Showing papers in "Big Data & Society in 2015"
TL;DR: In this article, the authors show how open data movement re-articulates notions of democracy, participation, and journalism by applying practices and values from open source culture to the creation and use of data.
Abstract: This article shows how activists in the open data movement re-articulate notions of democracy, participation, and journalism by applying practices and values from open source culture to the creation and use of data. Focusing on the Open Knowledge Foundation Germany and drawing from a combination of interviews and content analysis, it argues that this process leads activists to develop new rationalities around datafication that can support the agency of datafied publics. Three modulations of open source are identified: First, by regarding data as a prerequisite for generating knowledge, activists transform the sharing of source code to include the sharing of raw data. Sharing raw data should break the interpretative monopoly of governments and would allow people to make their own interpretation of data about public issues. Second, activists connect this idea to an open and flexible form of representative democracy by applying the open source model of participation to political participation. Third, activists acknowledge that intermediaries are necessary to make raw data accessible to the public. This leads them to an interest in transforming journalism to become an intermediary in this sense. At the same time, they try to act as intermediaries themselves and develop civic technologies to put their ideas into practice. The article concludes with suggesting that the practices and ideas of open data activists are relevant because they illustrate the connection between datafication and open source culture and help to understand how datafication might support the agency of publics and actors outside big government and big business.
198 citations
TL;DR: Differences between social scientists and computer scientists in the field of text analysis have implications that potentially can improve the practice of social science.
Abstract: Social scientists and computer scientist are divided by small differences in perspective and not by any significant disciplinary divide. In the field of text analysis, several such differences are noted: social scientists often use unsupervised models to explore corpora, whereas many computer scientists employ supervised models to train data; social scientists hold to more conventional causal notions than do most computer scientists, and often favor intense exploitation of existing algorithms, whereas computer scientists focus more on developing new models; and computer scientists tend to trust human judgment more than social scientists do. These differences have implications that potentially can improve the practice of social science.
117 citations
TL;DR: In this paper, a different relationship between the public and data mining might be established, one in which publics might be said to have greater agency and reflexivity vis-a`-vis data power.
Abstract: New methods to analyse social media data provide a powerful way to know publics and capture what they say and do. At the same time, access to these methods is uneven, with corporations and governments tending to have best access to relevant data and analytics tools. Critics raise a number of concerns about the implications dominant uses of data mining and analytics may have for the public: they result in less privacy, more surveillance and social discrimination, and they provide new ways of controlling how publics come to be represented and so understood. In this paper, we consider if a different relationship between the public and data mining might be established, one in which publics might be said to have greater agency and reflexivity vis-a`-vis data power. Drawing on growing calls for alternative data regimes and practices, we argue that to enable this different relationship, data mining and analytics need to be democratised in three ways: they should be subject to greater public supervision and regulation, available and accessible to all, and used to create not simply known but reflexive, active and knowing publics. We therefore imagine conditions in which data mining is not just used as a way to know publics, but can become a means for publics to know themselves.
108 citations
TL;DR: Using a new materialist line of questioning that looks at the agential potentialities of water and its entanglements with Big Data and surveillance, the authors explores how the recent Snowden rev...
Abstract: Using a new materialist line of questioning that looks at the agential potentialities of water and its entanglements with Big Data and surveillance, this article explores how the recent Snowden rev...
101 citations
TL;DR: In the case of We the People, a US national experiment in the use of social media technology to enable users to propose and solicit support for policy suggestions to the White House, this paper found a core group of petitions in the “support law-abiding gun owners” theme that were highly connected and four "communities" of e-petitioners mobilizing in opposition to change in gun control policies and in favor of alternative proposals.
Abstract: This study aims to reveal patterns of e-petition co-signing behavior that are indicative of the political mobilization of online “communities”. We discuss the case of We the People , a US national experiment in the use of social media technology to enable users to propose and solicit support for policy suggestions to the White House. We apply Baumgartner and Jones's work on agenda setting and punctuated equilibrium, which suggests that policy issues may lie dormant for periods of time until some event triggers attention from the media, interest groups, and elected representatives. In the case study presented, we focus on 21 petitions initiated during the week after the Sandy Hook shooting (14–21 December 2012) in opposition to gun control or in support of policy proposals that are alternatives to gun control, which we view as mobilized efforts to maintain stability and equilibrium in a policy system threatening to change. Using market basket analysis and social network analysis we found a core group of petitions in the “support law-abiding gun owners” theme that were highly connected and four “communities” of e-petitioners mobilizing in opposition to change in gun control policies and in favor of alternative proposals.
101 citations
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TL;DR: In this article, a special issue on data and agency argues that datafication should not only be understood as the process of collecting and analysing data about Internet users, but also as feeding such data back to users, enabling them to orient themselves in the world.
Abstract: This introduction to the special issue on data and agency argues that datafication should not only be understood as the process of collecting and analysing data about Internet users, but also as feeding such data back to users, enabling them to orient themselves in the world. It is important that debates about data power recognise that data is also generated, collected and analysed by alternative actors, enhancing rather than undermining the agency of the public. Developing this argument, we first make clear why and how the question of agency should be central to our engagement with data. Subsequently, we discuss how this question has been operationalized in the five contributions to this special issue, which empirically open up the study of alternative forms of datafication. Building on these contributions, we conclude that as data acquire new power, it is vital to explore the space for citizen agency in relation to data structures and to examine the practices of data work, as well as the people involved in these practices.
99 citations
TL;DR: The MobileMiner is outlined, an app to consider how gaining access to one’s own data not only augments the agency of the individual but of the collective user, and the data making that transpired during the hackathon is discussed.
Abstract: This paper builds off the Our Data Ourselves research project, which examined ways of understanding and reclaiming the data that young people produce on smartphone devices. Here we explore the growing usage and centrality of mobiles in the lives of young people, questioning what data-making possibilities exist if users can either uncover and/or capture what data controllers such as Facebook monetize and share about themselves with third-parties. We outline the MobileMiner, an app we created to consider how gaining access to one’s own data not only augments the agency of the individual but of the collective user. Finally, we discuss the data making that transpired during our hackathon. Such interventions in the enclosed processes of datafication are meant as a preliminary investigation into the possibilities that arise when young people are given back the data which they are normally structurally precluded from accessing.
98 citations
TL;DR: This paper challenges the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize and argues for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.
Abstract: The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage.
85 citations
TL;DR: The findings show that Twitter is considered a critical tool for informal communication within DH invisible colleges, functioning at varying levels as both an information network (learning to ‘Twitter’ and maintaining awareness) and a social network (imagining audiences and engaging other digital humanists).
Abstract: Big Data research is currently split on whether and to what extent Twitter can be characterized as an informational or social network. We contribute to this line of inquiry through an investigation of digital humanities (DH) scholars’ uses and gratifications of Twitter. We conducted a thematic analysis of 25 semi-structured interview transcripts to learn about these scholars’ professional use of Twitter. Our findings show that Twitter is considered a critical tool for informal communication within DH invisible colleges, functioning at varying levels as both an information network (learning to ‘Twitter’ and maintaining awareness) and a social network (imagining audiences and engaging other digital humanists). We find that Twitter follow relationships reflect common academic interests and are closely tied to scholars’ pre-existing social ties and conference or event co-attendance. The concept of the invisible college continues to be relevant but requires revisiting. The invisible college formed on Twitter is messy, consisting of overlapping social contexts (professional, personal and public), scholars with different habits of engagement, and both formal and informal ties. Our research illustrates the value of using multiple methods to explore the complex questions arising from Big Data studies and points toward future research that could implement Big Data techniques on a small scale, focusing on sub-topics or emerging fields, to expose the nature of scholars’ invisible colleges made visible on Twitter.
68 citations
TL;DR: It is argued that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected, one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters.
Abstract: Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification) build on large aggregates of information (Big Data) analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike), Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings), one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.
60 citations
TL;DR: This is the first study in which political positions are automatically extracted and derived from a very large corpus of online news, generating a network that goes well beyond traditional word-association networks by means of richer linguistic analysis of texts.
Abstract: The automated parsing of 130,213 news articles about the 2012 US presidential elections produces a network formed by the key political actors and issues, which were linked by relations of support and opposition. The nodes are formed by noun phrases and links by verbs, directly expressing the action of one node upon the other. This network is studied by applying insights from several theories and techniques, and by combining existing tools in an innovative way, including: graph partitioning, centrality, assortativity, hierarchy and structural balance. The analysis yields various patterns. First, we observe that the fundamental split between the Republican and Democrat camps can be easily detected by network partitioning, which provides a strong validation check of the approach adopted, as well as a sound way to assign actors and topics to one of the two camps. Second, we identify the most central nodes of the political camps. We also learnt that Clinton played a more central role than Biden in the Democrat...
TL;DR: This paper discusses the empirical, Application Programming Interface (API)-based analysis of very large Facebook Pages, and outlines an exploratory approach and a number of analytical techniques that take the API and its idiosyncrasies as a starting point for the concrete investigation of a large dataset.
Abstract: This paper discusses the empirical, Application Programming Interface (API)-based analysis of very large Facebook Pages. Looking in detail at the technical characteristics, conventions, and peculiarities of Facebook’s architecture and data interface, we argue that such technical fieldwork is essential to data-driven research, both as a crucial form of data critique and as a way to identify analytical opportunities. Using the ‘‘We are all Khaled Said’’ Facebook Page, which hosted the activities of nearly 1.9 million users during the Egyptian Revolution and beyond, as empirical example, we show how Facebook’s API raises important questions about data detail, completeness, consistency over time, and architectural complexity. We then outline an exploratory approach and a number of analytical techniques that take the API and its idiosyncrasies as a starting point for the concrete investigation of a large dataset. Our goal is to close the gap between Big Data research and research about Big Data by showing that the critical investigation of technicity is essential for empirical research and that attention to the particularities of empirical work can provide a deeper understanding of the various issues Big Data research is entangled with.
TL;DR: In this article, the authors survey the 18 contributions from scholars in the humanities and social sciences, and highlight several questions and themes that emerge within and across them, including the locus and nature of human life, the nature of interpretation, the categorical constructions of individual entities and agents, the relevance of contexts and temporalities, and the determinations of causality.
Abstract: In our Introduction to the Conceiving the Social with Big Data Special Issue of Big Data & Society, we survey the 18 contributions from scholars in the humanities and social sciences, and highlight several questions and themes that emerge within and across them. These emergent issues reflect the challenges, problems, and promises of working with Big Data to access and assess the social. They include puzzles about the locus and nature of human life, the nature of interpretation, the categorical constructions of individual entities and agents, the nature and relevance of contexts and temporalities, and the determinations of causality. As such, the Introduction reflects on the contributions along a series of binaries that capture the dualities and dynamisms of these themes: Life/Data; Mind/Machine; and Induction/Deduction.
TL;DR: In this article, the authors situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics.
Abstract: Data analytics, particularly the current rhetoric around “Big Data”, tend to be presented as new and innovative, emerging ahistorically to revolutionize modern life. In this article, we situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics. Spatial Big Data promises an epistemic break in marketing, a leap from targeting geodemographic areas to targeting individuals. Yet it inherits characteristics and problems from geodemographics, including a justification through the market, and a process of commodification through the black-boxing of technology. As researchers develop sustained critiques of data analytics and its effects on everyday life, we must so with a grounding in the cultural and historical contexts from which data technologies emerged. This article and others (Barnes and Wilson, 2014) develop a historically situated, critical approach to spatial Big Data. This history illustrates connections to the critical issues of surveillance, redlining, and the production of consumer subjects and geographies. The shared histories and structural logics of spatial Big Data and geodemographics create the space for a continued critique of data analyses’ role in society.
TL;DR: It is explained how most analyses performed on Big Data today lead to “precisely inaccurate” results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size.
Abstract: Social scientists and data analysts are increasingly making use of Big Data in their analyses. These data sets are often “found data” arising from purely observational sources rather than data derived under strict rules of a statistically designed experiment. However, since these large data sets easily meet the sample size requirements of most statistical procedures, they give analysts a false sense of security as they proceed to focus on employing traditional statistical methods. We explain how most analyses performed on Big Data today lead to “precisely inaccurate” results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size. Before any analyses are performed on large data sets, we recommend employing a simple data segmentation technique to control for some major components of observational data biases. These segments will help to improve the accuracy of the results.
TL;DR: The role of data in the production and playing of football is discussed in this article, with the suggestion that forms of measurement and pattern recognition are now central to the performance of footballers and the recruitment and organization of squads.
Abstract: This article reflects on how data circulations and data analysis have become a central and routine part of contemporary life; it does this through the lens of a particular cultural form: the game of football. More specifically, the article focuses upon the role of data in the production and playing of football, with the suggestion that forms of measurement and pattern recognition are now central to the performance of footballers and the recruitment and organization of squads. The article reflects on what this case study reveals about the implications of data, metrics and analytics for contemporary culture and suggests that we can use examples like football to see how embedded these data-processes are in the social world. This article presents the concept of productive measures as one means for analysing such developments.
TL;DR: In an original analysis of developments from commercial, governmental and civil society sectors, the article examines two interrelated dimensions of an emerging smart schools imaginary: the constant flows of digital data that smart schools depend on and the mobilization of analytics that enable student data to be used to anticipate and shape their behaviours.
Abstract: Coupled with the ‘smart city’, the idea of the ‘smart school’ is emerging in imaginings of the future of education. Various commercial, governmental and civil society organizations now envisage edu...
TL;DR: In this article, the authors present a low latency method for the observation and measurement of social indicators within an online community by focusing on the public Facebook profiles of 187 federal German politicians.
Abstract: In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today's society. Platforms like Facebook, Twitter, Google+ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: “zooming” in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text.
TL;DR: In this article, a commentary recaps a Centre for Central Banking Studies event held at the Bank of England on 2-3 July 2014 is presented, where the authors discuss central banks' emerging interest in Big Data approaches with their broader uptake by other economic agents.
Abstract: This commentary recaps a Centre for Central Banking Studies event held at the Bank of England on 2–3 July 2014. The article covers three main points. First, it situates the Centre for Central Banking Studies event within the context of the Bank’s Strategic Plan and initiatives. Second, it summarises and reflects on major themes from the event. Third, the article links central banks’ emerging interest in Big Data approaches with their broader uptake by other economic agents.
TL;DR: The computational hermeneutics of content analysis as mentioned in this paper has been proposed to mimic the kinds of questions and concerns that have traditionally been the focus of a close reading, a reading that focuses on what Kenneth Burke described as the poetic meanings of a text.
Abstract: We describe some of the ways that the field of content analysis is being transformed in an Era of Big Data. We argue that content analysis, from its beginning, has been concerned with extracting the main meanings of a text and mapping those meanings onto the space of a textual corpus. In contrast, we suggest that the emergence of new styles of text mining tools is creating an opportunity to develop a different kind of content analysis that we describe as a computational hermeneutics. Here the goal is to go beyond a mapping of the main meaning of a text to mimic the kinds of questions and concerns that have traditionally been the focus of a hermeneutically grounded close reading, a reading that focuses on what Kenneth Burke described as the poetic meanings of a text. We illustrate this approach by referring to our own work concerning the rhetorical character of US National Security Strategy documents.
TL;DR: Like the navigation tools that freed ancient sailors from the need to stay close to the shoreline—eventually affording the discovery of new worlds—Big Data might open us up to new sociological possibilities by freeing us from the shackles of hypothesis testing, but for that to happen forensic social science needs to happen.
Abstract: Like the navigation tools that freed ancient sailors from the need to stay close to the shoreline—eventually affording the discovery of new worlds—Big Data might open us up to new sociological possibilities by freeing us from the shackles of hypothesis testing. But for that to happen we need forensic social science: the careful compilation of evidence from unstructured digital traces as a means to generate new theories.
TL;DR: In this article, the authors relate some research experiences and reflect upon data construction and the links between theory, data, and methods for empirical social research, which is challenging and very rewarding.
Abstract: Working with computational methods and large textual analysis has been challenging and very rewarding—with all the ups and downs that doing empirical social research entails. In my contribution, I relate some research experiences and reflect upon data construction and the links between theory, data, and methods.
TL;DR: It is argued that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work.
Abstract: Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already embedded in previously collected data and built tools. These decisions refer to the recording, indexing and representation of data and the settings for analysis methods. While these choices can have tremendous impact on research outcomes, they are not often obvious, not considered or not being made explicit. Consequently, our awareness and understanding of the impact of these decisions on analysis results and derived implications are highly underdeveloped. This might be attributable to occasional high levels of over-confidence in computational solutions as well as the possible yet questionable assumption that Big Data can wash out minor data quality issues, among other reasons. This article provides examples for how to address this issue. It argues that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work. Scrutinizing data for accuracy issues, systematically fixing them and diligently documenting these processes can have another positive side effect: Closely interacting with the data, thereby forcing ourselves to understand their idiosyncrasies and patterns, can help us to move from being able to precisely model and formally describe effects in society to also understand and explain them.
TL;DR: In this article, the authors elaborate on this metaphor to highlight three relatively basic fallacies in the way we tend to think about Big Data: first, that they contain information on complete populations, or " N " =" all", and second, they contain recordings of naturalistic behavior.
Abstract: “Digital footprints” is an attractive, useful, and increasingly popular metaphor for thinking about Big Data. In this essay, I elaborate on this metaphor to highlight three relatively basic fallacies in the way we tend to think about Big Data: first, that they contain information on complete populations, or “ N = all”; second, that they contain recordings of naturalistic behavior; and third, that they can be understood devoid of context.
TL;DR: Wikipedia is taken as an instance Wikipedia's evolving representation of the field of sociology and sociologists, including such gendered aspects as male and female scholars and topics associated with masculinity and femininity.
Abstract: Wikipedia is an important instance of “Big Data,” both because it shapes people's frames of reference and because it is a window into the construction—including via crowd-sourcing—of new bodies of knowledge. Based on our own research as well as others' critical and ethnographic work, we take as an instance Wikipedia's evolving representation of the field of sociology and sociologists, including such gendered aspects as male and female scholars and topics associated with masculinity and femininity. Both the gender-specific dynamics surrounding what counts as “notability” on the online encyclopedia and Wikipedia's relative categorical incoherence are discussed. If “Big Data” can be said to construct its own object, it is, in this instance, a curious and lop-sided one, exemplifying pitfalls as well as promise with respect to more accurate and democratic forms of knowledge.
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TL;DR: DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common?
Abstract: DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But no data, big or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable.
TL;DR: In this article, the authors investigate whether geographically localised, or hyperlocal, uses of Twitter succeed in creating peer-to-peer neighbourhood networks or simply act as broadcast media at a reduced scale.
Abstract: This paper asks whether geographically localised, or ‘hyperlocal’, uses of Twitter succeed in creating peer-to-peer neighbourhood networks or simply act as broadcast media at a reduced scale. Literature drawn from the smart cities discourse and from a UK research project into hyperlocal media, respectively, take on these two opposing interpretations. Evidence gathered in the case study presented here is consistent with the latter, and on this basis we criticise the notion that hyperlocal social media can be seen as a community in itself. We demonstrate this by creating a network map of Twitter followers of a popular hyperlocal blog in Brockley, southeast London. We describe various attributes of this network including its average degree and clustering coefficient to suggest that a small and highly connected cluster of visible local entities such as businesses form a clique at the centre of this network, with individual residents following these but not one another. We then plot the locations of these entities and demonstrate that sub-communities in the network are formed due to close geographical proximity between smaller sets of businesses. These observations are illustrated with qualitative evidence from interviews with users who suggest instead that rather than being connected to one another they benefit from what has been described as ‘neighbourhood storytelling’. Despite the limitations of working with Twitter data, we propose that this multi-modal approach offers a valuable way to investigate the experience of using social media as a communication tool in urban neighbourhoods.
TL;DR: In this paper, a series of new data-sets that integrate approximately 30 years of survey data on victimisation, fear of crime and disorder and social attitudes with indicators of socioeconomic conditions and policy outcomes in Britain are presented.
Abstract: Bold approaches to data collection and large-scale quantitative advances have long been a preoccupation for social science researchers. In this commentary we further debate over the use of large-scale survey data and official statistics with ‘Big Data’ methodologists, and emphasise the ability of these resources to incorporate the essential social and cultural heredity that is intrinsic to the human sciences. In doing so, we introduce a series of new data-sets that integrate approximately 30 years of survey data on victimisation, fear of crime and disorder and social attitudes with indicators of socio-economic conditions and policy outcomes in Britain. The data-sets that we outline below do not conform to typical conceptions of ‘Big Data’. But, we would contend, they are ‘big’ in terms of the volume, variety and complexity of data which has been collated (and to which additional data can be linked) and ‘big’ also in that they allow us to explore key questions pertaining to how social and economic policy c...
TL;DR: In this paper, Lippmann published The Phantom Public, denouncing the "mystical fallacy of democracy." Decrying romantic democratic models that privilege self-governance, he writes: "I have not hap...
Abstract: In 1927, Walter Lippmann published The Phantom Public, denouncing the ‘mystical fallacy of democracy.’ Decrying romantic democratic models that privilege self-governance, he writes: “I have not hap...
TL;DR: In this article, the authors argue that the effects of Big Data on the practice of historical social science may be more limited than one might wish, since such accounts are the norm.
Abstract: “Big Data” can revolutionize historical social science if it arises from substantively important contexts and is oriented towards answering substantively important questions. Such data may be especially important for answering previously largely intractable questions about the timing and sequencing of events, and of event boundaries. That said, “Big Data” makes no difference for social scientists and historians whose accounts rest on narrative sentences. Since such accounts are the norm, the effects of Big Data on the practice of historical social science may be more limited than one might wish.