitudes and identified factors contributing to the difference.