59,393 research outputs found
Digital Stylometry: Linking Profiles Across Social Networks
There is an ever growing number of users with accounts on multiple social
media and networking sites. Consequently, there is increasing interest in
matching user accounts and profiles across different social networks in order
to create aggregate profiles of users. In this paper, we present models for
Digital Stylometry, which is a method for matching users through stylometry
inspired techniques. We experimented with linguistic, temporal, and combined
temporal-linguistic models for matching user accounts, using standard and novel
techniques. Using publicly available data, our best model, a combined
temporal-linguistic one, was able to correctly match the accounts of 31% of
5,612 distinct users across Twitter and Facebook.Comment: SocInfo'15, Beijing, China. In proceedings of the 7th International
Conference on Social Informatics (SocInfo 2015). Beijing, Chin
The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election
The present study argues that political communication on social media is
mediated by a platform's digital architecture, defined as the technical
protocols that enable, constrain, and shape user behavior in a virtual space. A
framework for understanding digital architectures is introduced, and four
platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the
typology. Using the 2016 US election as a case, interviews with three
Republican digital strategists are combined with social media data to qualify
the studyies theoretical claim that a platform's network structure,
functionality, algorithmic filtering, and datafication model affect political
campaign strategy on social media
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
User profiles matching for different social networks based on faces embeddings
It is common practice nowadays to use multiple social networks for different
social roles. Although this, these networks assume differences in content type,
communications and style of speech. If we intend to understand human behaviour
as a key-feature for recommender systems, banking risk assessments or
sociological researches, this is better to achieve using a combination of the
data from different social media. In this paper, we propose a new approach for
user profiles matching across social media based on embeddings of publicly
available users' face photos and conduct an experimental study of its
efficiency. Our approach is stable to changes in content and style for certain
social media.Comment: Submitted to HAIS 2019 conferenc
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