10,102 research outputs found
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
Technology has recently been recruited in the war against the ongoing obesity
crisis; however, the adoption of Health & Fitness applications for regular
exercise is a struggle. In this study, we present a unique demographically
representative dataset of 15k US residents that combines technology use logs
with surveys on moral views, human values, and emotional contagion. Combining
these data, we provide a holistic view of individuals to model their physical
exercise behavior. First, we show which values determine the adoption of Health
& Fitness mobile applications, finding that users who prioritize the value of
purity and de-emphasize values of conformity, hedonism, and security are more
likely to use such apps. Further, we achieve a weighted AUROC of .673 in
predicting whether individual exercises, and we also show that the application
usage data allows for substantially better classification performance (.608)
compared to using basic demographics (.513) or internet browsing data (.546).
We also find a strong link of exercise to respondent socioeconomic status, as
well as the value of happiness. Using these insights, we propose actionable
design guidelines for persuasive technologies targeting health behavior
modification
The crowd as a cameraman : on-stage display of crowdsourced mobile video at large-scale events
Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework's main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes
Predicting Session Length in Media Streaming
Session length is a very important aspect in determining a user's
satisfaction with a media streaming service. Being able to predict how long a
session will last can be of great use for various downstream tasks, such as
recommendations and ad scheduling. Most of the related literature on user
interaction duration has focused on dwell time for websites, usually in the
context of approximating post-click satisfaction either in search results, or
display ads. In this work we present the first analysis of session length in a
mobile-focused online service, using a real world data-set from a major music
streaming service. We use survival analysis techniques to show that the
characteristics of the length distributions can differ significantly between
users, and use gradient boosted trees with appropriate objectives to predict
the length of a session using only information available at its beginning. Our
evaluation on real world data illustrates that our proposed technique
outperforms the considered baseline.Comment: 4 pages, 3 figure
User-Item Reciprocity in Recommender Systems: Incentivizing the Crowd
Data consumption has changed significantly in the last 10
years. The digital revolution and the Internet has brought an abundance
of information to users. Recommender systems are a popular means of
finding content that is both relevant and personalized. However, today’s
users require better recommender systems, able of producing continuous
data feeds keeping up with their instantaneous and mobile needs. The
CrowdRec project addresses this demand by providing context-aware,
resource-combining, socially-informed, interactive and scalable recommendations.
The key insight of CrowdRec is that, in order to achieve
the dense, high-quality, timely information required for such systems, it
is necessary to move from passive user data collection, to more active
techniques fostering user engagement. For this purpose, CrowdRec activates
the crowd, soliciting input and feedback from the wider communit
Strategic corporate communication in the digital age
This chapter presents a systematic review of over thirty (30) types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the ‘oldest’ online marketing methods including the email and the websites are still very relevant for today’s corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.peer-reviewe
Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions
Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
- …