78,106 research outputs found
The Pulse of News in Social Media: Forecasting Popularity
News articles are extremely time sensitive by nature. There is also intense
competition among news items to propagate as widely as possible. Hence, the
task of predicting the popularity of news items on the social web is both
interesting and challenging. Prior research has dealt with predicting eventual
online popularity based on early popularity. It is most desirable, however, to
predict the popularity of items prior to their release, fostering the
possibility of appropriate decision making to modify an article and the manner
of its publication. In this paper, we construct a multi-dimensional feature
space derived from properties of an article and evaluate the efficacy of these
features to serve as predictors of online popularity. We examine both
regression and classification algorithms and demonstrate that despite
randomness in human behavior, it is possible to predict ranges of popularity on
twitter with an overall 84% accuracy. Our study also serves to illustrate the
differences between traditionally prominent sources and those immensely popular
on the social web
The Impact of Crowds on News Engagement: A Reddit Case Study
Today, users are reading the news through social platforms. These platforms
are built to facilitate crowd engagement, but not necessarily disseminate
useful news to inform the masses. Hence, the news that is highly engaged with
may not be the news that best informs. While predicting news popularity has
been well studied, it has not been studied in the context of crowd
manipulations. In this paper, we provide some preliminary results to a longer
term project on crowd and platform manipulations of news and news popularity.
In particular, we choose to study known features for predicting news popularity
and how those features may change on reddit.com, a social platform used
commonly for news aggregation. Along with this, we explore ways in which users
can alter the perception of news through changing the title of an article. We
find that news on reddit is predictable using previously studied sentiment and
content features and that posts with titles changed by reddit users tend to be
more popular than posts with the original article title.Comment: Published at The 2nd International Workshop on News and Public
Opinion at ICWSM 201
Uber Effort: The Production of Worker Consent in Online Ride Sharing Platforms
The rise of the online gig economy alters ways of working. Mediated by algorithmically programmed mobile apps, platforms such as Uber and Lyft allow workers to work by driving and completing rides at any time or in any place that the drivers choose. This hybrid form of labor in an online gig economy which combines independent contract work with computer-mediated work differs from traditional manufacturing jobs in both its production activity and production relations. Through nine interviews with Lyft/Uber drivers, I found that workers’ consent, which was first articulated by Michael Burawoy in the context of the manufacturing economy, is still present in the work of the online gig economy in post-industrial capitalism. Workers willingly engage in the on-demand work not only to earn money but also to play a learning game motivated by the ambiguity of the management system, in which process they earn a sense of self-satisfaction and an illusion of autonomous control. This research points to the important role of technology in shaping contemporary labor process and suggests the potential mechanism which produces workers’ consent in technology-driven workplaces
Extroverts Tweet Differently from Introverts in Weibo
Being dominant factors driving the human actions, personalities can be
excellent indicators in predicting the offline and online behavior of different
individuals. However, because of the great expense and inevitable subjectivity
in questionnaires and surveys, it is challenging for conventional studies to
explore the connection between personality and behavior and gain insights in
the context of large amount individuals. Considering the more and more
important role of the online social media in daily communications, we argue
that the footprint of massive individuals, like tweets in Weibo, can be the
inspiring proxy to infer the personality and further understand its functions
in shaping the online human behavior. In this study, a map from self-reports of
personalities to online profiles of 293 active users in Weibo is established to
train a competent machine learning model, which then successfully identifies
over 7,000 users as extroverts or introverts. Systematical comparisons from
perspectives of tempo-spatial patterns, online activities, emotion expressions
and attitudes to virtual honor surprisingly disclose that the extrovert indeed
behaves differently from the introvert in Weibo. Our findings provide solid
evidence to justify the methodology of employing machine learning to
objectively study personalities of massive individuals and shed lights on
applications of probing personalities and corresponding behaviors solely
through online profiles.Comment: Datasets of this study can be freely downloaded through:
https://doi.org/10.6084/m9.figshare.4765150.v
Issue Framing in Online Discussion Fora
In online discussion fora, speakers often make arguments for or against
something, say birth control, by highlighting certain aspects of the topic. In
social science, this is referred to as issue framing. In this paper, we
introduce a new issue frame annotated corpus of online discussions. We explore
to what extent models trained to detect issue frames in newswire and social
media can be transferred to the domain of discussion fora, using a combination
of multi-task and adversarial training, assuming only unlabeled training data
in the target domain.Comment: To appear in NAACL-HLT 201
What is news? News values revisited (again)
The deceptively simple question “What is news?” remains pertinent even as we ponder the future of journalism in the digital age. This article examines news values within mainstream journalism and considers the extent to which news values may be changing since earlier landmark studies were undertaken. Its starting point is Harcup and O’Neill’s widely-cited 2001 updating of Galtung and Ruge’s influential 1965 taxonomy of news values. Just as that study put Galtung and Ruge’s criteria to the test with an empirical content analysis of published news, this new study explores the extent to which Harcup and O’Neill’s revised list of news values remain relevant given the challenges (and opportunities) faced by journalism today, including the emergence of social media. A review of recent literature contextualises the findings of a fresh content analysis of news values within a range of UK media 15 years on from the last study. The article concludes by suggesting a revised and updated set of contemporary news values, whilst acknowledging that no taxonomy can ever explain everything
Online reverse discourses? Claiming a space for trans voices
In recent years, online media have offered to trans people helpful resources to create new political, cultural and personal representations of their biographies. However, the role of these media in the construction of their social and personal identities has seldom been addressed. Drawing on the theoretical standpoint of positioning theory and diatextual discourse analysis, this paper discusses the results of a research project about weblogs created by Italian trans women. In particular, the aim of this study was to describe the ways online resources are used to express different definitions and interpretation of transgenderism, transsexuality and gender transitioning. We identified four main positioning strategies: \u201cTransgender\u201d, \u201cTranssexual before being a woman\u201d, \u201cA woman who was born male\u201d and \u201cJust a normal woman\u201d. We conclude with the political implications of the pluralization of narratives about gender non-conformity. Specifically, we will highlight how aspects of neoliberal discourses have been appropriated and rearticulated in the construction of gendered subjectivities
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