During the 2016 U.S. presidential election, evidence showed that Russia used social media ads and bots to influence social media political conversations, spread disinformation, increase polarization between political groups, and ultimately played a role in affecting the outcome of the U.S. presidential election. Robots or bots were used to disrupt and distract online users around the world while advancing the interests of individuals, politicians, and organizations. This is a concern for the general public, governments, and researchers. Despite previous research conducted on the subject, no research identifies if certain demographic variables are indicators if social media users will engage with partisan bot content or share bot content, effectively amplifying bot messaging. This dissertation created new research to survey U.S. Twitter users to determine if they would engage with or share political bot content if it aligns with their political affiliation and which variables could determine if their Twitter usage or voting behavior could be influenced. The research utilized a quantitative methodology and the theoretical frameworks of conflict theory, structural violence theory, and resource mobilization theory. The outcomes of this research revealed that six independent variables affected Twitter users’ perceptions of bots. These variables were bot identification, gender, news source, political affiliation, Twitter frequency, and using Twitter for political updates. The results from this study will add to the greater field of conflict resolution as it pertains to digital conflict in the United States.