The “Capturing the Conference Conversation” series (Part 1, Part 2) introduced tools and methods that enable capture, visualization, analysis, and sharing of Twitter data. The value of being able to work with Twitter data in this manner is multifaceted.
In practical terms, the ability to consolidate and share discussions about research, resources, and tools enhances the possibility that this data can be put to productive use. Possible uses include referencing the data for mention of a tool or tracking the progression of a debate over time.
Viewshare: Timeline
Building collections of Twitter data based on a hashtag provides access to all people speaking about a topic. Using Viewshare these collections can be filtered by other hashtags. Filtering allows discovery of topics that are not strongly expressed in the collection. This function helps to ensure that subtopics are not lost in a larger topic of discussion.
Viewshare: List view, filtered by #Chaucer
Both methods described in this series provide the means to derive insights about the structure and behavior of a community that coalesces around an event like an academic conference. For example the method described in Part 1 of the series allows simple visualization of languages used during a conference on Twitter. For international conferences this could give some sense of how truly international discourse is within a field on a specific platform.
Viewshare: Pie graph, sorted by language
The method used in Part 2 of the series enables visualization of interactions between Twitter users. For example, TAGS provides a network visualization of retweeting relationships around a topic. User names are scaled according to the number of retweets they receive. The visualization quickly provides a sense of who the primary sources of information are in a given network. This knowledge can inform decisions about who to add to a network, thus providing a strategic means of enriching Twitter as an information resource.
TAGS: Retweet Network
In line with using these approaches to understand the structure and behavior of a community, an analysis of tweet frequency can inform decisions as to optimal information sharing times. In other words the data can help you answer the question – “When will I have the best opportunity to be heard?”
TAGS: Twitter activity over time
The tools described in this series were ultimately chosen because of their free cost and ease of use. However, they are not the only tools that can enable this kind of work. Twitter data can be gathered via application programming interface (API) and visualized using a wide range of tools. As your technical skills increase we encourage exploration of the Twitter API and visualization libraries like D3.js.