56,617 research outputs found
Emotions, Demographics and Sociability in Twitter Interactions
The social connections people form online affect the quality of information
they receive and their online experience. Although a host of socioeconomic and
cognitive factors were implicated in the formation of offline social ties, few
of them have been empirically validated, particularly in an online setting. In
this study, we analyze a large corpus of geo-referenced messages, or tweets,
posted by social media users from a major US metropolitan area. We linked these
tweets to US Census data through their locations. This allowed us to measure
emotions expressed in the tweets posted from an area, the structure of social
connections, and also use that area's socioeconomic characteristics in
analysis. %We extracted the structure of online social interactions from the
people mentioned in tweets from that area. We find that at an aggregate level,
places where social media users engage more deeply with less diverse social
contacts are those where they express more negative emotions, like sadness and
anger. Demographics also has an impact: these places have residents with lower
household income and education levels. Conversely, places where people engage
less frequently but with diverse contacts have happier, more positive messages
posted from them and also have better educated, younger, more affluent
residents. Results suggest that cognitive factors and offline characteristics
affect the quality of online interactions. Our work highlights the value of
linking social media data to traditional data sources, such as US Census, to
drive novel analysis of online behavior.Comment: International Conference on the Web and Social Media (ICWSM2016
Analysis of Home Location Estimation with Iteration on Twitter Following Relationship
User's home locations are used by numerous social media applications, such as
social media analysis. However, since the user's home location is not generally
open to the public, many researchers have been attempting to develop a more
accurate home location estimation. A social network that expresses
relationships between users is used to estimate the users' home locations. The
network-based home location estimation method with iteration, which propagates
the estimated locations, is used to estimate more users' home locations. In
this study, we analyze the function of network-based home location estimation
with iteration while using the social network based on following relationships
on Twitter. The results indicate that the function that selects the most
frequent location among the friends' location has the best accuracy. Our
analysis also shows that the 88% of users, who are in the social network based
on following relationships, has at least one correct home location within
one-hop (friends and friends of friends). According to this characteristic of
the social network, we indicate that twice is sufficient for iteration.Comment: The 2016 International Conference on Advanced Informatics: Concepts,
Theory and Application (ICAICTA2016
Animating and sustaining niche social networks
Within the communicative space online Social Network Sites (SNS) afford, Niche Social Networks Sites (NSNS) have emerged around particular geographic, demographic or topic-based communities to provide what broader SNS do not: specified and targeted content for an engaged and interested community. Drawing on a research project developed at the Queensland University of Technology in conjunction with the Australian Smart Services Cooperative Research Centre that produced an NSNS based around Adventure Travel, this paper outlines the main drivers for community creation and sustainability within NSNS. The paper asks what factors motivate users to join and stay with these sites and what, if any, common patterns can be noted in their formation. It also outlines the main barriers to online participation and content creation in NSNS, and the similarities and differences in SNS and NSNS business models. Having built a community of 100 registered members, the staywild.com.au project was a living laboratory, enabling us to document the steps taken in producing a NSNS and cultivating and retaining active contributors. The paper incorporates observational analysis of user-generated content (UGC) and user profile submissions, statistical analysis of site usage, and findings from a survey of our membership pool in noting areas of success and of failure. In drawing on our project in this way we provide a template for future iterations of NSNS initiation and development across various other social settings: not only niche communities, but also the media and advertising with which they engage and interact. Positioned within the context of online user participation and UGC research, our paper concludes with a discussion of the ways in which the tools afforded by NSNS extend earlier understandings of online ‘communities of interest’. It also outlines the relevance of our research to larger questions about the diversity of the social media ecology
Remote from what? Perspectives of distance learning students in remote rural areas of Scotland
Distance learning is seen as the obvious answer for remote learners, and the use of online media is expected to overcome any access difficulties imposed by geographical distance. However, this belief may be obscuring our understanding of the role that location and individual circumstances have in shaping student experience. This paper explores the variation in experiences of remote rural students who study with the Open University (UK). The researchers found that perceptions of remoteness depended on geography, but were also relative to individual circumstances. With respect to students’ sense of connection with university staff and peers, most mentioned their contact with their personal tutor. Networks with peers were less common, a matter of concern if peer networks are integral to fostering improved retention and progression. In this particular context, distance education may be playing an important and distinctive role for remote students by providing opportunities for connections with like-minded people
Do Diffusion Protocols Govern Cascade Growth?
Large cascades can develop in online social networks as people share
information with one another. Though simple reshare cascades have been studied
extensively, the full range of cascading behaviors on social media is much more
diverse. Here we study how diffusion protocols, or the social exchanges that
enable information transmission, affect cascade growth, analogous to the way
communication protocols define how information is transmitted from one point to
another. Studying 98 of the largest information cascades on Facebook, we find a
wide range of diffusion protocols - from cascading reshares of images, which
use a simple protocol of tapping a single button for propagation, to the ALS
Ice Bucket Challenge, whose diffusion protocol involved individuals creating
and posting a video, and then nominating specific others to do the same. We
find recurring classes of diffusion protocols, and identify two key
counterbalancing factors in the construction of these protocols, with
implications for a cascade's growth: the effort required to participate in the
cascade, and the social cost of staying on the sidelines. Protocols requiring
greater individual effort slow down a cascade's propagation, while those
imposing a greater social cost of not participating increase the cascade's
adoption likelihood. The predictability of transmission also varies with
protocol. But regardless of mechanism, the cascades in our analysis all have a
similar reproduction number ( 1.8), meaning that lower rates of
exposure can be offset with higher per-exposure rates of adoption. Last, we
show how a cascade's structure can not only differentiate these protocols, but
also be modeled through branching processes. Together, these findings provide a
framework for understanding how a wide variety of information cascades can
achieve substantial adoption across a network.Comment: ICWSM 201
Coupling Human Mobility and Social Ties
Studies using massive, passively data collected from communication
technologies have revealed many ubiquitous aspects of social networks, helping
us understand and model social media, information diffusion, and organizational
dynamics. More recently, these data have come tagged with geographic
information, enabling studies of human mobility patterns and the science of
cities. We combine these two pursuits and uncover reproducible mobility
patterns amongst social contacts. First, we introduce measures of mobility
similarity and predictability and measure them for populations of users in
three large urban areas. We find individuals' visitations patterns are far more
similar to and predictable by social contacts than strangers and that these
measures are positively correlated with tie strength. Unsupervised clustering
of hourly variations in mobility similarity identifies three categories of
social ties and suggests geography is an important feature to contextualize
social relationships. We find that the composition of a user's ego network in
terms of the type of contacts they keep is correlated with mobility behavior.
Finally, we extend a popular mobility model to include movement choices based
on social contacts and compare it's ability to reproduce empirical measurements
with two additional models of mobility
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