Title: Automated Assistants to Identify and Prompt Action on Visual News Bias,
Abstract: Bias is a common problem in today's media, appearing frequently in text and
in visual imagery. Users on social media websites such as Twitter need better
methods for identifying bias. Additionally, activists --those who are motivated
to effect change related to some topic, need better methods to identify and
counteract bias that is contrary to their mission. With both of these use cases
in mind, in this paper we propose a novel tool called UnbiasedCrowd that
supports identification of, and action on bias in visual news media. In
particular, it addresses the following key challenges (1) identification of
bias; (2) aggregation and presentation of evidence to users; (3) enabling
activists to inform the public of bias and take action by engaging people in
conversation with bots. We describe a preliminary study on the Twitter platform
that explores the impressions that activists had of our tool, and how people
reacted and engaged with online bots that exposed visual bias. We conclude by
discussing design and implication of our findings for creating future systems
to identify and counteract the effects of news bias.