CommentCoder - A platform to support the open coding of comments from social news sites

Grounded theory is one of the most popular analytic techniques in qualitative analysis. It focuses on generating theoretical ideas or hypotheses from data instead of coming up with ideas or hypotheses beforehand. Basically, the idea of grounded theory is that it is “grounded” in the data. The data to be analyzed may be gathered for example from interviews, observations, or documents.

A recently published paper [1] has shown that extracting knowledge from social news platforms such as Redditand Hacker News can support grounded theory research and may provide insights into topics which aren’t necessarily considered in qualitative research (e.g., interview studies), simply because of its complexity and other limiting factors, e.g. time.

Within this master project, students should design, implement, and evaluate tooling to support grounded theory research. The goal is to create a web-based platform which allows to browse and search through comments posted on social news platforms such as Reddit and Hacker News. In a first step, users should have the ability to record interesting comments, which are used later to form a theory. Having a set of interesting comments, the platform should provide users the ability to apply open coding techniques. This includes the ability to tag and categorize certain comments as a next step towards the formulization of a theory. Besides this classical coding by hand, the platform should provide also more automated techniques supporting this process. This could include the sentiment analysis of comments, the application of machine learning algorithms, and the usage of other tools and frameworks in this area.

The main tasks of this projects are:

• Provide a visually appealing and intuitive web-based platform which allows searching for comments and statements on various social news sites:
  • allow browsing through results, including parent and child comments to understand a conversation’s context
  • support recording and rating interesting comments
  • provide filter-based browsing through those recorded and rated comments
• Support open coding techniques in an intuitive and user-friendly way:
  • tag interesting comments with specifiable codes
  • show and filter comments based on those codes
  • combine various tags into categories
  • show and filter comments based on those categories
  • support the extraction/creation of theories
• Provide extension mechanisms to add more platforms
• (*) Provide extension points for and add additional functionality such as
  • sentiment analysis of statements, e.g., are statements considered positive or negative
  • automated clustering of statements by applying machine learning algorithms
  • interoperability with other grounded theory or open coding tools
• Writing a report summarizing the results from the described work

(*) The scope depends on the number of students

• [1] Barik, Titus and Johnson, Brittany and Murphy-Hill, Emerson:
  I Heart Hacker News: Expanding Qualitative Research Findings by Analyzing Social News WebsitesLink

Posted: 02.11.2015

Contact: Gerald Schermann