You are being asked to take part in a study, which seeks to understand judgments of political statements. You will be asked to play a game where you are prompted to judge whether or not actual statements made by political figures are factual or not. You will be provided survey questions at the start and end of the game. The study should take about 15-20 minutes of your time. This study is completely voluntary and you can skip questions you don’t feel comfortable with and you may opt out at any time. All responses will be kept confidential.
|About FibberThe upcoming presidential elections will be the first since Citizens United v. FEC, which means an ever-increasing role of political advertisements. According to the Annenberg Public Policy Center of the University of Pennsylvania, these political advertisements are not just becoming more prevalent, but also riddled with deception and confusion.Framed within the context of changes to financing political ads and exponential growth in social media and online gaming, Fibber explores the question – how can we design games that increase self-reflection on one’s susceptibility to deception in an effort to heighten fact-checking around political messaging?Beyond an attempt to internalize facts or advocate for fact-checking, Fibber attempts to both use in-game decisions to prompt players to reflect on their own bias in how they perceive “the truth” while also crowdsourcing the most “uncaught” deceptive statements made by presidential candidates so they can be shared via Twitter with the broader community.|
At the end of the game, players are provided data on the rate at which they were able to detect facts and fibs, as well as the kind of bias that emerged such as being more likely to think a specific candidate was lying or being truthful. Such reflective practices can lead to more critical literacy around political messaging.
|Crowd-sourcing Deceptive Statements
Decisions made by players are collected into a database whereby we can determine which are the most popular deceptions that are commonly thought to be true. These crowdsourced misperceptions are then automatically tweeted with to relevant political hashtags. For example a lie made by Romney and very commonly guessed as true by players will be tweeted with hashtags #romney #foxnews. Such a mechanic uses a collection of players to source deceptions that can then be provided to the broader community for their own reflection.