Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

CLOUD-BASED MACHINE LEARNING AND SENTIMENT ANALYSIS

The role of a Data Scientist is becoming increasingly ubiquitous as companies and institutions see the need to gain additional insights and information from data to make better decisions to improve the quality-of-service delivery to customers. This thesis document contains three aspects of data science projects aimed at improving tools and techniques used in analyzing and evaluating data. The first research study involved the use of a standard cybersecurity dataset and cloud-based auto-machine learning algorithms were applied to detect vulnerabilities in the network traffic data. The performance of the algorithms was measured and compared using standard evaluation metrics. The second research study involved the use of text-mining social media, specifically Reddit. We mined up to 100,000 comments in multiple subreddits and tested for hate speech via a custom designed version of the Python Vader sentiment analysis package. Our work integrated standard sentiment analysis with Hatebase.org and we demonstrate our new method can better detect hate speech in social media. Following sentiment analysis and hate speech detection, in the third research project, we applied statistical techniques in evaluating the significant difference in text analytics, specifically the sentiment-categories for both lexicon-based software and cloud-based tools. We compared the three big cloud providers, AWS, Azure, and GCP with the standard python Vader sentiment analysis library. We utilized statistical analysis to determine a significant difference between the cloud platforms utilized as well as Vader and demonstrated that each platform is unique in its analysis scoring mechanism