Social Media Hashtags Associated with Bike Commuting: Applying Natural Language Processing Tools

Emphasis on non-motorized travel modes (for example, biking) reduces motorized trips and provides positive effects on the environment and the quality of human life. Understanding factors that influence people to biking or bike commuting can help decision makers, transportation planners, and bike commuting networks. Historically, conventional methods like surveys and crash data analyses were conducted to understand relevant factors. Survey and crash data analysis are difficult to perform in broad scale due to data availability and efforts. A novice approach to determining relevant factors is social media data mining to understand sentiments or motivations of bike commuters. People use terms (with hashtag in the beginning of the term) in Twitter, a popular social media network, to express their thoughts, activities or information. In this study Twitter data associated with bike commuting hashtags were obtained for eight years (2009-2016). Different natural language processing (NLP) tools were employed to perform knowledge discovery from the unstructured text data. The methods of analysis performed in this study to understand the bike commuting community, include exploratory text mining to understand most frequent words and temporal patterns; sentiment analysis to understand people’s opinion or sentiments over the years; and a network analysis to help visualize information sharing patterns of Twitter users who share posts on bike commuting. Sentiments over the years on social media in relation to bike commuting has remained more positive than negative. The study shows multiple insights may be gained through social media data mining.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ANF20 Standing Committee on Bicycle Transportation.
• Authors:
  • Das, Subasish
  • Medina, Gabriella
  • Minjares-Kyle, Lisa
  • Elgart, Zachary
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 18p

Subject/Index Terms

• TRT Terms: Bicycle commuting; Classification; Data mining; Information processing; Network analysis (Planning); Social media
• Identifier Terms: Twitter
• Uncontrolled Terms: Natural language processing (Computer science); Text mining
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01656872
• Record Type: Publication
• Report/Paper Numbers: 18-03545
• Files: TRIS, TRB, ATRI
• Created Date: Jan 22 2018 10:49AM