Using social media research methods to identify hidden churches

Transcription

1 Using social media research methods to identify hidden churches Anthony-Paul Cooper CofE Faith in Research Conference th June 2014

2 Overview The research described in this presentation is part of the London Church Attendance Baseline study. End result of the study will be a tagged map of all known churches in London, from a range of sources (This data will also be available in list format!). For the sake of this presentation, we re just interested in how social media research methods can be used to identify previously undocumented churches. Important note: This presentation features snapshots of real data (captured on 20th April 2014). As such, despite efforts to redact offensive language, some offensive content/themes may still remain.

3 Twitter: What will be done? Use the Twitter API to pull all Tweets created within London which contain the word church every Sunday ( ) for a 6 month period between April and September Where this methodology identifies churches not already known about, add to the online map which will form the new baseline.

4 Twitter: How will this be done? An API search term will be used. This term takes a starting point within London (latitude: , longitude: ) and then gathers all Tweets containing the word church from within 60Km of this starting point. This will generate some results from outside the M25 area (as 60Km is a deliberately generous radius), but this will become apparent when we come to add results to online map.

5 Twitter: What does this search area look like? The starting point and 60Km radius gives the following coverage area:

6 Twitter: What will the search results look like? Results are initially output into a large.txt file:

7 Twitter: What will the search results look like? These can easily be moved to a.xls spreadsheet. From here the data can be cleaned. Headings can be added, formatting can be tidied and any data from days not being considered can be discarded:

8 Twitter: What will the search results look like? Many results will contain no geo data, so these data can also be discarded:

9 Twitter: How can any sense be made of the data? The data we are interested in is: - Posted on the date in question (i.e. a Sunday between 0001 and 2359) - Contains the word Church - Contains geo data Making sense of this subset of data requires manual coding. I have chosen to code as follows: - Rose: Tweets with no reason to believe posted from a church - Tan: Tweets which may or may not have been posted from a church - Light Green: Tweets believed to be posted from a church

10 Twitter: How can any sense be made of the data?

11 Twitter: How can any sense be made of the data? The Tweets which were posted within a church can be gathered together, and the geo data for these Tweets can be cleaned, ready for input onto the online map.

12 Twitter: How useful is the data? Some coded as Tweets with no reason to believe posted from a church: I miss going to church u know Church in the AM. Some coded as Tweets which may or may not have been posted from a church: i love church We decided to go to a less modern church for Easter. While I would have loved to go to #Hillsong Some coded as Tweets believed to be posted from a church: Easter Sunday Hillsong Church London I'm at Our Lady and St Joseph's Catholic Church

13 Geo Mapping (During last 3 months of Tweet gathering) Create a version of the online map which includes only churches we already know of, and allow users to tag their own churches.

14 The Future: Longitudinal Study using Twitter Mini-Reports Commence longitudinal attendance reporting over a 12 month period, with willing volunteer churches from the new baseline. Use Twitter as the submission tool for this data (e.g. Westminster Abbey. Adults = 500, Children = 180 #LondonChurchStudy )

15 Any Questions? For any questions following the conference, please