Public Opinion on Dockless Bike Sharing: A Machine Learning Approach

Abstract

Dockless bike sharing is an emerging paradigm. Like many other technologies, it brings advantages and disadvantages to communities. Further investigation into public opinion will shed light on the impact of this technology on communities and provide input to city authorities for transportation planning. Transportation planning processes can be enhanced by engaging the community through social media technologies. Social media like Twitter, Facebook, and other microblogging media have been used for planning, but have not been extensively evaluated for that purpose. This study examined approximately 32,000 posts on Twitter to assess public opinion on dockless bike-sharing systems. Using a mix of text mining and statistical techniques, we examined relevant posts to determine the sentiment polarity of tweets, the underlying topics in the tweets, and the extent of engagement and impact on the decision-making process. Results given by two different sentiment algorithms show that there is more positive than negative polarity across the algorithms. Also, the findings show that the underlying topics in tweets include electric scooters, private e-hailing companies, and blockage of sidewalks, among others. The results indicate that the dockless shared mobility models are potentially useful in generating participation, but faced substantial technical, analytical, and communication barriers to influencing decision-making.