HELPING PEOPLE'S

Patrick Readshaw is a Media and Cultural Studies Doctoral Candidate at Canterbury Christ Church University. Patrick is interested in social media as an alternative and empowering source of information on current events, free from the constraints of other agenda-setting media forms. You can contact Patrick by email on p.j.readshaw68@canterbury.ac.uk     When I was asked to write a blog for NSMNSS, I was certainly excited and being my first post of this kind I was suitably anxious about the prospect. However, my ongoing thesis has never ceased to provide interesting discussions with individuals in linked or parallel fields relating to social media. The main caveat in these discussions is that I often have to try not to over complicate things. With that in mind and my ham-fisted introduction out of the way I want to take some time to break down the value of so called “new media systems” like Twitter and the how I personally go about dealing with the data I collect.  Sinc...

Brian Head is a research methodologist at RTI International. This post first appeared on SurveyPost on 20 May, 2014. You can follow Brian on Twitter @BrianFHead . Survey researchers have become interested in big data because it offers potential solutions to problems we’re experiencing with traditional methods. Much of the focus so far has been on social media (e.g., Tweets), but sensors (wearable tech) and the internet of things (IoT) are producing an increasingly rich, complex, and massive source of data. These new data sources could lead to an important change in how individuals see the data collected about them, and thus have ramifications for those interested in gathering and analyzing those data. Who compiles data? Quantitative data about people have been gathered for millennia. But with technological advances and identification of new purposes for it, the past 100 years have seen significant increases in the amount of data produced and collected—e.g., data on consume...

Ashley Richards is a survey methodologist at RTI International. This post first appeared on SurveyPost on 29, July 2014.   Last fall at MAPOR  , Joe Murphy presented the findings of a fun study he did with our colleague, Justin Landwehr, and me. We asked survey respondents if we could look at their recent Tweets and combine them with their survey data. We took a subset of those respondents and masked their responses on six categorical variables. We then had three human coders and a machine algorithm try to predict the masked responses by reviewing the respondents’ Tweets and guessing how they would have responded on the survey. The coders looked for any clues in the Tweets, while the algorithm used a subset of Tweets and survey responses to find patterns in the way words were used. We found that both the humans and machine were better than random in predicting values of most of the variables. We recently took this research a step further and compared the accuracy of ...