4,718 research outputs found
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications
The unprecedented proliferation of smart devices together with novel
communication, computing, and control technologies have paved the way for the
Advanced Internet of Things~(A-IoT). This development involves new categories
of capable devices, such as high-end wearables, smart vehicles, and consumer
drones aiming to enable efficient and collaborative utilization within the
Smart City paradigm. While massive deployments of these objects may enrich
people's lives, unauthorized access to the said equipment is potentially
dangerous. Hence, highly-secure human authentication mechanisms have to be
designed. At the same time, human beings desire comfortable interaction with
their owned devices on a daily basis, thus demanding the authentication
procedures to be seamless and user-friendly, mindful of the contemporary urban
dynamics. In response to these unique challenges, this work advocates for the
adoption of multi-factor authentication for A-IoT, such that multiple
heterogeneous methods - both well-established and emerging - are combined
intelligently to grant or deny access reliably. We thus discuss the pros and
cons of various solutions as well as introduce tools to combine the
authentication factors, with an emphasis on challenging Smart City
environments. We finally outline the open questions to shape future research
efforts in this emerging field.Comment: 7 pages, 4 figures, 2 tables. The work has been accepted for
publication in IEEE Network, 2019. Copyright may be transferred without
notice, after which this version may no longer be accessibl
Analysis Role of ML and Big Data Play in Driving Digital Marketing's Paradigm Shift
Marketing strategies are being revolutionized by the development of user data and the expanding usability of Machine Learning (ML) as well as Big Data approaches. The wide variety of options that ML and Big Data applications provide in building and sustaining a competitive corporate edge are not fully understood by researchers and marketers. Based on a thorough analysis of academic and commercial literature, we offer a classification of ML and Big Data use cases in marketing in this article. In order to effectively employ ML and Big Data in marketing, we have discovered 11 recurrent use cases that are grouped into 4 homogenous families. These families are: fundamentals of the consumer, the consumer experience, decision-making, and financial impact. We go over the taxonomy's repeating patterns and offer a conceptual framework for understanding and extending it, emphasizing the practical ramifications for marketers and academics
Rethinking affordance
n/a – Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory
- …