237,964 research outputs found
Big Data Techniques to Improve Learning Access and Citizen Engagement for Adults in Urban Environments
This presentation explores the emerging concept of ‘Big Data in Education’ and introduces
novel technologies and approaches for addressing inequalities in access to participation and
success in lifelong learning, to produce better life outcomes for urban citizens. It introduces
the work of the new Urban Big Data Centre (UBDC) at the University of Glasgow, presenting
a case study of its first data product – the integrated Multimedia City Data (iMCD) project.
Educational engagement and predictive factors are presented for adult learners, and older
adult learners, in a representative survey of 1500 households. This was followed up with
mobility tracking data using GPS data and wearable camera images, as well as one year’s
worth of contextual data from over one hundred web sources (social media, news, weather).
The chapter introduces the complex dataset that can help stakeholders, academics, citizens
and other external users examine active aging and citizen learning engagement in the
modern urban city, and thus support the development of the learning city. It concludes with a call for a more three-dimensional view of citizen-learners’ daily activity and mobility, such
as satellite, mobile phone and active travel application data, alongside administrative data
linkage to further explore lifelong learning participation and success. Policy implications are
provided for addressing inequalities, and interventions proposed for how cities might
promote equal and inclusive adult learning engagement in the face of continued austerity
cuts and falling adult learner numbers
Data and Predictive Analytics Use for Logistics and Supply Chain Management
Purpose
The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach
The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings
Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications
This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value
The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area
Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis
One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite"
Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia
As a major source for information on virtually any topic, Wikipedia serves an
important role in public dissemination and consumption of knowledge. As a
result, it presents tremendous potential for people to promulgate their own
points of view; such efforts may be more subtle than typical vandalism. In this
paper, we introduce new behavioral metrics to quantify the level of controversy
associated with a particular user: a Controversy Score (C-Score) based on the
amount of attention the user focuses on controversial pages, and a Clustered
Controversy Score (CC-Score) that also takes into account topical clustering.
We show that both these measures are useful for identifying people who try to
"push" their points of view, by showing that they are good predictors of which
editors get blocked. The metrics can be used to triage potential POV pushers.
We apply this idea to a dataset of users who requested promotion to
administrator status and easily identify some editors who significantly changed
their behavior upon becoming administrators. At the same time, such behavior is
not rampant. Those who are promoted to administrator status tend to have more
stable behavior than comparable groups of prolific editors. This suggests that
the Adminship process works well, and that the Wikipedia community is not
overwhelmed by users who become administrators to promote their own points of
view
Hacking the social life of Big Data
This paper builds on 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
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A person’s
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the user’s experience than generic
parameters, accurate predictions reveal important aspects of user’s
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumer’s buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five users’
personality factors and 1,200 personal users’ pictures
Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.
This report gives an overview of the most relevant organisational and\ud
behavioural aspects regarding user profiling. It discusses not only the\ud
most important aims of user profiling from both an organisation’s as\ud
well as a user’s perspective, it will also discuss organisational motives\ud
and barriers for user profiling and the most important conditions for\ud
the success of user profiling. Finally recommendations are made and\ud
suggestions for further research are given
Global Innovations in Measurement and Evaluation
We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas
Are black friday deals worth it? Mining twitter users' sentiment and behavior response
The Black Friday event has become a global opportunity for marketing and companies’
strategies aimed at increasing sales. The present study aims to understand consumer behavior
through the analysis of user-generated content (UGC) on social media with respect to the Black Friday
2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed
Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent
Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday.
In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings
towards the identified topics and offers published by the companies on Twitter. Thirdly and finally,
a data-text mining process called textual analysis (TA) was performed to identify insights that could
help companies to improve their promotion and marketing strategies as well as to better understand
the customer behavior on social media. The results show that consumers had positive perceptions of
such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA),
insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on
these results, we offer guidelines to practitioners to improve their social media communication.
Our results also have theoretical implications that can promote further research in this area
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