At a practical level, when starting, you can use facebook posts as a class exercise (if its relevant to topic obviously :o) to help think through how to thematicize content. I do this as its a good way to get business students talking about controversial social issues relating to business problems. Start with a vigorous debate of interest. Print out all of the comments, cut them up with scissors and then get students to organize into themes. Usually posts responding to a controversial issue are polarised, so for and against is a good place to start, and then start to tease out the issues within each post and look for patterns. This is a good practical way to get started with a research project because the discussions with students then help the researcher realise all the fishhooks as well in thematicising content (e.g. hypertext linking behaviours; mini outbreaks of warfare between posters; chronological changes in the tone of posing as debate settles down etc..).

Another useful method is in conversational analysis and mapping techniques.

The other way is simply to use exel and do it the old-fashioned way.

I see that software like leximancer can be used for this as well, to generate concept maps but there needs to be a lot of data and the conceptual work still needs to be done.

Thanks for the useful insight, Janet. I have one more question, though: to what extent would you regard the data collected through the process you have described above as qualitative or quantitative?

Thanks, Ruvane. Your method of copying and pasting in Word for numbering and subsequent coding in excel sounds interesting. How many posts did you have to code in all?

That will certainly take a while, Ruvanee. All the best. Summer will be an exciting time, considering I will have similar amounts of data to analyze. Have a great week ahead!

Don't know what tool(s) you may be using Henry, but you may want to consider DiscoverText. This is a relatively new tool used for scraping and analyzing textual data from Facebook, Twitter, YouTube, blogs, RSS feeds, etc. (obviously, mostly qualitative in approach). It was created by Dr. Stuart Shulman, the same person behind Coding Analysis Toolkit (CAT). Like many web-based analytical tools, DiscoverText works on a subscription model. The Professional edition of DiscoverText runs about US$25/month, but you can sign up for a free trial version of it for one month. There is also a free Community edition with limited features.

Disclaimer: I am have no vested interest in this company nor the software, but I used it (the free trial version) on a limited content analysis on Twitter just to test it out. Good for content... not so good for social network analysis & identifying influencers...