2,578 research outputs found
Modelling Opinion Dynamics in the Age of Algorithmic Personalisation
Modern technology has drastically changed the way we interact and consume
information. For example, online social platforms allow for seamless
communication exchanges at an unprecedented scale. However, we are still
bounded by cognitive and temporal constraints. Our attention is limited and
extremely valuable. Algorithmic personalisation has become a standard approach
to tackle the information overload problem. As result, the exposure to our
friends' opinions and our perception about important issues might be distorted.
However, the effects of algorithmic gatekeeping on our hyper-connected society
are poorly understood. Here, we devise an opinion dynamics model where
individuals are connected through a social network and adopt opinions as
function of the view points they are exposed to. We apply various filtering
algorithms that select the opinions shown to users i) at random ii) considering
time ordering or iii) their current beliefs. Furthermore, we investigate the
interplay between such mechanisms and crucial features of real networks. We
found that algorithmic filtering might influence opinions' share and
distributions, especially in case information is biased towards the current
opinion of each user. These effects are reinforced in networks featuring
topological and spatial correlations where echo chambers and polarisation
emerge. Conversely, heterogeneity in connectivity patterns reduces such
tendency. We consider also a scenario where one opinion, through nudging, is
centrally pushed to all users. Interestingly, even minimal nudging is able to
change the status quo moving it towards the desired view point. Our findings
suggest that simple filtering algorithms might be powerful tools to regulate
opinion dynamics taking place on social network
Predicting User-Interactions on Reddit
In order to keep up with the demand of curating the deluge of crowd-sourced
content, social media platforms leverage user interaction feedback to make
decisions about which content to display, highlight, and hide. User
interactions such as likes, votes, clicks, and views are assumed to be a proxy
of a content's quality, popularity, or news-worthiness. In this paper we ask:
how predictable are the interactions of a user on social media? To answer this
question we recorded the clicking, browsing, and voting behavior of 186 Reddit
users over a year. We present interesting descriptive statistics about their
combined 339,270 interactions, and we find that relatively simple models are
able to predict users' individual browse- or vote-interactions with reasonable
accuracy.Comment: Presented at ASONAM 201
Characteristics of Social Media Stories
An emerging trend in social media is for users to create and publish stories , or curated lists of web resources with the purpose of creating a particular narrative of interest to the user. While some stories on the web are automatically generated, such as Facebook’s Year in Review , one of the most popular storytelling services is Storify , which provides users with curation tools to select, arrange, and annotate stories with content from social media and the web at large. We would like to use tools like Storify to present automatically created summaries of archival collections. To support automatic story creation, we need to better understand as a baseline the structural characteristics of popular (i.e., receiving the most views) human-generated stories. We investigated 14,568 stories from Storify, comprising 1,251,160 individual resources, and found that popular stories (i.e., top 25 % of views normalized by time available on the web) have the following characteristics: 2/28/1950 elements (min/median/max), a median of 12 multimedia resources (e.g., images, video), 38 % receive continuing edits, and 11 % of the elements are missing from the live web
Measuring patient-perceived quality of care in US hospitals using Twitter
BACKGROUND: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. OBJECTIVE: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. DESIGN: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. KEY RESULTS: Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). CONCLUSIONS: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
One goal of online social recommendation systems is to harness the wisdom of
crowds in order to identify high quality content. Yet the sequential voting
mechanisms that are commonly used by these systems are at odds with existing
theoretical and empirical literature on optimal aggregation. This literature
suggests that sequential voting will promote herding---the tendency for
individuals to copy the decisions of others around them---and hence lead to
suboptimal content recommendation. Is there a problem with our practice, or a
problem with our theory? Previous attempts at answering this question have been
limited by a lack of objective measurements of content quality. Quality is
typically defined endogenously as the popularity of content in absence of
social influence. The flaw of this metric is its presupposition that the
preferences of the crowd are aligned with underlying quality. Domains in which
content quality can be defined exogenously and measured objectively are thus
needed in order to better assess the design choices of social recommendation
systems. In this work, we look to the domain of education, where content
quality can be measured via how well students are able to learn from the
material presented to them. Through a behavioral experiment involving a
simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we
show that sequential voting systems can surface better content than systems
that elicit independent votes.Comment: To be published in the 10th International AAAI Conference on Web and
Social Media (ICWSM) 201
Distances in the field : mapping similarity and familiarity in the production, curation and consumption of Australian art music
This thesis provides a timely intervention in the investigation of cultural fields by employing traditional and new data analytics to expand our understanding of fields as multi-dimensional sites of production, curation and consumption. Through a case study of contemporary Australian art music, the research explores the multiple ways in which the concept of ‘distance’ contributes to how we conceive of and engage with fields of artistic practice. While the concept of distance has often been an implicit or axiomatic concern for cultural sociology, this thesis foregrounds how it can be used to analyse fields from multiple perspectives, at multiple scales of enquiry and using diverse methodologies. In doing so, it distinguishes between notions of distance in the related concepts of similarity and familiarity. In the former, the relative proximities of cultural producers can be mapped to discern and contrast the organising principles which underlie different perspectives of a field. In the latter, the degree of an individual’s familiarity with an item or genre can be included in theorisations of cultural preferences and their social dimensions. This is disrupted in a field such as Australian art music, however, as its emphasis on experimentation and innovation presents barriers to developing familiarity. Distance can be considered a defining characteristic of this field, and motivates its selection as a critical case study from which to investigate how audiences form attachments to distant musical sounds. The investigation of distance from multiple perspectives, using different scales of analysis and across a series of focal points in the lifecycle of artist practice, provides an analysis of Australian art music in terms of the tensions which emerge from these intersecting representations of the field. The singular spatial representation of ‘objective relations’ in a field, and a concern with power and domination – as found in the approach of Bourdieu – is replaced by a multiplicity of sets of relations and a concern with their organising principles and juxtapositions. The thesis argues that the actor constellations which distances produce are intimately linked to our capacity to engage with fields as discrete and knowable domains of cultural practice. Beyond our capacity to know a cultural field, it also argues for the importance of reconsidering how we form attachments to distant musical tastes. As an avant-garde genre which embraces foreign and confounding sounds, audiences require the capacity to draw on a range of consumption strategies and techniques to successfully engage with and value the unfamiliar
A Picture Tells a Thousand Words -- About You! User Interest Profiling from User Generated Visual Content
Inference of online social network users' attributes and interests has been
an active research topic. Accurate identification of users' attributes and
interests is crucial for improving the performance of personalization and
recommender systems. Most of the existing works have focused on textual content
generated by the users and have successfully used it for predicting users'
interests and other identifying attributes. However, little attention has been
paid to user generated visual content (images) that is becoming increasingly
popular and pervasive in recent times. We posit that images posted by users on
online social networks are a reflection of topics they are interested in and
propose an approach to infer user attributes from images posted by them. We
analyze the content of individual images and then aggregate the image-level
knowledge to infer user-level interest distribution. We employ image-level
similarity to propagate the label information between images, as well as
utilize the image category information derived from the user created
organization structure to further propagate the category-level knowledge for
all images. A real life social network dataset created from Pinterest is used
for evaluation and the experimental results demonstrate the effectiveness of
our proposed approach.Comment: 7 pages, 6 Figures, 4 Table
Friend Network as Gatekeeper: A Study of WeChat Users' Consumption of Friend-Curated Contents
Social media enables users to publish, disseminate, and access information
easily. The downside is that it has fewer gatekeepers of what content is
allowed to enter public circulation than the traditional media. In this paper,
we present preliminary empirical findings from WeChat, a popular messaging app
of the Chinese, indicating that social media users leverage their friend
networks collectively as latent, dynamic gatekeepers for content consumption.
Taking a mixed-methods approach, we analyze over seven million users'
information consumption behaviors on WeChat and conduct an online survey of
users. Both quantitative and qualitative evidence suggests that friend
network indeed acts as a gatekeeper in social media. Shifting from what should
be produced that gatekeepers used to decide, friend network helps separate the
worthy from the unworthy for individual information consumption, and its
structure and dynamics that play an important role in gatekeeping may inspire
the future design of socio-technical systems
Asymmetry in Online Social Networks
Varying degrees of symmetry can exist in a social network's connections. Some early online social networks (OSNs) were predicated on symmetrical connections, such as Facebook 'friendships' where both actors in a 'friendship' have an equal and reciprocal connection. Newer platforms -- Twitter, Instagram, and Facebook's 'Pages' inclusive -- are counterexamples of this, where 'following' another actor (friend, celebrity, business) does not guarantee a reciprocal exchange from the other.
This paper argues that the basic asymmetric connections in an OSN leads to emergent asymmetrical behaviour in the OSN's overall influence and connectivity, amongst others. This paper will then draw on empirical examples from popular sites (and prior network research) to illustrate how asymmetric connections can render individuals 'voiceless'.
The crux of this paper is an argument from the existentialist viewpoint on how the above asymmetric network properties lead to Sartrean bad faith (Sartre, 1943). Instead of genuine interpersonal connection, one finds varying degrees of pressure to assume the Sartrean 'in-itself' (the en soi) mode-of-being, irregardless of the magnitude of 'followers' one has.
Finally, this paper poses an open question: what other philosophical issues does this inherent asymmetry in modern social networking give rise to
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