Social and Behavioral Sciences Commons^™

The Relevance Of Shame Across Time And Location, Miranda Vander Berg, Kari Sandouka

SDSU Data Science Symposium

Twitter is used among various entities professionals, politicians, and the general public as an online social network. Many tweets are informational, but others are reactive based on judgment that leads to public shaming. In response to the book “The Shame Machine” (by Cathy O’Neil), we look at Tweets to determine a linguistical and content analysis of shame. The research focuses on content analysis to define if a tweet contains language that is deduced as public shaming. Other factors relating to the tweet are the time, date, location of the author, and if it’s the initial post or a response to …

Session 8: Ensemble Of Score Likelihood Ratios For The Common Source Problem, Federico Veneri, Danica M. Ommen

SDSU Data Science Symposium

Machine learning-based Score Likelihood Ratios have been proposed as an alternative to traditional Likelihood Ratios and Bayes Factor to quantify the value of evidence when contrasting two opposing propositions.

Under the common source problem, the opposing proposition relates to the inferential problem of assessing whether two items come from the same source. Machine learning techniques can be used to construct a (dis)similarity score for complex data when developing a traditional model is infeasible, and density estimation is used to estimate the likelihood of the scores under both propositions.

In practice, the metric and its distribution are developed using pairwise comparisons …

Session 7: Would Ai Stocks Estimate Be As Surprised To Usda Stock Reports As Private Market Analysts?, Asif Mahmud Chowdhury, Matthew Elliott

SDSU Data Science Symposium

Would AI Stocks Estimate Be as Surprised to USDA Stock Reports as Private Market Analysts?

Keywords: Machine Learning, Random Forest, Agricultural Commodities Market, Informational Impact, Efficient Market Hypothesis.

The USDA survey-based Quarterly Grain Stocks reports are the primary source of information regarding the relative supply of U.S. corn, soybeans, and wheat for the last fifty years. Previous research has examined the accuracy of the USDA stock reports and their relevancy to the market, given alternative sources of estimates (e.g., Isengildina-Massa et al., 2021). For example, private industry analysts also estimate expected quarterly grain stock reports before USDA releases their reports. …