196 research outputs found
How open are journalists on Twitter? Trends towards the end-user journalism
The many activities of journalists on Twitter should be analyzed. Are they doing a different kind of journalism? With a content analysis of 1125 tweets, this study reveals trends of some Spanish journalists using Twitter. A traditional role like gatekeeping can be highly amplified in terms of transparency and accountability with actions as retweeting or linking. The landscape offered by this platform is framed with the "ambient journalism", which will help to understand the proposal of this study: the end-user journalism. The findings will show the level of opening with the audience in aspects about replies, requests and linking
4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community
We present two studies of online ephemerality and anonymity based on the popular discussion board /b/ at 4chan.org: a website with over 7 million users that plays an influential role in Internet culture. Although researchers and practitioners often assume that user identity and data permanence are central tools in the design of online communities, we explore how /b/ succeeds despite being almost entirely anonymous and extremely ephemeral. We begin by describing /b/ and performing a content analysis that suggests the community is dominated by playful exchanges of images and links. Our first study uses a large dataset of more than five million posts to quantify ephemerality in /b/. We find that most threads spend just five seconds on the first page and less than five minutes on the site before expiring. Our second study is an analysis of identity signals on 4chan, finding that over 90% of posts are made by fully anonymous users, with other identity signals adopted and discarded at will. We describe alternative mechanisms that /b/ participants use to establish status and frame their interaction
Identifying Purpose Behind Electoral Tweets
Tweets pertaining to a single event, such as a national election, can number
in the hundreds of millions. Automatically analyzing them is beneficial in many
downstream natural language applications such as question answering and
summarization. In this paper, we propose a new task: identifying the purpose
behind electoral tweets--why do people post election-oriented tweets? We show
that identifying purpose is correlated with the related phenomenon of sentiment
and emotion detection, but yet significantly different. Detecting purpose has a
number of applications including detecting the mood of the electorate,
estimating the popularity of policies, identifying key issues of contention,
and predicting the course of events. We create a large dataset of electoral
tweets and annotate a few thousand tweets for purpose. We develop a system that
automatically classifies electoral tweets as per their purpose, obtaining an
accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class
task (both accuracies well above the most-frequent-class baseline). Finally, we
show that resources developed for emotion detection are also helpful for
detecting purpose
Content consumption cartography of the Paris urban region using cellular probe data
A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at base station and back-hauling network level. In order to answer these questions, in this paper we document the content consumption in Orange cellular network for Paris metropolitan area.
From spatial and application-level extensive analysis of real data, we numerically and statistically quantify the geographical distribution of content consumption with per-service classifications. We provide experimental statistical distributions usable for further research in the area
Studying and Modeling the Connection between People's Preferences and Content Sharing
People regularly share items using online social media. However, people's
decisions around sharing---who shares what to whom and why---are not well
understood. We present a user study involving 87 pairs of Facebook users to
understand how people make their sharing decisions. We find that even when
sharing to a specific individual, people's own preference for an item
(individuation) dominates over the recipient's preferences (altruism). People's
open-ended responses about how they share, however, indicate that they do try
to personalize shares based on the recipient. To explain these contrasting
results, we propose a novel process model of sharing that takes into account
people's preferences and the salience of an item. We also present encouraging
results for a sharing prediction model that incorporates both the senders' and
the recipients' preferences. These results suggest improvements to both
algorithms that support sharing in social media and to information diffusion
models.Comment: CSCW 201
Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram
Instagram is a relatively new form of communication where users can instantly
share their current status by taking pictures and tweaking them using filters.
It has seen a rapid growth in the number of users as well as uploads since it
was launched in October 2010. Inspite of the fact that it is the most popular
photo sharing application, it has attracted relatively less attention from the
web and social media research community. In this paper, we present a
large-scale quantitative analysis on millions of users and pictures we crawled
over 1 month from Instagram. Our analysis reveals several insights on Instagram
which were never studied before: 1) its social network properties are quite
different from other popular social media like Twitter and Flickr, 2) people
typically post once a week, and 3) people like to share their locations with
friends. To the best of our knowledge, this is the first in-depth analysis of
user activities, demographics, social network structure and user-generated
content on Instagram.Comment: 5 page
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