## 1 Introduction
In particular, the visual representation described in the aforementioned paper by [PERSON] _et al._[11],matches tweets against percentages of internet access in different countries to show that the six largest countries of information production via Twitter's network are the US, Brazil, Indonesia, the UK, Mexico and Malaysia. In other words, just two countries are in the Global North as traditional hubs of codified knowledge production. Claims of democratisation via Twitter are also strongest in relation to its political role. [PERSON] [22] content analysis of tweets (referenced with the Canadian political hashtag #cdnpoli) makes clear that it functions as a disseminator of information and is used by individuals scouring the internet for content around particular political events. However, she also points out that whilst Twitter can be a democratic tool by allowing participation in news making practices, tweets include gaps and inaccuracies. [PERSON] uses an example tweet allegedly posted by the Indian government during the 2008 Mumbai attacks. This requested Twitter users to cease broadcasting about the police and military operations in the city but after being retweeted numerous times, a BBC journalist needed to retract a story, live, on-air after the poster could not be verified. Therefore, whilst social media are particularly sensational sources in terms of participation and the diversity of voices able to speak, critics emphasise that there are no mechanisms for determining whether the information is credible, suggesting that it is a platform to \"anything and everything\" [22]. A lot of the excitement around social media rests on the idea that data volume increases rigor but \"volume\" needs interrogating--for instance, having a large amount of data can mislead people into concluding that it is representative and/or of excellent quality when this isn't always the case [23]. We also see a need to question the parameters denoting the types of content getting sidelined as \"back chatter\" or \"white noise\". This is important because the information and opinion being produced about people, things, places and activities are becoming part of the \"layers, experiences and landscapes of place\" [24]. In other words, as big data is being utilised in the production of knowledge, we need to think critically about its limits, restrictions and biases. Twitter is used by a tiny proportion of people globally, is not universally popular, and there are alternatives like China's Weibo. Editorial intent, social and political bias and other cultural, linguistic, gendered and political factors also need to be understood to account for the presences and absences that exist in emerging data sources and how the knowledge(s) they give way to might then influence individual lives. [PERSON] and [PERSON] [13] make a similar suggestion, extending a concern with data volume to critical reflection on the abilities to aggregate and analyse this data. Describing how big data is positioned as both a tool against societal ills and a danger to civil freedoms (the heightening of corporate and state controls), they call for focus on the subtle shifts underway, drawing parallels to post-Fordism in suggesting that the current turn involves similar \"cellular level\" changes to how society views itself. This means foregrounding questions of epistemology and ethics, reminding us that just as Ford's automated assembly line had its limits, big data is also constrained. For instance, [PERSON] and [PERSON] underline Twitter's poor search features. This means researchers are more likely to focus on the present or immediate past (a political election or natural disaster) because of the impossibility of gaining other types of data. This is why they stress questions must go beyond what the sciences can learn from sources like Twitter to how harvesters of such data might change the meaning of learning and the limits and possibilities of this.