## 1 Introduction
Following a review of literature which synthesises commentary from computing science, sociology and geography, this paper details the software framework and schema design and the technical challenges inherent to its development. We then turn to the methodological issues inherent to testing a first version of the framework with a small sample of pilot data. The issues we focus on include closeness with research subjects, ethics and issues of interdisciplinarity that are crucial if researchers are to benefit from one another's expertise and gain from emerging sources of big data within neogeography. A concluding section offers thought on scholars' digital responsibilities during the current turn towards computational approaches. **2. Neogeography, Social Media and Resurging Interest in \"Big Data\"**  
Though participation is uneven and there remain infrastructural barriers to internet access, one in four people now use social media, with penetration of platforms like Facebook and Twitter in almost every part of the world [10]. The data trails left by users of social media are of key concern to researchers because they reveal social, economic and political practices at a scale once unimaginable [11]. This is why social media are looked to as additional sources of big data alongside others such as government repositories, telephone logs and archives [12]. [PERSON] and [PERSON] [13] suggest that scientists, technologists, health specialists, business professionals and others are clamouring for access to these new sources and the frequency of celebratory assertions leveled about them supports this claim. For example, an article in _Wired_ magazine claimed \"the end of science in the Petabyte Age\" with links to studies explaining how the mining of big data could support the filing of successful lawsuits, track breaking news, monitor outbreaks of disease and predict airfares [14]. Similarly, [PERSON] [15] and [PERSON] [16] suggest a slew of businesses and marketing commentators looking to big data to inform consumer decisions on a range of products and services. Twitter is the most prominent source of data in a social media context [17]. [PERSON] [18] lauds the social networking tool as an unprecedented opportunity to study human communication whilst [PERSON] _et al._[19] underline its popularity as an open platform for third party developers as it makes several years worth of archives available through its application programming interfaces (APIs). The latter includes tweets, user profile information, and geo-located data that we collect, organise and visualise through the TMS software. However, the rise of Web 2.0 tools like Twitter is as challenging for academic researchers as it is promising. Social media offer vast reserves of information but are not without limits. These tools therefore need to be approached cautiously [20]. Questions include: how can we make information from these sources more manageable for socially beneficial research, what skills and technologies are needed to harvest and analyse the data from these constantly changing sources and crucially, what might big data hide as well as reveal? A key concern is that whilst many look to the networks characteristic of these sources as unbounded, these can actually give way to a \"rich gets richer\" effect [21]. Working with the PR firm Edelmann, [PERSON] _et al._[21] developed a five-category model of influence based on Twitter exchanges that identify prominent players across Twitter's network by analysing their conversation habits. Whilst this aids researchers in identifying established nodes of information (and power) within the \"Twittersphere\", it also indicates that the most established nodes increase their connectivity at the expense of younger, less connected ones. However, evidence also indicates that Twitter is allowing for broader participation than possible in other forms of media.