The role of bots in U.S. Real estate development online communication. (January 2023)

• Record Type: Journal Article
• Authors: Hollander, Justin B.; Potts, Ruth; Hartt, Maxwell; Situ, Minyu; Seto, Alexander
• Is Part Of: Computers, environment and urban systems. Volume 99(2023)
• Publication Date: 2023-01
• Subjects: Social media -- Social bots -- Participatory planning -- Artificial intelligence -- Real estate development; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2022.101918
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Planners and policymakers have been quick to open electronic channels of participation to inform decision-making processes due to the low cost and high potential engagement. This has created an opportunity for subversive groups to amplify their voice, and therefore influence, in land development and planning processes using automated social media accounts. While scholars have begun to examine how social media platforms such as Facebook and Twitter have been used to manipulate political discourse, no research has considered the potential damage that such manipulation can and does cause in the local planning context. This study examines the role and potential risk of automated social media accounts in the participatory planning process through an empirical investigation of 21 real estate development projects in the U.S. Using a machine learning model to assess 41, 191 relevant Tweets, we found that at least 10% and upwards of 50% of Tweets concerning the various real estate development projects were generated by bots. Automated tweets included critical, neutral, and supportive content demonstrating that social bots represent a range of interests. Planning and public policy professionals will need to develop, apply, and be aware of bot-detection tools and actively promote and build trust through their own digital channels to ensure accurate information is disseminated. Highlights: Research explored the impacts that social bots have on the local planning context. Machine learning tools were employed to classify Twitter accounts as bots. Analysis found that upwards of 50% of Tweets concerning various real estate development projects were generated by bots.