No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
How President Obama's campaign used big data to rally individual voters
Abstract
The Obama 2012 campaign used data analytics and the experimental method to assemble a winning coalition vote by vote. In doing so, it overturned the long dominance of TV advertising in U.S. politics and created something new in the world: a national campaign run like a local ward election, where the interests of individual voters were known and addressed. One way the campaign sought to identify the ripest targets was through a series of what the Analyst Institute called 'experiment-informed programs,' or EIPs, designed to measure how effective different types of messages were at moving public opinion. The Obama team found that voters between 45 and 65 were more likely to change their views about the candidates after hearing Obama's Medicare arguments than those over 65, who were currently eligible for the program. Analysts identified their attributes and made them the core of a persuasion model that predicted, on a scale of 0 to 10, the likelihood that a voter could be pulled in Obama's direction after a single volunteer interaction.
... Through this feature, LinkedIn observed that most of its suggestions in inviting people were successful [352]. Similarly, in the year 2012, for the US presidential elections, President Obama's campaign experienced massive boost and success through predictive analysis using a big dataset consisting of voter's profiles, their likes, and their patterns [207]. ...
... Examples of big data and its impact in analytics and predictions have been discussed in references [207,236]. [191] provides further discussion on analytics and the size challenges for big data. CAP Theorem and its impact on design of big data been discussed in many research papers. ...
... So ermittelten Obamas Wahlkampfplaner 2008 basierend auf Wahl-, Marktforschungs-und Zensusdaten, an welche Türen sie ihre Helfer entsenden mussten, um Wechselwähler zu überzeugen und wahlmüde Stammwähler zu aktivieren. Die Daten, die die Wahlkampfhelfer damals selbst sammelten, bildeten eine wichtige Grundlange für die 2012er Direktkampagne (Pilkington 2012;Issenberg 2013). Datensammlung und darauf aufbauende Strategieanpassung werden dabei zunehmend miteinander verzahnt und automatisiert: Neben der Emanzipierung der Wahlkampforganisatoren von externen (Daten-)Anbietern lag eine zentrale Neuerungen der 2012er Kampagne in der Etablierung der Webplattform "Dashboard", auf die die freiwilligen Helfer von mobilen Endgeräten aus zugreifen konnten, um mit anderen Unterstützern in Kontakt zu treten, neue Zielpersonen und -orte in Erfahrungen zu bringen und ihre an den Haustüren erfassten personalisierten Daten einzuspeisen (Issenberg 2013;Burgard 2011). ...
... Die Daten, die die Wahlkampfhelfer damals selbst sammelten, bildeten eine wichtige Grundlange für die 2012er Direktkampagne (Pilkington 2012;Issenberg 2013). Datensammlung und darauf aufbauende Strategieanpassung werden dabei zunehmend miteinander verzahnt und automatisiert: Neben der Emanzipierung der Wahlkampforganisatoren von externen (Daten-)Anbietern lag eine zentrale Neuerungen der 2012er Kampagne in der Etablierung der Webplattform "Dashboard", auf die die freiwilligen Helfer von mobilen Endgeräten aus zugreifen konnten, um mit anderen Unterstützern in Kontakt zu treten, neue Zielpersonen und -orte in Erfahrungen zu bringen und ihre an den Haustüren erfassten personalisierten Daten einzuspeisen (Issenberg 2013;Burgard 2011). ...
Im Zentrum des vorliegenden Beitrags stehen drei Fragen: Erstens, welche Strategien verfolgen politische Akteure mit ihrer direkten Wahlkampfkommunikation? Zweitens, welche Ziele wollen sie damit erreichen? Drittens, wie setzen sie ihre Strategien in der Praxis um? Untersuchungsgegenstand ist der Thüringer Landtagswahlkampf 2014, im Fokus stehen der Haustürwahlkampf als traditionelle direkte Wahlkampfform sowie Social Media-Kommunikation als neuere, medienvermittelte Form direkter Wähleransprache. Ausgehend vom bisherigen Forschungsstand zu direkter Wahlkampfkommunikation unterscheiden wir die Strategien politischer Akteure hinsichtlich vier idealtypischer Foki: 1) einseitige Informationsvermittlung an die Wähler; 2) interpersonale Interaktion mit den Wählern; 3) Mobilisierung der Bürger bzw. Wähler zur politischen Partizipation; 4) längerfristiger Beziehungsaufbau zum Wähler. Die empirische Basis bildet eine qualitative, systematisierende Expertenbefragung mit den verantwortlichen Wahlkampfmanagern von CDU, SPD, Die Linke, Bündnis 90/Die Grünen und FDP. Als zentrales Ergebnis lässt sich festhalten, dass politische Akteure im Landtagswahlkampf 2014 auch in ihrer direkten Wähleransprache online und offline primär auf die einseitige Informationsvermittlung an den Wähler fokussierten. Neben begrenzten personellen Ressourcen und der Angst vor Kontrollverlust dürfte das „Wählerbild“, das der Kampagnenplanung politischer Parteien zugrunde liegt, maßgeblich hierfür sein.
... In the 2008 presidential election, the Democratic National Committee (DNC) of USA uses their mobilization programs among supporters, to cover up the participation among citizens, stakeholders, etc. and to pre-determine the statistics of election status. Obama uses Web based platforms, sharing through social media, and smart phones for his supporters to make them participate in the political processes of his election campaign for each voters [13]. P-Gov refers to governance technology for political parties, in the extension of e-Governance for political party management as well as the strategic use of political activities, which can be evaluate through web services, social media, or mobile services (using SMS or mobile app). ...
... To register, mobilize, or persuade any supporter during campaign of a party, using a mobile application could be a better solution for them. Obama uses this technology for volunteer activity of most active supporters, canvassers, citizens, stakeholders to make them notice about his approach, to make them hear his speech and taking back a statistical report in return, such as rating, from them as suggestions without ringing the doorbell for their home during the campaigning period [13]. ...
Information and Communication Technology (ICT) has been playing a pivotal role since the last decade in developing countries that brings citizen services to the doorsteps and connecting people. With this aspiration ICT has introduced several technologies of citizen services towards all categories of people. The purpose of this study is to examine the Governance technology perspectives for political party, emphasizing on the basic critical steps through which it could be operationalized. We call it P-Governance. P-Governance shows technologies to ensure governance, management, interaction communication in a political party by improving decision making processes using big data. P-Governance challenges the competence perspective to apply itself more assiduously to operationalization, including the need to choose and give definition to one or more units of analysis (of which the routine is a promising candidate). This paper is to focus on research challenges posed by competence to which P-Governance can and should respond include different strategy issues faced by particular sections. Both the qualitative as well as quantitative research approaches were conducted. The standard of citizen services, choice & consultation, courtesy & consultation, entrance & information, and value for money have found the positive relation with citizen's satisfaction. This study results how can be technology make important roles on political movements in developing countries using big data.
... In 2008, for example, the Obama campaign had used survey data to assign every voter a probability of voting and a probability of supporting Obama. Using continually updated data from call centers, the campaign kept its models up to date and adjusted advertising and field communication accordingly (Issenberg, 2013). The practices of 2012, however, represented a leap beyond previous "micro" messaging toward the modeling of multiple behaviors of citizens using dozens of predictor variables at a new scale . ...
... In July, Slate (Issenberg, 2012b) reported that the Romney campaign was attempting to reverse-engineer what Obama was doing-a practice typically associated with following rather than leading in technological innovation. After the election, Brent McGoldrick, a member of Romney's data science group, acknowledged that his campaign was unable to interpret some of Obama's ad buys or to understand how Obama was targeting his messages (Issenberg, 2013). In a post-election meeting of the International Association of Political Consultants, Brian Jones, senior communications advisor to the Romney campaign, conceded that the Obama data operation was more sophisticated than their own had been (Cramer, 2012). ...
This essay provides a descriptive interpretation of the role of digital media in the campaigns of Barack Obama in 2008 and 2012 with a focus on two themes: personalized political communication and the commodification of digital media as tools. The essay covers campaign finance strategy, voter mobilization on the ground, innovation in social media, and data analytics, and why the Obama organizations were more innovative than those of his opponents. The essay provides a point of contrast for the other articles in this special issue, which describe sometimes quite different campaign practices in recent elections across Europe.
... In the 2008 presidential election, the Democratic National Committee (DNC) of USA uses their mobilization programs among supporters, to cover up the participation among citizens, stakeholders, etc. and to pre-determine the statistics of election status. Obama uses Web based platforms, sharing through social media, and smart phones for his supporters to make them participate in the political processes of his election campaign for each voters [13]. P-Gov refers to governance technology for political parties, in the extension of e-Governance for political party management as well as the strategic use of political activities, which can be evaluate through web services, social media, or mobile services (using SMS or mobile app). ...
... To register, mobilize, or persuade any supporter during campaign of a party, using a mobile application could be a better solution for them. Obama uses this technology for volunteer activity of most active supporters, canvassers, citizens, stakeholders to make them notice about his approach, to make them hear his speech and taking back a statistical report in return, such as rating, from them as suggestions without ringing the doorbell for their home during the campaigning period [13]. ...
Information and Communication Technology (ICT) has been playing a pivotal role since the last decade in developing countries that brings citizen services to the doorsteps and connecting people. With this aspiration ICT has introduced several technologies of citizen services towards all categories of people. The purpose of this study is to examine the Governance technology perspectives for political party, emphasizing on the basic critical steps through which it could be operationalized. We call it P-Governance. P-Governance shows technologies to ensure governance, management, interaction communication in a political party by improving decision making processes using big data. P-Governance challenges the competence perspective to apply itself more assiduously to operationalization, including the need to choose and give definition to one or more units of analysis (of which the routine is a promising candidate). This paper is to focus on research challenges posed by competence to which P-Governance can and should respond include different strategy issues faced by particular sections. Both the qualitative as well as quantitative research approaches were conducted. The standard of citizen services, choice & consultation, courtesy & consultation, entrance & information, and value for money have found the positive relation with citizen’s satisfaction. This study results how can be technology make important roles on political movements in developing countries using big data.
... Finally, in the late 1990s, the Voter Vault, As a result, Democratic Party found Obama blow out his opponents and reelected as US president, which was not anticipated by any press and media. It is considered that the victory was attributable to Big data-based election strategy that claimed to the slogan 'Any of election should be measured by numbers [4,11]. ...
... In addition, election was simulated 66,000 times every night by computer-based simulator. The simulation results were analyzed and monitored for change in every and each part of the whole country and manpower and fields to be focused were decided on the basis of the result every morning[2,4]. ...
The victory of Barack Obama in the presidential reelection, in which he got closer to voters by scientific election strategy based on data, is making a new paradigm of this scientific election mechanism. But it is within bounds to say that Korean election has developed based on emotional confrontation, rather than on the confrontation of policy or personal qualification. This study suggests a Big data-based election campaign strategy in an effort to reduce the harmful consequences of Korean election and to settle down a desirable campaign culture. To do so, this study examines the actual status and problems of Korean politics and election campaign. And then it designs a Korean election strategy model using Big data as an alternative to break through the problems. Last, it discusses the plan to utilize Big data.
... L'influence américaine est souvent soulignée tant par les acteurs de terrain, que par les chercheurs (Gerstlé, 2007). La campagne numérique de B. Obama en 2008 (Kreiss, 2012 ;Issenberg, 2013) préside ainsi au développement de la croyance autour du « pouvoir » des outils numériques pour rationaliser le travail militant et faire la différence dans les urnes. Le travail des « passeurs » est non seulement de promouvoir ces outils, mais aussi de faire croire en leur efficacité en réactivant des mythes électoraux (Baldwin-Philippi, 2017 ;Theviot, 2018). ...
... Pero a algunos, la realidad no les va a estropear una buena historia y, para expresarlo de manera escueta y exenta de toda exageración, se desató en los medios la "fiebre" Big Data. Baste para ilustrarlo lo expresado apenas un mes más tarde por Issenberg (2012): "Detrás de todo eso hubo puntuaciones describiendo votantes concretos: una nueva divisa política que predijo el comportamiento de sujetos humanos. La campaña no solo sabía quién eras; supo exactamente cómo convertirte en el tipo de persona que quería que fueses" (la cursiva ha sido añadida). ...
Big Data ha irrumpido como una prometedora infraestructura tecnológica, garantía de grandes progresos en ciencias sociales. La recogida masiva de información sobre hábitos y características de los usuarios de dispositivos móviles se propone como clave para una descripción precisa de la sociedad y sus individuos. No obstante, el entusiasmo ante esta nueva fuente de datos descubierta y su posibilidad de interconexión (redes sociales, compras online, etc.) podría suponer una nada desdeñable amenaza para un adecuado progreso científico, que diera origen a cierto neoqueteletismo tecnológico que confunda la datificacióndel mundo con su comprensión. Surge así el riesgo de transferir la atención a los datos, desatendiéndose contexto y metodología. Big Data puede suponer una importante contribución, pero una mala comprensión podría tornarla engañosa
... In today's world, such hidden mathematics is constitutive for numerous applications of automated decision-making processes. Applications such as suggestions of content or advertisements on online platforms, diagnoses in healthcare, evaluation of legal issues (Dressel & Farid, 2018) or even support for election campaigns (Issenberg, 2012) are implemented through automatised decision-making systems. The use of automated decision-making is accompanied by a social discourse, which is partially characterised by euphoric exaggeration of the performance and the value on the one hand and anxious rejection of so-called "Frankenstein algorithms" on the other (sueddeutsche. ...
In the setting of design-based research, the second version of an experimental course on data science is implemented accompanied by research. The three modules of the course focus on “data and data detectives”, “machine learning” and a combination of both in working on a final project. In this paper, we will focus on the topic “decision trees” which is part of “machine learning”. The students learn approaches of how to build decision trees manually from data using the tree plugin of CODAP. Further on, they learn to design and code an algorithm with Python that automatically generates trees. Afterwards, the algorithm is applied to real data sets with the support of Jupyter Notebooks. The instructional approach provides a deep content knowledge, which also serves as a basis for discussing the difference between humans’ and machines’ building decision trees and the societal implications of implementing them in practice.
... CSS studies on areas like social anthropology and political science can have significant negative impacts, such as the subversion of democracy, profiling of individuals or groups for the development of better manipulation or even brainwashing techniques (Oboler et al., 2012). It is known that during the last few presidential elections in the United States, social media data was actively utilized to shape the course of elections (Bessi & Ferrara, 2016;Issenberg, 2013;Sen, Flock, &Wagner, 2020). In that perspective, the power of conducting CSS studies on online data is unequivocal. ...
The immense impact of social media on contemporary cultural evolution is undeniable, consequently declaring them an essential data source for computational social science studies. Alongside the advancements in natural language processing and machine learning disciplines, computational social science researchers continuously adapt new techniques to the data collected from social media. Although these developments are imperative for studying the sociological transformations in many communities, there are some inconspicuous problems on the horizon. This chapter addresses issues that may arise from the use of social media data, like biased models. It also discusses various obstacles associated with machine learning methods while also providing possible solutions and recommendations to overcome these struggles from an interdisciplinary perspective. In the long term, this chapter will guide computational social science researchers in their future studies, from things to be aware of with data collection to assembling an accurate experimental design.
... Automated decision-making processes based on machine learning methods are relevant in many societal applications. Applications such as personalized advertisements on online platforms, diagnoses in healthcare, evaluation of legal issues [5], or even support for election campaigns [13] are implemented by data-driven decision models that are based on machine learning. The social discourse around automated decision-making is often characterized by a euphoric exaggeration of the performance and value on the one hand and anxious rejection of so-called "franken-algorithms" [18] on the other. ...
This paper reports on progress in the development of a teaching module on
machine learning with decision trees for secondary-school students, in which
students use survey data about media use to predict who plays online games
frequently. This context is familiar to students and provides a link between
school and everyday experience. In this module, they use CODAP's “Arbor”
plug-in to manually build decision trees and understand how to systematically
build trees based on data. Further on, the students use a menu-based environment
in a Jupyter Notebook to apply an algorithm that automatically generates
decision trees and to evaluate and optimize the performance of these. Students
acquire technical and conceptual skills but also reflect on personal and social
aspects of the uses of algorithms from machine learning.
... In today's world, such hidden mathematics is constitutive for numerous applications of automated decision-making processes. Applications such as suggestions of content or advertisements on online platforms, diagnoses in healthcare, evaluation of legal issues (Dressel & Farid, 2018) or even support for election campaigns (Issenberg, 2012) are implemented through automatised decision-making systems. The use of automated decision-making is accompanied by a social discourse, which is partially characterised by euphoric exaggeration of the performance and the value on the one hand and anxious rejection of so-called "Frankenstein algorithms" on the other (sueddeutsche. ...
In the setting of design-based research, the second version of an experimental course on data science is implemented accompanied by research. The three modules of the course focus on "data and data detectives", "machine learning" and a combination of both in working on a final project. In this paper, we will focus on the topic "decision trees" which is part of "machine learning". The students learn approaches of how to build decision trees manually from data using the tree plugin of CODAP. Further on, they learn to design and code an algorithm with Python that automatically generates trees. Afterwards, the algorithm is applied to real data sets with the support of Jupyter Notebooks. The instructional approach provides a deep content knowledge, which also serves as a basis for discussing the difference between humans' and machines' building decision trees and the societal implications of implementing them in practice. INTRODUCTION Mathematics is both part of our world and hidden in it (Heymann, 2003). In today's world, such hidden mathematics is constitutive for numerous applications of automated decision-making processes. Applications such as suggestions of content or advertisements on online platforms, diagnoses in healthcare, evaluation of legal issues (Dressel & Farid, 2018) or even support for election campaigns (Issenberg, 2012) are implemented through automatised decision-making systems. The use of automated decision-making is accompanied by a social discourse, which is partially characterised by euphoric exaggeration of the performance and the value on the one hand and anxious rejection of so-called "Frankenstein algorithms" on the other (sueddeutsche.de, 5.9.18). Decision trees represent one kind of model in the field of machine learning. With an increasing societal relevance, there is a growing demand for data-driven procedures to be taken up in school education (Biehler & Schulte, 2018; Engel, 2017; Ridgway, 2016). Automated decision models are linked to everyday experiences of students (e.g. personalised advertising) and dealing with underlying algorithms and mathematics allows for a re-evaluation and well-founded reflection on its opportunities and limitations in different contexts. This corresponds to the motif of "world orientation" in general mathematics education (Heymann, 2003) and can be helpful for people to understand the digitalised world and for an informed participation in a discourse on machine learning. Therefore, we created and implemented a teaching module on machine learning for upper secondary level in the setting of design-based research. In this paper, we present the part of this machine learning module about the method of decision trees. The target is to show our elementarisation of the content and to give insights in the practical implementation. An outlook on the accompanying research about potential obstacles to understanding is given as well.
... Such advisory services often require data scientists to use sophisticated techniques and data that their organization's BI platform provides. Famous examples in which data scientists have created organizational benefits include improving Netflix's movie recommendation algorithm by 10 percent (Netflix, 2019) and helping Barack Obama to secure the victory in the 2012 U.S. presidential campaign (Issenberg, 2012(Issenberg, , 2013. ...
... La información obtenida mediante Google Analytics, compra de datos agregados de usuarios segmentados de Facebook y el cruce con otras bases comerciales permitía al equipo rector de la campaña una distribución muy minuciosa de su plan general de actuaciones. Como llegara a escribir un destacado comentarista, "la campaña no sólo sabía quién eras, sino exactamente cómo tenías que cambiar para convertirte en el tipo de persona que ellos querían que fueras" (Issenberg, 2012). ...
El activismo digital y la analítica de datos aplicados en las elecciones presidenciales estadounidenses de 2008, 2012 y 2016 han consolidado la “gestión computacional de campañas” como un recurso estratégico que implica un cambio de paradigma en la concepción de la comunicación política electoral. La incorporación de esas mismas herramientas y prácticas de movilización y búsqueda del voto a los comicios nacionales españoles de 2015 y 2016 demuestra que la campaña ‘online’ también adquiere un rol creciente en otras democracias de desarrollo tecnopolítico todavía menor. Los principales cambios del nuevo paradigma son: la microsegmentación y geolocalización de sectores muy particularizados de electores para el posterior envío de mensajes ajustados a cada microperfil, el papel decisivo de los simpatizantes como intermediarios del contacto electoral en una “influencia en dos pasos digital” y la generación por los grupos políticos de un discurso llamativo para las redes sociales con el objetivo de esquivar su dependencia ante los hegemónicos medios de comunicación de masas. Entre las transformación más profundas que se perciben se identifica el “tecnocabildeo”, o práctica del clientelismo propio de los pequeños grupos, las comunidades primitivas y el siglo XIX, a través de herramientas del siglo XXI.
... 12 Uplift modeling has also been used for selecting customers to include in retention campaigns and as such to maximize the effect of a retention campaign, by identifying customers whose probability to churn is most reduced by offering an incentive to remain loyal. 13 Uplift modeling has also been adopted in politics, 14,15 for identifying swing voters to target in election campaigns. An overview of various applications of uplift modeling is provided in the work of Siegel. ...
Prescriptive analytics extends on predictive analytics by allowing to estimate an outcome in function of control variables, allowing as such to establish the required level of control variables for realizing a desired outcome. Uplift modeling is at the heart of prescriptive analytics and aims at estimating the net difference in an outcome resulting from a specific action or treatment that is applied. In this article, a structured and detailed literature survey on uplift modeling is provided by identifying and contrasting various groups of approaches. In addition, evaluation metrics for assessing the performance of uplift models are reviewed. An experimental evaluation on four real-world data sets provides further insight into their use. Uplift random forests are found to be consistently among the best performing techniques in terms of the Qini and Gini measures, although considerable variability in performance across the various data sets of the experiments is observed. In addition, uplift models are frequently observed to be unstable and display a strong variability in terms of performance across different folds in the cross-validation experimental setup. This potentially threatens their actual use for business applications. Moreover, it is found that the available evaluation metrics do not provide an intuitively understandable indication of the actual use and performance of a model. Specifically, existing evaluation metrics do not facilitate a comparison of uplift models and predictive models and evaluate performance either at an arbitrary cutoff or over the full spectrum of potential cutoffs. In conclusion, we highlight the instability of uplift models and the need for an application-oriented approach to assess uplift models as prime topics for further research.
... 'Big data' has had a significant impact in a variety of disciplines, such as transportation, 1 economics and politics. 2 Healthcare has been slow to leverage the growth of 'big data', such as patient information routinely collected through digitalised medical records. 3 Secondary use of routinely collected electronic medical record (EMR) data could improve sample size, study efficiency, data quality and reduce selection bias in clinical research, which is often limited by traditional opt-in consent processes. ...
Research has been slow to leverage digitalised medical records as a data resource. Our study assessed patient acceptability of opt-out consent for secondary use of digital patient data. A questionnaire was distributed to patients in multiple languages and with an interpreter. Of 919 completed surveys, 33% were of non-English speaking background, 15% self-reported cognitive impairment and 3% were refugees. Opt-out consent was accepted in this diverse population; 87% of participants approved, or were indifferent to opt-out consent. Gender, employment and cognition status were not significant determinants of acceptability.
... The votes may have been cast via secret ballot, but because Obama's analysts had come up with individual-level predictions, they could look at the Democrat's vote totals in each precinct and identify the people most likely to have backed him. ( Issenberg 2012) Four years later the Obama campaign created Narwhal, a software program that combined and merged data collected from multiple databases and financial sources. The Obama campaign began with a 10 Terabyte database that grew to 50 Terabytes by the end of the campaign ( Nickerson and Rogers 2014). ...
Survey data have recently been compared and contrasted with so-called “Big Data” and some observers have speculated about how Big Data may eliminate the need for survey research. While both Big Data and survey research have a lot to offer, very little work has examined the ways that they may best be used together to provide richer datasets. This chapter offers a broad definition of Big Data and proposes a framework for understanding how the benefits and error properties of Big Data and surveys may be leveraged in ways that are complementary. This chapter presents several of the opportunities and challenges that may be faced by those attempting to bring these different sources of data together.
... Previous academic research has documented that survey polls only provide broadbrush estimates but that, in most elections, individual voter scores can provide a more microscopic view of the electorate. 4 In another review of the literature, authors Nickerson and Rogers 5 describe a data science arms race within political campaigns. In their telling, there is a widespread usage of traditional statistical techniques such as ordinary least squares and logistic regression, but also an understanding that these methods are both too dependent on the skill of the analyst and may not apply to different regions, issues, or campaigns. ...
The problem of accurately predicting vote counts in elections is considered in this article. Typically, small-sample polls are used to estimate or predict election outcomes. In this study, a machine-learning hybrid approach is proposed. This approach utilizes multiple sets of static data sources, such as voter registration data, and dynamic data sources, such as polls and donor data, to develop individualized voter scores for each member of the population. These voter scores are used to estimate expected vote counts under different turnout scenarios. The proposed technique has been tested with data collected during U.S. Senate and Louisiana gubernatorial elections. The predicted results (expected vote counts, predicted several days before the actual election) were accurate within 1%.
... 12 Uplift modeling has also been used for selecting customers to include in retention campaigns and as such to maximize the effect of a retention campaign, by identifying customers whose probability to churn is most reduced by offering an incentive to remain loyal. 13 Uplift modeling has also been adopted in politics, 14,15 for identifying swing voters to target in election campaigns. An overview of various applications of uplift modeling is provided in the work of Siegel. ...
This chapter introduces uplift modeling approaches, which aim at estimating the net effect of a treatment, such as a marketing campaign, on customer behavior. Uplift models allow users to optimize the selection of customers to include in marketing campaigns as well as a further customization at the individual customer level of the campaign design, for example, in terms of the contacting channel and the characteristics of the incentive that is offered. Such customization may even further increase the effect and return of the campaign. For uplift modeling, it is necessary to actively gather the required data by means of well-designed experiments or, alternatively, to passively gather the required data by tracking information on marketing campaigns at the customer level. The chapter discusses two regression-based approaches: Lo's method is based on logistic regression, whereas Lai's method and the generalized Lai method reformulate the uplift modeling problem to allow standard approaches.
... Many researchers have successfully showed the usage social media as valuable data resource. When combined with data mining technology, it can give researchers valuable insights in a significantly reduced time [1][2][3]. ...
Social Network Analysis (SNA) has become a common tool to conduct social and business research. In marketing SNA is used to measure word of mouth of a marketing campaign. For an example, a good marketing campaign should create intensive conversation between users in social media. In this paper we use SNA metrics to find out if we can predict brand awareness. We crawl conversation data from Twitter to form seven graph of seven brand in Indonesia. We use multiple regression method, an extension of linear regression, to analyse network properties to get insight on how network structure affect brand awareness of a product. Even though this research is still in early stage, but we manage to discover that a good network structure in knowledge dissemination case (such as word of mouth) eventually differ with the one in brand awareness.
... In the past decade, machine learning researchers have championed data-driven decision making in place of oft-fallible human intuition. This approach has revolutionized the way we design and evaluate the effectiveness of business practices (Brynjolfsson et al., 2011;Kohavi et al., 2009), advertisements (Breese et al., 1998, and political campaigns (Issenberg, 2013). Gun violence policy should be no different. ...
... From the topic of Big Data analysis, a lot of R&D subjects had been drew attention such as data gathering, data reducing, data classification, data association, data prediction and etc. Also, the usage of Big Data is quite diversified and complex in reason of variable types of required areas like personal [1], social [2], medical [3] or even political [4] or militarized [5] field and this situation made few of standardizations or heuristic frameworks or structures for the Big Data analysis had been represented. ...
With incoming data era from early 2010's, the word “Big Data” has been taken focus in many of research and development areas. The final purpose of Big Data research is the efficient utilization of useful information; however, the process involves a lot of complex subjects like data processing, data mining, and etc. In this paper, we summarize and discuss the subjects in area of Big Data analysis, for a clearer understanding of scientific challenges in Big Data researches.
... Furthermore, the Obama campaign hosted, amongst others, events such as "Dinner with Barack," or "Dinner with Barack and Joe," or "Dinner with Barack and Michelle" during the 2012 campaign to engage with voters on a personal level (Dinner with Barack and Michelle 2012). In 2012, the Obama campaign intensified its 2008 pioneering use of social media (Issenberg 2012). As social media created a platform for the Obama campaign to interact with individual voters, the campaign achieved double the Facebook "Likes" and nearly 20 times as many re-tweets as the Romney campaign (Rutledge 2013); creating familiarity of President Obama among individual voters. ...
... Over the past years online political discussions have received a lot of attention. E.g. the Obama 2012 election team initiated an extensive use of text analytics and machine learning techniques towards online material to guide advertising campaigns, identifying key voters, and improve fundraising (Issenberg, 2012). There has also been a lot of concern about the alarming growth in hate and racism against minorities like Muslims, Jews and Gypsies in online discussions (Goodwin et al., 2013;Bartlett et al., 2013). ...
... Physics has its applications too. For example, the European Organization for Nuclear Research (CERN) built the largest and most powerful particle collider, the Large Hadron Collider (LHC) [ [19]. ...
Big Data is characterized by large data sets and compute-intensive applications. Examples include computational biology applications such as genome or DNA sequencing, proteomics, computational neuroscience, computational pharmacology and metagenomics. Physics, business, and government also have many applications. Such data and corresponding applications present a challenge to traditional storage and computing solutions. This is in addition to the problem of sharing such a large amount of data among researchers in a controlled fashion. Cloud computing is a promising solution that offers unlimited on-demand elastic storage and compute capacity at an affordable cost. The purpose of this paper is to discuss opportunities and challenges of using cloud computing for processing Big Data. Additionally, it provides a comprehensive survey of existing tools for Big Data and classifies them using a criterion specific for Big Data. Example applications utilizing these tools are also provided.
... On an anecdotal level, the realities of modern election campaigns are a good example of this new idea of tailored persuasion. In the 2012 presidential race, for instance, Barack Obama employed a team of statisticians and social media strategists to mine large amounts of data on individual voters in order to develop more effective modes of persuading voters to adopt issue stances, donate money or turn out on Election Day (Issenberg, 2012). There is also some initial experimental evidence suggesting that the same tendency to self-select into highly homogenous social networks that produces preference-based reinforcement, as discussed above, might also promote the exchange of belief-inconsistent information among audiences once that information does enter their network. ...
Framing has become one of the most popular areas of research for scholars in communication and a wide variety of other disciplines, such as psychology, behavioral economics, political science, and sociology. Particularly in the communication discipline, however, ambiguities surrounding how we conceptualize and therefore operationalize framing have begun to overlap with other media effects models to a point that is dysfunctional. This article provides an in-depth examination of framing and positions the theory in the context of recent evolutions in media effects research. We begin by arguing for changes in how communication scholars approach framing as a theoretical construct. We urge scholars to abandon the general term “framing” altogether and instead distinguish between different types of framing. We also propose that, as a field, we refocus attention on the concept's original theoretical foundations and, more important, the potential empirical contributions that the concept can make to our field and our understanding of media effects. Finally, we discuss framing as a bridge between paradigms as we shift from an era of mass communication to one of echo chambers, tailored information and microtargeting in the new media environment. © 2015 Mass Communication & Society Division of the Association for Education in Journalism and Mass Communication
... En sciences de l'information, ce terme décrit le recueil et la gestion de bases de données se distinguant par un volume important, une variété des types de données et une grande vitesse de génération [194]. L'observation de l'évolution du nombre de requêtes contenant le terme "big data" dans le moteur de recherche Google depuis 10 ans ( [88,159]. ...
The increasing size of datasets is a growing issue in epidemiology. The CoPanFlu-France cohort(1450 subjects), intended to study H1N1 pandemic influenza infection risk as a combination of biolo-gical, environmental, socio-demographic and behavioral factors, and in which hundreds of covariatesare collected for each patient, is a good example. The statistical methods usually employed to exploreassociations have many limits in this context. We compare the contribution of data-driven exploratorymethods, assuming the absence of a priori hypotheses, to hypothesis-driven methods, requiring thedevelopment of preliminary hypotheses.Firstly a data-driven study is presented, assessing the ability to detect influenza infection determi-nants of two data mining methods, the random forests (RF) and the boosted regression trees (BRT), ofthe conventional logistic regression framework (Univariate Followed by Multivariate Logistic Regres-sion - UFMLR) and of the Least Absolute Shrinkage and Selection Operator (LASSO), with penaltyin multivariate logistic regression to achieve a sparse selection of covariates. A simulation approachwas used to estimate the True (TPR) and False (FPR) Positive Rates associated with these methods.Between three and twenty-four determinants of infection were identified, the pre-epidemic antibodytiter being the unique covariate selected with all methods. The mean TPR were the highest for RF(85%) and BRT (80%), followed by the LASSO (up to 78%), while the UFMLR methodology wasinefficient (below 50%). A slight increase of alpha risk (mean FPR up to 9%) was observed for logisticregression-based models, LASSO included, while the mean FPR was 4% for the data-mining methods.Secondly, we propose a hypothesis-driven causal analysis of the infection risk, with a structural-equation model (SEM). We exploited the SEM specificity of modeling latent variables to study verydiverse factors, their relative impact on the infection, as well as their eventual relationships. Only thelatent variables describing host susceptibility (modeled by the pre-epidemic antibody titer) and com-pliance with preventive behaviors were directly associated with infection. The behavioral factors des-cribing risk perception and preventive measures perception positively influenced compliance with pre-ventive behaviors. The intensity (number and duration) of social contacts was not associated with theinfection.This thesis shows the necessity of considering novel statistical approaches for the analysis of largedatasets in epidemiology. Data mining and LASSO are credible alternatives to the tools generally usedto explore associations with a high number of variables. SEM allows the integration of variables des-cribing diverse dimensions and the explicit modeling of their relationships ; these models are thereforeof major interest in a multidisciplinary study as CoPanFlu.
... For instance the use of data analysis in the recruitment of baseball players, as recorded in Moneyball -the book (Lewis, 2003) and the filmwas of maximum effectiveness while only one team knew of its capabilities. Another notable example was in the context of President Obama's re-election campaign (Issenberg, 2012), something that will surely be mimicked by all candidates with the necessary resources in the future. ...
In 2008, Chris Anderson (2008), at that time the Editor-in-Chief of Wired, proposed that in the age of the petabyte, there was no longer any need for the scientific method, nor for models or theories. Although it might be contended that this was more provocation and journalistic hubris than formal or substantiated claim, the issue was taken up and has gathered momentum ever since. Indeed within a year or so of Anderson's article, and a series of rejoinders published on the Edge Web site, 'The Age of Big Data' was being heralded, and the measure had increased from petabytes to exabytes, zettabytes, and yottabytes. Diebold (2012) usefully distinguishes between Big Data 'the phenomenon', 'the term', and 'the discipline'; arguing that the phenomenon 'continues unabated', the term is 'firmly entrenched', and the discipline is 'emerging'. In what follows we focus initially on the term and the phenomenon, but our main objective is to argue that it is critical that there is general understanding of the emerging discipline. In particular we aim to justify the assertion that in the age of Big Data the ability to be able to develop abstractions and concepts is at least as important as it was previously; perhaps even more so. Moreover that these skills and techniques need to be understood and available to all of us in an era where we are all analysts and researchers at least to the extent of our use of the internet and its potential for affording search and investigation of online resources. We seek to offer some critical insights into these activities - modeling, conceptualizing, and theorizing - by comparing and contrasting Knowledge Discovery from Data (KDD) with the Grounded Theory Method (GTM). The former a technical orientation, that although predating Big Data, lies at the heart of the emerging tools and techniques. The latter a widely used approach to qualitative research aimed at developing conceptual models 'grounded in the data'.
... Analytics were critical to the success of President Obama's 2012 campaign. President Obama's team used data analytics and the experimental method to assemble a winning coalition vote by vote; through the analysis of large amounts of data, the interests of individual voters were known and addressed (Issenberg 2013). All of these organizations have been using analytics to identify patterns that help to increase the intelligence of their business processes and improve their performance. ...
The purpose of integrated operations is to provide complete monitoring and analysis of the production process in relevant time to help enable superior and timely decision making and to optimize asset performance. Several industry technologies support integrated operations, such as intelligent completions, real-time systems, surface-sub-surface models, and workflow automation systems, etc. Each of these technologies provides relevant data pertaining to one specific part the asset. The integration, correlation, and analysis of this data (current and historic) helps the operator understand the current state of the asset as well as make inferences about future behavior. Such capabilities are provided by a set of tools and techniques known within the industry as analytics. Operating and service companies are using new and improved analytics to support oil and gas operation and management processes. Additionally, several analytics commonly used in the foreign market are being applied to industry operation and management workflows. This has led to more robust and effective solutions for oil and gas production operations. However, the analytics value added is limited if implemented in an isolated fashion; the real value is obtained when analytics are immersed within comprehensive production workflows, aiding analysis, processing, and modeling of the production process. Workflows enhanced using analytics can transform integrated operations into intelligent operations. This paper presents analysis of how analytics have been applied to support integrated operations and demonstrates the value obtained when these tools are embedded into production workflows. To support this analysis, two case studies are described in which analytics are the main elements of advanced production monitoring and optimization workflows.
... An example is web logs: websites such as Google or Facebook automatically record user information at each visit. Other examples come from the stock market [20], earthquake surveillance [21], political elections [22], behavioral studies [23], sports [24], pharmaceutical reports [25], health care [26,27], electronic medical records [28], imaging data [29], genome data [30,31], and entrepreneur transaction records [32]. Data collection is sometimes interdisciplinary. ...
Background: In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.
Objective: The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.
Methods: We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.
Results: This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.
Conclusions: Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.
(JMIR Med Inform 2014;2(1):e1)
doi:10.2196/medinform.2913
KEYWORDS
big data; database; medical informatics; clinical research; medicine
Este texto tiene como objeto analizar los cambios que se están produciendo en la comunicación política que se desarrolla en el marco de las campañas electorales, en especial, pero no solamente, a la luz de las recientes tendencias en diversos países de América Latina y, en menor medida, Europa. La argumentación sobre estos cambios se estructura en diez re! exiones, que fundamentadas en investigaciones previas tanto del autor como de otros investigadores, han podido ser percibidas y contrastadas sobre el terreno, a partir de la experiencia personal como consultor para gobiernos, partidos políticos e instituciones con función ejecutiva.
There was more discussion of e-mails and e-mail servers in the 2016 election than any previous campaign. Yet, practically no attention was paid to the actual e-mails distributed by campaigns themselves. This study explores how presidential campaigns used e-mail as a strategic tool during the 2016 election. We collected and analyzed e-mails from all major party candidates over 10 months leading up to election day. The campaigns were organized into three categories based on how successful they were and how seriously they were run. Comparing the use of e-mail within each category and between them illuminates variation in how campaigns used e-mail, including during the primaries and the general election between Clinton and Trump. Notably, the Trump campaign e-mails were more participatory, fitting the populist theme of the campaign, and the Clinton campaign made the surprising strategic decision to stop direct e-mail communication to passive e-mail subscribers over two months before election day. Overall this study demonstrates how some strategies, such as the frequency of emailing, focus on fundraising, and consistent forms of interactions have become widely accepted norms. Further, it is clear that e-mail remains valuable for campaigns and an important subject for scholarship, despite its mundane nature.
In the current moment of democratic upheaval, the role of technology has been gaining increasing space in the democratic debate due to its role both in facilitating political debates, as well as how users' data is gathered and used. This article aims to discuss the relationship between democracy and the "algorithmic turn" - which the authors define as the "central and strategic role of data processing and automated reasoning in electoral processes, governance and decision making." In doing so the authors help us understand how this phenomenon is influencing society - both positively and negatively - and what are the practical implications we see as a result.
https://link.springer.com/chapter/10.1007/978-3-658-21665-8_2 +++++ In diesem Beitrag stellen wir ein Big-Data-Prozessmodell vor, welches Big Data aus einer dezidiert soziologischen Perspektive in den Blick nimmt. Die wesentlichen Elemente des Modells orientieren sich an dem traditionellen Datenverarbeitungsprozess, beginnend bei der Datengenerierung über die Datenauswertung bis hin zur Steuerung komplexer Systeme. Es werden verschiedene Datenquellen und -typen diskutiert. Deren Besonderheiten werden anhand des Fallbeispiels der Selbstvermessung exemplifiziert. Im Rahmen der Datenverarbeitung stellen sich Fragen der Datenqualität und -reliabilität sowie geeigneter Strategien und Verfahren. Ferner erörtern wir den möglichen Einsatz dieser Verfahren innerhalb der soziologischen Forschung. Schließlich legen wir die Möglichkeiten der Steuerung komplexer Systeme mittels Big-Data-Verfahren anhand der Fallbeispiele Verkehrsteuerung, Smart Grid, Smart Governance und Predictive Policing dar. Abschließend diskutieren wir, inwiefern Vertrauen Grundlage für den Einsatz der beschriebenen Big-Data-Verfahren ist.
Invited submission for Sur- International Journal on Human Rights (forthcoming) - This essay explores the role of the algorithmic turn – the central and strategic role of digital intelligence – in relation to the democratic transition underway. We first discuss the ways in which digital intelligence is influencing and dictating voter behaviors and outcomes. Second, we look at the increasing role of data and algorithms in governance and policy decision processes and the implications for citizen rights. Lastly, we bring to fore some questions on the governance of such technological integration in democratic processes.
We use the term "big data" with the understanding that the real game changer is the connection and digitization of everything. Every portfolio is affected: finance, transport, housing, food, environment, industry, health, welfare, defense, education, science, and more. The authors in this symposium will focus on a few of these areas to exemplify the main ideas and issues.
This chapter first provides definition to data mining. The ongoing remarkable growth in the field of data mining and knowledge discovery has been fueled by a fortunate confluence of a variety of factors such as the development of “off-the-shelf” commercial data mining software suites, and the tremendous growth in computing power and storage capacity. Automation is no substitute for human input. Humans need to be actively involved at every phase of the data mining process. The chapter then discusses cross-industry standard practice for data mining (CRISP-DM), which provides a nonproprietary and freely available standard process for fitting data mining into the general problem solving strategy of a business or research unit. It discusses four fallacies of the data mining. The chapter finally lists most common data mining tasks such as description, estimation, prediction, classification, clustering and association.
Thus far, the literature on election campaigns has identified three phases of campaigning: the pre-modern, the modern, and the professionalized phase. With this paper, we suggest that election campaigning has entered a fourth phase, characterized by new applications of communication technologies, quantitative data, immediate communications, a reinvented citizen-politics relationship offering more emotional access and lower barriers for active roles for citizens in campaigns. We root the change in the citizen-media nexus, with changes in media use and production being a major driver of this development. The classification of the ‘Mediatized Campaign’ emphasizes the role of media use and the connected changes in political and social institutions. We suggest that this classification can add coherence to future research on campaigning.
So far, I have explored the power of observation and the ability to collaborate with customers. But how do we convert all this into a razor-sharp focus on a specific set of customers? The marketer has now the opportunity to use this power to bring the customer to a positive decision about a product, whether this is a first-time purchase, a repeat purchase, or a tweet to friends exalting the virtues of the recently purchased product. These decisions happen over time and require a series of collaborations. Without a proper conductor, the musicians hired to influence the customer can at best create musical noise. How do we orchestrate these powerful tools to collaborate with each other? This chapter discusses how marketing efforts can be pooled across the silos to influence a customer through the stages of marketing. How would marketers coordinate the effort to reduce cost and the annoyance factor and use the power of collaboration to improve the relationship with their customers?
Data generation has increased drastically over the past few years. Data management has also grown in importance because extracting the significant value out of a huge pile of raw data is of prime importance while making different decisions. This article reviews the concept of Big Data. The Thomson Reuters Web of Science Core Collection academic database was used to overview publications that contained “BIG DATA” keywords and were included in Web of Science Category under “Engineering”. The analysis of publications was made according to year, country, journal, authors, language and funding agency.
MICHAEL AND I ARE STANDING IN HIS SMALL MANHATTAN KITCHEN LOOKING through his window, which faces south over Central Park. Skyscrapers loom on the other side of the expansive green space. Michael is in his early thirties, right on the Gen-X/Millennial cusp. He lives in Harlem and exemplifies a number of trends in today’s urban living. He is part of a growing black professional class. His income would make him affluent in most parts of the world, but not so much here. So he and his Hispanic partner also have a third roommate in their apartment. He keeps a lot of his expenses low and therefore has enough discretionary income for entertaining, dining out, and other social activities.
Voter dissatisfaction with a poor US economy during President Obama's first term in office poses the question of how he was re-elected to a second term. If Republican strategists framed the 2012 election as a referendum on the economy, then a clear disconnect between dissatisfaction and voting behavior is indicative. This article employs balance theory and the theory of framing to explore how the 2012 Obama campaign favorably persuaded voters. We argue that the Obama campaign framed economically related dimensions of happiness (e.g., income equality and employment) to increase voters' individual happiness despite the unfavorable economic climate. © Common Ground, Mansoureh Ebrahimi, Mir Mohammad Ali Seyed Jalili Kohkamar, Maryam Soltani, All Rights Reserved.
To what extent do political campaigns mobilize voters? Despite the central role of campaigns in American politics and despite many experiments on campaigning, we know little about the aggregate effects of an entire campaign on voter participation. Drawing upon inside information from presidential campaigns and utilizing a geographic research design that exploits media markets spanning state boundaries, we estimate the aggregate effects of a large-scale campaign. We estimate that the 2012 presidential campaigns increased turnout in highly targeted states by 7–8 percentage points, on average, indicating that modern campaigns can significantly alter the size and composition of the voting population. Further evidence suggests that the predominant mechanism behind this effect is traditional ground campaigning, which has dramatically increased in scale in the last few presidential elections. Additionally, we find no evidence of diminishing marginal returns to ground campaigning, meaning that voter contacts, each likely exhibiting small individual effects, may aggregate to large effects over the course of a campaign.
Which online tools should you use? How can you spread the political message and involve supporters? Is advertising on the web useful? How can you make sure search engines find you? How can you grow your mailing list? These questions come up every time a political campaign is launched (or for that matter any kind of campaign to increase involvement and online participation). Every campaign is unique, every political undertaking a case unto itself. Tools and communication methods must be adapted to address the specific situation and altered over time to reflect changing needs. Some tools, however, are critical to any political communication strategy. This chapter shows how to use these tools most effectively, demonstrating that the most innovative options are not necessarily the best—and that one of the most effective online communication tools is in fact one of the oldest.
We propose an interpretable rule-based classification system based on ideas from Boolean compressed sensing. We represent the problem of learning individual conjunctive clauses or individual disjunctive clauses as a Boolean group testing problem, and apply a novel linear programming relaxation to find solutions. We derive results for exact rule recovery which parallel the conditions for exact recovery of sparse signals in the compressed sensing literature: although the general rule recovery problem is NP-hard, under some conditions on the Boolean 'sensing' matrix, the rule can be recovered exactly. This is an exciting development in rule learning where most prior work focused on heuristic solutions. Furthermore we construct rule sets from these learned clauses using set covering and boosting. We show competitive classification accuracy using the proposed approach.
FOREWORD
Welcome to Greenwich, London, UK.
We have the pleasure to arrange the first international conference on eBusiness, eCommerce, eManagement, eLearning and eGovernance, IC5E 2014 in London. This conference is hoped to be the first in many in its comprehensiveness, inclusivity and universality in its approach. We extend warm heartfelt thanks to our keynote speakers who have voluntarily given up their free time to share their many years of experience and knowledge with us, without whom this conference would not have been a success. Thanks are also to be given to all those who submitted their papers and to the team of reviewers for their due diligence and careful scrutiny of each paper submitted.
The rather challenging task of organising this whole conference would not have been possible without the constant help of the editors, Mahdi H. Miraz and Kokula Krishna Hari and their dedicated organisations of CReATED and ASDF.
Special thanks must go to Mahdi for obtaining technical co-sponsorship by the British Computer Society and for preparing the conference proceedings amongst his many roles. Kokula Krishna Hari helped in obtaining the DOI, ISBN and the considerable logistical support from the ASDF and Techno Forum organisations including the financial sponsorship of the whole conference.
Special thanks must also go to Greenwich University for agreeing to host the conference and for their extreme patience in working with all of us. The chairs and technical programme committee members must also not be forgotten for their eagerness to make this conference a success.
The IC5E 2014 conference will also comprise of the First International Workshop on Mobile Usability, Learning and Application Development (MULAD). The workshop is organized under the partnership with the Centre for Research on Applied Technology, Entrepreneurship and Development (CReATED) and the Association of Scientists, Developers and Faculties (ASDF). This workshop is specifically focused on Mobile Usability, Learning and Application Development (MULAD) that improve existing mobile technologies and application. The workshop’s primary aim is to bring together professionals, technologists, policy makers and academics to share and disseminate their innovations and research projects.
Finally, a special gratitude is due to the printers, Marston Book Services Limited, for working to such a tight deadline.
To conclude, we would like to see this conference as a starting point in future international collaboration in the work of IC5E.
Maaruf Ali, IC5E 2014 Conference Chair
Mahdi H. MIraz, IC5E 2014 Publication Chair
Kokula Krishna Hari Kunasekaran, IC5E Organizing Committee Chair
Modern campaigns develop databases of detailed information about citizens to inform electoral strategy and to guide tactical efforts. Despite sensational reports about the value of individual consumer data, the most valuable information campaigns acquire comes from the behaviors and direct responses provided by citizens themselves. Campaign data analysts develop models using this information to produce individual-level predictions about citizens' likelihoods of performing certain political behaviors, of supporting candidates and issues, and of changing their support conditional on being targeted with specific campaign interventions. The use of these predictive scores has increased dramatically since 2004, and their use could yield sizable gains to campaigns that harness them. At the same time, their widespread use effectively creates a coordination game with incomplete information between allied organizations. As such, organizations would benefit from partitioning the electorate to not duplicate efforts, but legal and political constraints preclude that possibility.
Probably the major approach to making predictions or recommendations about user behavior is by pairing unambiguous indicators of preference with attributes of the users who have given those indications. However, since the necessary preference data is often difficult to obtain, it is considered very valuable and often held closely by vendors and advertisers. On the other hand, while Twitter and other social media platforms provide a wealth of data about users by way of what they say or tweet, who or what they like or follow, etc., little work has been done to combine these data with indicators of preference for purposes of prediction or recommendation. In this paper we present a novel approach to mining preference data from natural language expressions in social media, which are then extrapolated to other individuals whose preferences are not known through predictive modeling. As an application for this approach, we describe the implementation of Tweetcast Your Vote, a publicly accessible system that predicted the voting decisions of Twitter users in the 2012 U.S. presidential election.
The world's data is growing more than 40% annually. Coupled withexponentially growing computing horsepower, this provides us withunprecedented basis for 'learning' useful things from the data throughstatistical induction without material human intervention and acting onthem. Philosophers have long debated the merits and demerits ofinduction as a scientific method, the latter being that conclusions arenot guaranteed to be certain and that multiple and numerous models canbe conjured to explain the observed data. I propose that 'big data'brings a new and important perspective to these problems in that itgreatly ameliorates historical concerns about induction, especially ifour primary objective is prediction as opposed to causal modelidentification. Equally significantly, it propels us into an era ofautomated decision making, where computers will make the bulk ofdecisions because it is infeasible or more costly for humans to do so.In this paper, I describe how scale, integration and most importantly,prediction will be distinguishing hallmarks in this coming era of DataScience.' In this brief monograph, I define this newly emerging fieldfrom business and research perspectives.
ResearchGate has not been able to resolve any references for this publication.