NCapture, a plug-in to the Internet Explorer and Google Chrome web browsers, enables the extracting of data from the Surface Web and various online social media platforms. The extracted data is easily ingested into an NVivo project for coding and analysis.  (The main social media platforms that may be accessed currently are Facebook, LinkedIn, and Twitter.  The social media platforms generally have to have an application programming interface or "API" that developers may use to develop tools to access the social media platforms' data layer.  Some will require authentication in order to access the data, so the service providers may rate-limit and somewhat control access to their data.)  

If this was not downloaded and installed early on, it may still be added by going to QSR International’s download site for NVivo 10 for Windows add-ons. There is a version available for Macs that have the Google Chrome browser installed.  There is a dedicated page for NCapture downloads.




So what may be done with NCapture? What are some of the general workflow sequences? What may be asserted from some of the social media platform research? 
Finally, NCapture may also be used to extract microblogging messages from a Twitter account’s Tweetstream (known as a user stream) or even from a #hashtag conversation on Twitter.  A Tweetstream is a collection of messaging around a particular @user account on Twitter, and it goes back in time.  Approximately 3,200+ total messages (of one account) may be captured based on the Twitter API.  In terms of #hashtag conversations, those collect ad hoc discussions around a labeled topic, and the Twitter API for these only goes back about a week.  Those who want full datasets will have to go through a commercial vendor (Gnip).  The #hashtag search captures a small percentage of the most recent Tweets with the designated #hashtagged label.  The data is time-limited cross-sectional semi-synchronous data; other tools enable the capture of "continuous" data (but still not the full set given the Twitter API limits).  

Basically, go to the particular page on Twitter. Decide whether you want to extract Tweets as a dataset (instead of just capturing the web page as a .pdf). Define the parameters of the data extraction, such as whether you want to include retweets in the dataset or not. Include the information for how NVivo should ingest the data. Download the dataset. Open NVivo. Ingest the extracted information. 

Once the dataset of Tweets has been uploaded (whether as an NCapture set or from some other capturing source), click on it to activate options to process it in NVivo.  

In the ribbon, go to the Analyze tab.  In the Coding area, click on the AutoCode option (which should be in color, not grayed out).  Select "Auto code using source structure or style."  

    

Follow the steps to select how you want the data coded--by hashtags as nodes with related Tweets coded to those nodes; with selected columns as the nodes and the related coded text in the cells as the related data inside the node; or with each respondent as the node and all related captured text as the included data inside the node.  (It is important to really think through how you want to ingest the data into NVivo.  You can always delete what you've ingested in the Nodes area and re-process.  You can always process the data in multiple ways for different types of queries--for full exploitation of the data.)  The three visuals below show these various approaches.  Node cells are highlighted in pink, and the data cells are highlighted in yellow.  



There are a few other steps to this, which depend on the specifics of the data.  

It makes rational sense to maintain some sort of basic data accounting log when using a wide range of data types over a time span.  The time element is of particular importance when dealing with social media-based data because much of it is dynamic (particularly microblogging data), and most of it is time-sensitive.  To be relevant, the data may have to be captured periodically or even continuously.  A data accounting log may help a researcher keep track of what information was captured when, and when to conduct more data extractions.  

Also, researchers sometimes talk of "future worlds" in the context of data analysis.  The idea is that in the future, there may be other methods that are applicable to extracting information from data.  Proper documentation of the data will enable future users of that data to know what they are looking at, and it may better enable them to extract information and ultimately knowledge from the data.  

Another salient issue regarding the handling of social media platform data involves the ethics of the uses of these sources for research.  What are some common ethical considerations? 

• Is the social media information being used in research private or public?
• Have those who shared the information been sufficiently made aware of the research for informed consent? 
  
• Are common users participating in online social networks (OSNs) and social media platforms aware of what is knowable from what they share (through data mining, stylometry, network analysis, and other techniques)?  Are users aware that trace and metadata are collected along with their shared contents? 
  
• How can individuals (and their information) be sufficiently de-identified (in a way so that they are not re-identifiable with a little work)?
  
• How can researchers differentiate actual "personhood" of those behind social media user accounts as compared to algorithmic agents or 'bots? 
  
• How can researchers identify children (whose data involves even greater protections under the Children's Online Privacy Protection Act) from a set of user accounts?
  
• How verifiable are the assertions about social media data ?  Are there ways to cross-validate such data and to attain "ground truth"?  How can the uncertainties be accurately represented to strengthen the research? 
  

Pro-research oversight stance:  On the one hand, researchers have argued that people who share information on social media are not generally aware of the many ways their information may be mined for meanings and ideas.  They are not aware of the “data leakage” that occurs whenever they decide to include EXIF (exchangeable image file format) data in their images or videos.  They are not aware of the potential of location tracing based on their mobile app check-ins to certain hotspots.  They have not given their explicit permission to use their data for research.  Most social media information is trackable to personally identifiable information (PII).  Some suggest that if the Internet is used for the collection of survey information, Internet Protocol (IP) addresses should not be captured because these may be identifiable to a person and a physical location by sophisticated researchers. 

Institutional Review Board (IRB) oversight is necessary even when researchers are using widely available datasets of people’s information to provide perspective in the potential misuse of such data (whether by the researcher...or even further downstream).  Having Institutional Review Board (IRB) oversight also protects researchers and also protects the general public—particularly as the research may evolve into more dangerous territory. 

Anti-research oversight stance:  On the other hand, researcher have argued that this data is widely, legally, and publicly available (without a special need for creating user accounts to access much of the information).  The argument goes:  People have signed away their rights when they agreed to the various end user license agreements (EULAs) on the social media sites.  It is in-feasible for researchers to provide informed consent and to acquire permissions again.  Going through IRB review may be costly in time and effort.  

Both sides have valid points.  This decision is in the purview of the researcher and research team, the sponsoring organizations, the grant funding organizations, the research compliance oversight agencies, and other stakeholders.  

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