Engaging a community through social media-based topics and interactions


  • Andrea L. Kavanaugh Virginia Tech
  • Ziqian Song Virginia Tech




Social Media, Community Computing, Social and Political Participation


Social media collected over time using keywords, hashtags and accounts associated with a particular geographic community might reflect that community’s main events, topics of discussion, and social interactions. We are interested in evidence for the support of community involvement that the aggregated Web pages and social media might help to create. We collected and analyzed Twitter data related to a geographic area over a two-year period to identify and characterize relevant topics and social interactions, and to evaluate the support for community involvement that such Twitter use might indicate. This kind of data collection has built-in biases, of course, just as local print media or online newsgroups do. We analyzed our data using the open source tool NodeXL to identify topics and their changes over time, and to create social graphs based on retweets and @ mentions that suggest interactions around topics. Our findings show: 1) distinct topics; 2) large and small clusters of social interactions around a variety of topics; 3) patterns suggesting what are called ‘community clusters’ and ‘tight crowd’ types of conversations; and, 4) evidence that Twitter supports local community involvement among users. Modeling topics over time and displaying visualizations of social interactions around different topics in a community can offer insights into the important events and issues during a given period. Such visualizations also reveal hidden (or obscure) topics due to a smaller number of participants — whether government representatives, voluntary associations, or citizens. There is clear evidence that Twitter supports social interaction and informal discussion or exchange around local topics among users, thereby facilitating community involvement.

Author Biographies

Andrea L. Kavanaugh, Virginia Tech

Senior Research Scientist and Associate Director, Center for Human-Computer Interaction

Ziqian Song, Virginia Tech

PhD candidate, Computer Science Department


Additional Files



How to Cite

Kavanaugh, A. L., & Song, Z. (2018). Engaging a community through social media-based topics and interactions. First Monday, 23(4). https://doi.org/10.5210/fm.v23i4.8146