Spam Spotting – Using AI Tools to Educate and Improve Online Decision- Making

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Faculty Mentor/s: Sihong Xie, Assistant Professor, Computer Science & Engineering;
Qiong Fu, College of Education
Summer 2020 Students:
Chen Rui - GRAD – Special Education
Jennifer Liu – 2023 – UG - Computer Science
Diana Garcia – 2021 – UG - Psychology
Jack Curtis – 2022 – UG - Psychology and Computer Science
Griffin Reichert - 2021 – UG - Computer Science & Economics
Yifan Zhang – 2022 – UG - Computer Science
Project Summary:
On websites like Amazon and TripAdvisor, fake reviews (“spams”) are
prevalent. Stories about spams and their victims have been reported widely; these spams overturn
product and service reputations and adversely affect users’ decision making. To protect the
general public, AI-based spam detectors have been employed to actively flag the spams. Also,
more sophisticated users may use their judgments to spot spams. However, AI detectors are not
always accurate and transparent, and will not be much trusted and adopted by the general public
for fighting spams (“algorithm aversion”). Further, without training, even sophisticated users
have difficulty in distinguishing spams from genuine reviews. This project will work towards an
education-based defense against spams, where the general public will be educated to acquire the
skills to spot spams, and to trust and properly rely on AI detectors to improve their protection.
Our summer scope of work will be 1) develop surveys and questionnaires to understand the
scope of the challenges; 2) code a role-playing game where a spammer can craft spams for the
spotters to catch, both for fun and for research; 3) code a simple tutoring tool to teach human to
use AI spam detectors.
Month/Year Project Began:JUNE 2020