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In spite of these obstacles, some experiments were conducted; however their results were often published too late for the election in question. The field experiments conducted in , demonstrating that face—to—face canvassing was most effective for turnout, were published three years later Green, et al.

These experiments increased awareness that many methods that campaigns traditionally spent money on for example, slick mailers or phone calls were not very effective. A culture of experimentation was encouraged and embraced. The rise of digital platforms allowed incorporating real—time experimentation into the very act delivery of the political message.

International Journal of Big Data Intelligence

The results are measured in real time and quickly integrated into the delivery as the winning message becomes the message. Methodologically, of course, this is traditional experimental science but it has become possible because campaigns now partially take place over a medium that allows for these experimental affordances: cheap delivery of messages, immediate measurement, ability to randomize recipients, and quick turnaround of results which can then be applied to the next round. The Obama campaign had incorporated experiments into its methods as early as For example, in December , when the Obama campaign was still in its early stages, the campaign created 24 different button and media combinations for its splash page the first page that visitors land on.

Each variation were seen by 13, people — an incredibly large number for running a field experiment by old standards, but a relatively easy and cheap effort in the digital age Siroker, Through such experimentation, the Obama campaign was led to predominantly feature his family in much campaign material.

The increasing digitization of political campaigns as well as political acts by ordinary people provides a means through which political campaigns can now carry out such experiments with ease and effectiveness. These platforms operate via algorithms the specifics of which are mostly opaque to people outside the small cadre of technical professionals within the company with regards to content visibility, data sharing and many other features of political consequence. These proprietary algorithms determine the visibility of content and can be changed at will, with enormous consequences for political speech.

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Similarly, non—profits that relied on Facebook to reach their audiences faced a surprise in — The implications of opaque algorithms and pay—to—play are multiple: first, groups without funds to promote their content will become hidden from public view, or will experience changes to their reach that are beyond their ability to control. Second, since digital platforms can deliver messages individually — each Facebook user could see a different message tailored to her as opposed to a TV ad that necessarily goes to large audiences — the opacity of algorithms and private control of platforms alters the ability of the public to understand what is ostensibly a part of the public sphere, but now in a privatized manner.

Campaigns can access this data either through favorable platform policies which grant them access to user information. These private platforms can make it easier or harder for political campaigns to reach such user information, or may decide to package and sell data to campaigns in ways that differentially empower the campaigns, thus benefiting some over others. Further, a biased platform could decide to use its own store of big data to model voters and to target voters of a candidate favorable to the economic or other interests of the platform owners.

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  • Such a platform could help tilt an election without ever asking the voters whom they preferred gleaning that information instead through modeling, which research shows is quite feasible and without openly supporting any candidate. A similar technique could be possible for search results. Ordinary users often never visit pages that are not highlighted on the first page of Google results and researchers already found that a slight alteration of rankings could affect an election, without voter awareness Epstein and Robertson, Big—data driven computational politics engenders many potential consequences for politics in the networked era.

    In this section, I examine three aspects: deep and individualized profiling and targeting; opacity of surveillance; and, assault on idea of a Habermasian public sphere. First, the shift to tailored, individualized messaging based on profiling obtained through modeling brings potential for potential significant harms to civic discourse. Howard and Hillygus and Shields had already presciently warned of the dangers of data—rich campaigns. However, these can be double—edged for campaigns in that they elicit significant passion on all sides.

    Hence, campaigns aim to put wedge issues in front of sympathetic audiences while hiding them from those who might be motivated in other directions Hillygus and Shields, ; Howard, Until now, the ability to do just that has so far been limited by availability of data finding the exact wedge voter and means to target individuals Barocas, Prevalence of wedge issues is further damaging in that it allows campaigns to remain ambiguous on important but broadly relevant topics economy, education while campaigning furiously but now also secretly on issues that can mobilize small, but crucial, segments.

    It can also incorporate psychographic profiles modeled from online social data — data collected without directly interfacing with an individual. Hence, fear—mongering messages can be targeted only to those motivated by fear. Unlike broadcast, such messages are not visible to broad publics and thus cannot be countered, fact—checked or otherwise engaged in the shared public sphere the way a provocative or false political advertisement might have been.

    This form of big data—enabled computational politics is a private one. At its core, it is opposed to the idea of a civic space functioning as a public, shared commons. It continues a trend started by direct mail and profiling, but with exponentially more data, new tools and more precision. The second negative effect derives from information asymmetry and secrecy built into this mode of computational politics. While the observational aspect is similar, computational politics is currently exercised in a manner opposite of the panopticon.

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    • The panopticon operates by making very visible the act and possibility of observation, while hiding actual instances of observation, so that a prisoner never knows if she is being watched but is always aware that she could be. Modern social engineering operates by making surveillance as implicit, hidden and invisible as possible, without an observed person being aware of it [ 10 ]. While browsers, cell phone companies, corporate and software companies, and, as recently revealed, the U.

      This model of hegemony is more in line with that proposed by Gramsci which emphasizes manufacturing consent, and obtaining legitimacy, albeit uses state and other resources in an unequal setting, rather than using force or naked coercion. Research shows that people respond more positively to messages that they do not perceive as intentionally tailored to them, and that overt attempts are less persuasive than indirect or implicit messages. Political campaigns are acutely aware of this fact.

      When they see our fingerprints on this stuff, they believe it less. The public is constituted unequally; the campaign knows a great deal about every individual while ordinary members of the public lack access to this information. Even when identity information is not embedded into a platform such as Twitter where people can and do use pseudonyms , identity often cannot be escaped.

      Big data approaches to decomposing heterogeneity across the autism spectrum

      Modeling can ferret out many characteristics in a probabilistic but highly reliable manner Kossinki, Commercial databases which match computer IP to actual voter names for an overwhelming majority of voters in the United States Campaign Grid, ; U. Federal Trade Commission are now available.

      Thus, political campaigns with resources can now link individual computers to actual users and their computers without the consent. Big data makes anonymity difficult to maintain, as computer scientists have shown repeatedly Narayanan and Shmatikov, Given enough data, most profiles end up reducing to specific individuals; date of birth, gender and zip code positively correlate to nearly 90 percent of individuals in the United States. On the surface, this century has ushered in new digital technologies that brought about new opportunities for participation and collective action by citizens.

      Social movements around the world, ranging from the Arab uprisings to the Occupy movement in the United States Gitlin, , have made use of these new technologies to organize dissent against existing local, national and global power [ 11 ]. Such effects are real and surely they are part of the story of the rise of the Internet. However, history of most technologies shows that those with power find ways to harness the power of new technologies and turn it into a means to further their own power Spar, From the telegraph to the radio, the initial period of disruption was followed by a period of consolidation in which challengers were incorporated into transformed power structures, and disruption gave rise to entrenchment.

      The dynamics outlined in this paper for computational politics require access to expensive proprietary databases, often controlled by private platforms, and the equipment and expertise required to effectively use this data. At a minimum, this environment favors incumbents who already have troves of data, and favors entrenched and moneyed candidates within parties, as well as the data—rich among existing parties.

      The trends are clear. The methods of computational politics will, and already are, also used in other spheres such as marketing, corporate campaigns, lobbying and more. The six dynamics outlined in this paper — availability of big data, shift to individual targeting, the potential and opacity of modeling, the rise of behavioral science in the service of persuasion, dynamic experimentation, and the growth of new power brokers on the Internet who control the data and algorithms — will affect many aspects of life in this century.

      More direct research, as well as critical and conceptual analysis, is crucial to increase both our understanding and awareness of this information environment, as well as to consider policy implications and responses. Similar to campaign finance laws, it may be that data use in elections needs regulatory oversight thanks to its effects on campaigning, governance and privacy.

      Starting an empirically informed, critical discussion of data politics now may be the first important step in asserting our agency with respect to big data that is generated by us and about us, but is increasingly being used at us. E—mail: zeynep [at] unc [dot] edu. The political advertisement climate, and the need to advertise on broadcast, arguably, has a stronger effect in determining who can be a candidate in the first place, and not so much in selecting a winner among those who make it to that level.

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      Constine, In contrast, an online depository of books by leading large research libraries in the world contain a mere 78 terabytes of information in total Anderson, As Bryant and Raja astutely point out, this kind of analysis can be double—edged sword. The number of votes that needed to flip to change outcome in the Presidential election was about a mere , distributed in the right states.

      An piece published just as this paper was about to go to press suggested a similar scenario, and called it digital gerrymandering Zittrain, While the latest NSA revelations due to leaks by Edward Snowden may change that, the level of surprise and outrage they generated speaks to both lack of awareness of surveillance as well as efforts to keep it hidden. Nate Anderson, Solon Barocas, Robert M.

      Bond, Christopher J. Fariss, Jason J. Jones, Adam D. Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler, Edward L.

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      Most recent articles RSS View all articles. A survey of open source tools for machine learning with big data in the Hadoop ecosystem Authors: Sara Landset, Taghi M. Aims and scope The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research.

      The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Submit manuscript. Editorial Board Sign up for article alerts and news from this journal. Follow Follow us on Twitter Follow us on Facebook.

      Two important questions remain, however, concerning this type of learning: 1 which neural structures and mechanisms are involved in acquiring and exploiting such contextual knowledge? We now answer both these questions after closely examining behaviour and recording neural activity using magneto-encephalography MEG while observers male and female were acquiring and exploiting statistical regularities. Computational modelling of behavioural data suggested that after repeated exposures to a spatial context, participants' behaviour was marked by an abrupt switch to an exploitation strategy of the learnt regularities.

      MEG recordings showed that hippocampus and prefrontal cortex were involved in the task; and furthermore revealed a striking dissociation: only the initial learning phase was associated with hippocampal theta band activity, while the subsequent exploitation phase showed a shift in theta band activity to the prefrontal cortex. Intriguingly, the behavioural benefit of repeated exposures to certain scenes was inversely related to explicit awareness of such repeats, demonstrating the implicit nature of the expectations acquired. Taken together, these findings demonstrate 1a that hippocampus and prefrontal cortex play complementary roles in the implicit, unconscious learning and exploitation of spatial statistical regularities; 1b that these mechanisms are implemented in the theta frequency band; and 2 that contextual knowledge can indeed be acquired unconsciously, and that awareness of such knowledge can even interfere with the exploitation thereof.